Abstract:
Understanding the determinants of credit growth is an important issue, as credit is considered a key transmitter of financial shocks into real activity and it is at the heart of the lending channel of monetary policy. These issues have received renewed attention after the recent Great Recession following the collapse of the subprime housing market in the US.
This paper presents empirical evidence on the effect of banks' financial position (capital, profits and liquidity) on credit growth using a sample of 29 OECD countries. The empirical results show that among capital, profits and liquidity at the end of the previous year, capital is the most important predictor of credit growth in the current year. The relationship between capital and credit growth is non-linear. Point estimates from the preferred econometric specification imply that at the sample mean a one standard deviation increase (decrease) in capital is associated with an increase (decrease) of 0.8 (0.3) percentage points in credit growth upon impact and 1.6 (0.6) percentage points in the long-run. Capital is followed in importance by profits. Liquidity only seems to affect aggregate credit growth significantly in countries where smaller banks are important. These results are robust to the definition used to measure banks' financial positions and economic conditions, and are robust to considering the organization of the bank sector in each country. The failure of the exogeneity assumption of explanatory variables is addressed using the system GMM estimator from the dynamic panel literature. The use of this estimator for a "square" panel, instead of a "short" panel as originally devised, presents technical challenges that are discussed in the paper.
The paper is related to the literature on the determinants of banks' credit growth. This topic received considerable attention after the US recession of the early 90s, which coincided with a decline in banks' credit. Sharpe (1995) provides a very comprehensive survey of this work and discusses the extent to which the slowdown in credit growth was a result of weaknesses in banks' balance sheets, increased capital requirement or more stringent regulatory practices. The author concludes that the evidence shows a robust link between credit growth and both loan performance and bank profitability, although the causality of this relationship is not clear. The studies surveyed by Sharpe mostly analyze cross-sections of banks. In contrast, the results presented here use a panel of countries, adding to this literature in two dimensions. First, it investigates the generality of previous findings analyzing a single country. Second, the use of dynamic panel estimation techniques provides a nice alternative for the identification problem in this literature.
The use of a panel of countries to study bank-related questions is not new, but this is the first work to analyze the effect of banks' financial position on credit growth using this type of dataset. Ferreira (2009) used a panel of 26 EU countries, with quarterly observations between 1991 and 2006, to study the evolution of lending as a fraction of GDP and the lending channel of monetary policy. On the other hand, Levintal (2013) used a panel 28 OECD countries, with yearly observations for 1980-2003, to analyze the real effects of banking shocks. Levintal uses the same data source for bank information as this paper and identifies three types of bank shocks: profitability, capital, and reserves. He finds that profits, measured by ROA, is the bank shock with the most significant real effect. In contrast, the present paper ascribes the biggest explanatory power predicting credit growth to banks' equity capital. Thus, to the extent that the real effect of bank shocks operates through credit the result of the present study is at odds with the evidence presented by Levintal (2013). Furthermore, both studies cited above use "square" panels and so are subject to the methodological issues discussed in here.
The paper is organized as follows. Section 2 considers the specification of the economic model with a discussion about the variables that should be included in the model. Section 3 presents the data used in the econometric analysis. Section 4 discusses the econometric specification of the model with a discussion of how the system GMM estimator is used to address the dynamic panel bias and the failure of the exogeneity assumption of the variables included in the model. It presents the main results of the paper and analyzes in detail the estimated effects of banks' financial position on credit growth. Section 5 presents robustness checks to the main results, and section 6 concludes.
This section reviews the determinants of credit growth to inform the selection of the variables to be included in the model. The focus of the paper is on the effect of banks' financial position, which will be measured both from balance sheet and income statements. In particular, the effect of: (i) profits; (ii) equity capital; and (iii) liquidity, will be estimated. Additional variables are included to control for the time series structure of loan growth, economic conditions and the organization of the bank sector. The definition and rationale for all these variables is discussed below.
Time series structure: The dependent variable is the growth rate of outstanding loans, defined as the log change in outstanding loans, $$ {\Delta\ell}_t = \log L_t - \log L_{t-1}$$. Using loan growth is standard in the literature and has the advantages over using loans in level of being stationary. It is expected that loan growth depends on past values, as outstanding loans do not fully adjust in a year, which is the frequency of the dataset. Thus, $$ {\Delta \ell }_{it}$$ will depend on its own lags.
In order to specify the other variables that will be included in the model, it is helpful to start with the following simplified version of a bank balance sheet:
Assets | Liabilities |
---|---|
$$ L_t + L^{\text{IB}}_t + \text{Sec}_t + M_t$$ | $$ D_t + D^{\text{IB}}_t + E_t$$ |
$$\displaystyle {\Delta\ell}_t = \log\Biggl( \frac{ L_t }{ L_{t-1} } \Biggr) = \log\Biggl( \frac{ \delta_t A_t }{ \delta_{t-1} A_{t-1} } \Biggr) = \Delta \log \delta_t + \Delta a_t$$ | (1) |
Banks financial position: Profits are one source of new equity. Let Yt be banks (after tax) profits in year t and assume that these profits are used to increase the banks equity capital keeping the same leverage. Let $$ \lambda_t$$ be banks' leverage at the end of year t, equal to the ratio of assets to equity, $$ \frac{A_t}{E_t}$$. Thus, the increase in assets from these profits, $$ \Delta A^*$$, is given by,2
Economic conditions: will affect the costs of deposits, expected returns on different investments and the demand for credit. The cost of deposits could be proxied as the ratio of total interest expenses to total deposits.4Alternatively, the costs of deposits could be measured directly as the interest rate on deposits.
Expected returns on loans versus other type of assets will affect the portfolio decision. Bernanke and Blinder (1988) stress the dependence of this margin on interest rates, both on loans and on alternative investments (government bonds in the model). Another alternative is to invest in securities, which expected returns could be proxied by the return on domestic security markets. The expected return on loans depends on the interest rate and on the probability of borrower's default, the latter could be controlled for by the ratio of loans provisions to outstanding loans. This is the mechanism emphasized by the literature on the credit risk channel. Finally, Tobin (1982) highlights the dependence of the portfolio choice on the cost of banks' deposits, which were discussed above.
The business cycle will affect both the demand for credit and lending standards. Credit demand will be given by private and government consumption and investments decisions which are partially financed with credit. Finally, the bank literature also shows that banks change their lending standards over the business cycle.5
Organization of the bank sector: The banking literature identify other variables that may affect the growth of loans at the country level. First, the literature on bank efficiency identify a potential role for bank size and diversification. At the aggregate level bank size could be proxy by the ratio of banks' assets to GDP. On the other hand, we can use the fact that larger banks take more risk to use the ratio of large bank assets to total banks assets as a measure of both diversification and economies of scale in lending activities.This ratio of large banks assets to total banks assets may affect aggregate lending just by a composition effect as the evidence for the US have found that larger banks hold smaller fraction of loans to total assets.6
Finally, it is important to bear in mind that several variables affect loan growth through more than one channel something that needs to be considered when interpreting the results.
This section describes the data used in the econometric analysis, consisting of an unbalanced panel of countries with yearly observations. The sample of countries is determined primarily by availability of banks' information, which is obtained from the OECD Bank Statistics database. This data set reports information for bank groups in each country. The most aggregated group is all banks, which includes: commercial banks, saving banks, cooperative banks, and other miscellaneous monetary institutions. When available information for large commercial banks and foreign commercial banks is reported separately. The subsequent analysis considers information at the country level, therefore the most comprehensive bank group is chosen for each country. Table 1 presents the list of countries present in the OECD Bank Statistics dataset and the bank group selected for the analysis.7Table 1 considers only availability of information on credit growth, when information on all bank variables is considered the total number of observation drops from 726 to 705. Additional information is lost when bank variables are merged with long-term and lending rates leaving a total of 530 country-year observations.8Including domestic stock market returns further reduce the number of observations to 500 and 6 more observations are lost when real variables (GDP, consumption and investment) are included. It should be noted that Turkey and Luxembourg are left out of the analysis because of the information requirements. Turkey does not have information on long-term interest rates, whereas the series on stock market returns and lending rates do not overlap in the case of Luxembourg. As mentioned above it will be assumed that the growth rate of credit depends on its own lagged realization, which will make additional observations to be discarded in the econometric analysis. As Table 2 shows, in the benchmark regression, the number of observations is 480. The Table also lists the sample period by country of the data used in the analysis.
The OECD Bank Statistics database contains data for income statements and balance sheets of bank groups in OECD countries. All figures are in local currency at the end of the period and are transformed to real values using individual countries consumer price indices (CPI).9 Information on outstanding nominal loans for country i at the end of year t, is included in the assets breakdown of the balance sheet as item 16. Using domestic CPI loan series are deflated to obtain real outstanding loans, Lit. Loan growth is defined as the log-difference of real loans, $$ {\Delta\ell}_{it} \equiv \log L_{it} - \log L_{i,t-1}$$ expressed in percents. Table 3 presents mean loan growth by country for the sample of 480 observation used in the econometric analysis. The sample mean of credit growth is 5.8% per year. Ireland presents the largest annual growth of real credit with almost 22% for the period 1997-2005, whereas Mexico exhibit the largest decline in real credit with an average decline of 2.2% per year in 1995-2007.
Profit measures are constructed based on income statements reported in the OECD dataset. Return on equity, (ROE) is defined as the ratio of item 11, after-tax profits, to item 19, capital and reserves, expressed in percents. Capital and reserves is the closest measure of banks' capital reported. Table 3 presents averages by country of $$ {\text{ROE}}_{i,t-1}$$ for the sample used in the estimations below. The sample mean is 8.5%. New Zealand presents the highest average ROE in the sample with almost 17%, whereas Japan presents the lowest with almost -2%. Banks' equity capital, CAP is defined as the ratio of item 19, capital and reserves, to item 25, end-year balance sheet total, expressed in percents. Balance sheet total equals the sum of assets or liabilities at year-end and henceforth it will be referred to as total assets. Table 3 presents means by country of $$ {\text{CAP}}_{i,t-1}$$. Considering all country-year observations the mean is 6.1%, whereas taking individual countries it ranges from 3.1% in Belgium to 10.1% in Australia. Likewise, balance sheet liquidity, BSL is defined as item 17, securities in the asset side of the balance sheet at year-end, to total assets (item 25) and it is expressed in percents. Averages for this ratio for individual countries go from 7.1% in Australia to 33.4% in Greece. When all counties are considered the average $$ {\text{BSL}}_{i,t-1}$$ is 18.9% (Table 3).
Measures on deposits costs and loan provisions are also calculated using information from the OECD Bank Statistics dataset. DEPOSIT COSTS is defined as the ratio of item 2, interest expenses, to item 22, non-bank deposits.10Non-bank deposits corresponds to deposits held by bank customers as opposed to interbank deposits hold by banks among themselves. Table 4 presents the sample mean for DEPOSIT COSTS considering the sample of 480 observations used in the estimations below: approximately 10%. Since information on loan provisions (item 8.a) is not available for all countries and years, total provisions (item 8) is used instead in the benchmark specification. PROVISIONS is defined as the ratio of total provisions to nominal outstanding loans (item 16). The sample average of this variable is 86 basis points, as reported in Table 4. Loan provisions will be used to check the robustness of the results in section 5.
Economic conditions also include variables collected from other sources. Real effective lending rates are calculated as the difference between nominal lending rates from the IFS, line 60P..ZF... and effective CPI inflation obtained from the OECD Main Economic Indicators, Prices: Consumer Prices. Real effective long-term interest rates are calculated as the difference between nominal 10 year government bonds or similar and effective CPI inflation. Nominal long term rates are obtained from the OECD Main Economic Indicators and the IFS.11Real domestic stock market returns are calculated as the log difference of real stock market price indices and expressed in percents. Nominal price indices are obtained from the OECD Main Economic Indicators and the IFS and deflated using domestic CPI to compute real stock market price indices. Changes in real aggregate demand are calculated as the log difference of real aggregate demand and expressed in percents. Real aggregate demand is defined as the sum of real private and government consumption and investment. All these series are obtained from the OECD Main Economic Indicators .12
Organization of the bank sector is measured as the ratio of banks' total assets to GDP. GDP figures corresponds to real GDP at 2000 prices published by the OECD. Real total assets at 2000 prices were computed from nominal total assets, deflated by domestic CPI.13Table 4 reports averages by country and for all observations of all these variables. Appendix A provides additional descriptive statistics for all the variables in Tables 3 and 4.
This section discusses the econometric issues that arise when estimating the effect of banks' financial position on credit growth and specifies the benchmark econometric model for this analysis. Subsequently, it presents the main results on the effect of banks' financial position on credit growth. First, the effect of profits, capital and liquidity are estimated independently while controlling for economic conditions and the organization of the banking sector. Then, alternative measures for the three dimensions of banks' financial position are considered. Additional robustness checks are provided in section 5.
Credit growth is defined as the log-difference of real loans, $$ {\Delta\ell}_{it} \equiv \log L_{it} - \log L_{i,t-1}$$. The model to be estimated takes the form:
$$\displaystyle {\Delta\ell}_{it} = \alpha(L) {\Delta\ell}_{it} + \beta' X_{it} + \mu_t + \mu_i + v_{it} \qquad\qquad i = 1, \ldots, N \quad t = 1,\dots,T$$ | (2) |
$$\displaystyle X^{pre}_{it} = \left[ \begin{array}{l} {\text{ROE}}_{i,t-1} \qquad\qquad {\text{CAP}}_{i,t-1} \qquad\qquad {\text{CAP}}^2_{i,t-1} \qquad\qquad {\text{BSL}}_{i,t-1} \\ {\text{DEPOSIT COSTS}}_{i,t-1} \qquad\qquad {\text{PROVISIONS}}_{i,t-1} \qquad\qquad {\text{ASSETS/GDP}}_{i,t-1} \end{array} \right]$$ | |
$$\displaystyle X^{endo}_{it} = \left[ \begin{array}{l} {\text{LENDING RATE}}_{it} \qquad\qquad {\text{LONG TERM RATE}}_{it} \\ {\text{STOCK RETURNS}}_{it} \qquad\qquad {\Delta \text{AGG. DEMAND}}_{i,t} \end{array} \right]$$ |
The identification strategy relies on two assumptions. First, it is assumed that predetermined variables and the lagged value of credit growth $$ {\Delta\ell}_{i,t-1}$$ are weakly exogenous; whereas contemporaneous variables are endogenous. This is,
$$\displaystyle \mathbb{E}\bigl( v_{it} \big\vert {\Delta\ell}_{i,t-1}, X^{pre}_{it}, {\Delta\ell}_{i,t-2}, X_{i,t-1}, \ldots, {\Delta\ell}_{i1}, X_{i1}\bigr) = 0$$ | (Assumption 1) |
Arellano-Bond (1991) propose a difference GMM estimator for dynamic panels. The idea is to take first differences of model (2) and then instrument for endogenous variables in the transformed model. Differencing the model gives,
$$\displaystyle \Delta^2 \ell_{it} = \alpha \Delta^2 \ell_{i,t-1} + \beta' \Delta X_{it} + \Delta \mu_t + \Delta v_{it}$$ | (3) |
There are two ways around the problem of too many instruments which will be considered below. The first one is to restrict the number of lags to be used as instruments. The second consist of collapsing the set of instruments to get one instrument per instrumental variable. The latter combines elements of the IV and GMM style, as it builds a single instrumental variable using $$ x_{i,t-1}$$ but still replaces missing with zeros. This gives a single instrument using $$ x_{i,t-1}$$ as instrument:
As discussed above, when xit is a predetermined variable lags one and up are suitable instruments for the differenced model (3). In contrast, when xit is an endogenous variable suitable instruments are the second and deeper lags of the variable. In fact, in this case the term $$ x_{i,t-1}$$ is correlated with $$ v_{i,t-1}$$ and therefore $$ x_{i,t-1}$$ will not be a suitable instrument for $$ \Delta x_{it}$$, but $$ x_{i,t-2}$$ is still independent of $$ v_{i,t-1}$$ and could be used as an instrument. Deeper lags of $$ x_{i,t-2}$$ and $$ \Delta x_{i,t-2}$$ and deeper lags of it will also be valid instruments.
Estimations using difference GMM are reported in Table 5 columns 3 to 6. Column 3 presents estimates that use 2 lags of explanatory variables as instruments in GMM style. The estimated coefficient is in the lower range of the interval [0.19, 0.32], but the number of instruments is almost equal to the number of observations. With 6 lags in GMM style, the number of instruments is greater than the number of observation, but the algorithm limits the number of instruments by the number of observations. Estimated coefficients are very similar to the FE estimates, as was to be expected by the use of as many instruments as observations. Collapsing the set of instruments, using 2 and 6 lags of each explanatory variable yields instrument sets with 52 and 100 elements, respectively (columns 5 and 6). This yields a more reasonable number of instruments, but the coefficient on lagged credit growth falls outside the desired interval and there are other problems that suggests the model is poorly specified. Indeed, the Sargan tests rejects the joint validity of the moment restrictions.15Moreover, the Arellano-Bond test for the independence of the idiosyncratic disturbances-Assumption 2-is rejected, suggesting serial correlation of the innovations of model (2). This assumption was key in the construction of the appropriate instrument sets. Arellano and Bond (1991) shows how to construct a test statistics under the null of serial independence, that converges to a normal distribution when the number of panels, N is large. The procedure consist of testing for second order serial correlation in the differenced residuals to test for first order serial correlation on the original disturbances. The p-values for this tests are reported in all the GMM regressions for first and second order serial correlation in the original disturbances, AR(2) and AR(3) for the differenced residuals, respectively. For example, using 6 collapsed lags as instruments this test indicate first order serial correlation at the 5% significance level, but cannot reject that there is no serial correlation of second order for the original disturbances. There are two ways to take this time series pattern into account. One is to construct the instrument set starting with lag t-2 and t-3, respectively for predetermined and endogenous variables. However, following this approach seems to weaken the instruments significantly.16Another is to enrich the time series specification of the variables in the model, so the innovations become serially uncorrelated, as we do below including an additional lag for credit growth to the model.
Table 6 reports the estimates using OLS, FE and difference GMM of model (2) including 2 lags of credit growth as explanatory variables. Now the Arellano-Bond tests cannot reject the null of serially uncorrelated innovations. With 2 lags of the dependent variable we expect the sum of the coefficients in the $$ \alpha(L)$$ polynomial to be upward and downward biased, respectively in the case of OLS and FE. Therefore, all models report the sum of the estimated coefficients on $$ {\Delta\ell}_{i,t-1}$$ and $$ {\Delta\ell}_{i,t-2}$$ to facilitate comparison. As it was the case before OLS and FE estimates provide a useful benchmark to asses the performance of theoretically superior estimators, [0.36,0.48] in this case. Now the model seems better specified. The sum of these coefficients is in the desired range and the diagnostics tests do not reject the serial independence of the innovations or the joint validity of the moment restrictions. The coefficients of the FE estimator are similar as before and the joint test on the coefficients of CAP is rejected at the 5% confidence level. Figure 1 panel (a) plots the estimated effect of $$ {\text{CAP}}_{i,t-1}$$ on credit growth based on the FE estimates. Point estimates of the FE model imply that an increase of one standard deviation in the ratio of bank capital to assets at the sample mean of 6.1% will increase credit growth by 72 basis points upon impact and 1.13 percentage point in the long-run.
Despite the fact, that the difference GMM estimates pass the validations of the diagnostics checks indicated above, there are some signs of problems, as most coefficients are not significant. The problem with the difference GMM estimator in this case is originated by the use of persistent individual series. Bond (2002) recommends investigating the time series properties of all the series being used in the estimation and suggests using system GMM when they are found to be highly persistent. Appendix B analyze the time series properties of the individual series. BSL and ASSETS/GDP are found to be highly persistent with estimated coefficients for the autoregressive term between 0.81 and 0.93, and 0.98 and 1.04, respectively.
The system GMM estimator uses both the differenced and level equations, "doubling" the number of observations used in the estimation. The way right-hand side variables are instrumented for in the difference equations is the same as in difference GMM. For the level equations, right-hand side variables are instrumented by their differences, which are assumed independent of the individual effects. For example, for $$ {\Delta\ell}_{i,t-1}$$ a valid instrument will be $$ \Delta^2 \ell_{i,t-1}$$, as it is assumed not correlated with the fixed effect and correlated with $$ {\Delta\ell}_{i,t-1}$$. Similarly, for a variable xit which is predetermined, $$ \Delta x_{it}$$ will be a valid instrument as it is assumed not correlated with the fixed effect and correlated with xit. Deeper lags of them will also be valid instruments. For endogenous variables $$ \Delta x_{i,t-1}$$ and deeper lags may be used as instruments.
Column 5, system GMM with 2 collapsed lags seems the best fit for the model. LENDING RATE and $$ {\Delta \text {AGG. DEMAND}}$$ are significant, and LONG TERM RATE and ASSETS/GDP have the desired signs. The sum of the coefficient on lagged credit growth is on the upper part of the desired range and the diagnostics tests do not reject neither joint validity of moment restriction nor the serial uncorrelation of the innovations. The estimated effects of banks' financial position yields CAP as the only significant variable. In fact the coefficient on the linear term is significant at the 10% level and the linear and quadratic terms are jointly significant at the 5% level. This nonlinear effect will depend on the initial level of the ratio of equity to assets (Figure 1 panel b). For instance, starting at the sample mean of 6.1% the effect of an increase (decrease) of one standard deviation in CAP is an increase (decrease) of 0.8 (0.3) percentage points in the growth rate of credit.17The presence of lagged credit growth in the model imply that the long-run effect will be the previous effect times $$ \frac{1}{1-\alpha(L)}$$, i.e. an associated increase (decrease) in credit growth in the long-run of 1.6 (0.6) percentage points. The coefficient on ROE displays the right sign, but it does not seem to have neither a statically or economically significant effect on credit growth. Balance sheet liquidity, BSL, have a negative sign in contrast to what was expected. Deposit costs at the end of the previous year and contemporaneous lending rates, stock returns and aggregate demand growth are all significant with the expected signs. The implied effects on credit growth of this point estimates from a one standard deviation increase are: DEPOSIT COSTS (ratio of interest expenses to deposits) -2.76 percentage points; LENDING RATE 4.72 percentage points; STOCK RETURNS 5.01 percentage points; and the growth rate of aggregate demand, $$ {\Delta \text {AGG. DEMAND}}$$ 1.01 percentage points.18
Having specified the benchmark specification it is now possible to study in more detail the effect of banks' financial position on credit growth. Three aspects will be considered. First, the individual effect of each variable that measures banks' financial positions will be considered. Then, it will be analyzed the effect of different measures of profits, liquidity and capital, respectively. Finally, the next section presents some robustness checks. Table 7 presents the results when banks' variables are included one at a time to investigate the significance of each one separately and potential non-linear effect of profits and liquidity. The first column presents the benchmark regression results to facilitate comparison. All models use two collapsed lags to construct the instrument set and the system GMM estimator. When only CAP is included in the model, estimates remain qualitatively the same. The joint significance of the linear and quadratic capital terms is affected but they are still significant at the 10% level (column 3). When only ROE is included the estimates are as before. More interesting is the estimation that considers both a linear and a quadratic profit term (column 5). Both of the ROE coefficients are significant at the 10% level, but they are not jointly significant in evidence the individual coefficients were only marginally significant.19When only balance sheet liquidity, BSL is included the coefficient turns bigger in absolute value, but it is still statistically insignificant. No significant effect is found when both a linear and a quadratic BSL term are included. This analysis reinforce the result that CAP is the only significant predictor of subsequent credit growth in this sample, with an effect that is nonlinear as was expected by the presence of capital regulations.
Table 8 reports estimations for different definitions of banks' profits. As detailed above ROE was set to zero when equity was negative. If ROE is defined as the ratio of after tax profits to equity, even when equity is negative results remain unchanged. This was expected as there is only one country-year observation with negative equity, corresponding to the US in 1983 (see Appendix A). Next, return on assets (ROA) at the end of the previous year is considered instead of ROE. Column 3 in Table 8 reports the results for this specification. The coefficient on ROA is positive, but statistically insignificant. When leverage is included as an additional control the estimated coefficient on ROA do not change significantly. But the coefficient associated with CAP do change, but the joint significance of the linear and quadratic terms is not compromised.
Table 9 presents the estimation results for different definitions of banks' liquidity. Once again, to facilitate comparison, the first column presents the benchmark regression results. The second column presents the results of replacing $$ {\text{BSL}}_{i,t-1}$$ with the interaction of this variable and SMALL, the fraction of small banks' assets to total assets.20Not all countries reports information to compute this ratio so the regression include only 18 countries and 249 observations. The coefficient on liquidity turns statistically insignificant, in line with previous studies that suggests that liquid assets are a more important funding source for smaller banks. The second order polynomial on capital remains significant and now the linear term is significant by itself at the 1% level. More surprising is the fact, that the coefficient on ROE becomes significant and the coefficient on the lending rate becomes negative. The specification tests show that the joint validity of the moment conditions is rejected, whereas the independence of the original disturbances is not. Column 3 present the benchmark regression estimated with the restricted sample of 249 observations used in the previous regression. Again the joint validity of the moment restrictions is rejected, suggesting that the number of instruments is too large relative to the sample size of 249. Column 4 considers the sum of securities and reserves21to total assets at the end of the previous year as the measure of liquidity. Comparison with the benchmark regression shows that the coefficient on liquidity turns negative, the point estimates of other coefficients do not change significantly, and the results of the test of the significance of coefficients and diagnostics test are the same. The last column presents the estimation when liquidity is measured by the ratio of (non-bank) deposits to total assets at the end of the previous year. Again the coefficient of this measure of liquidity turns negative and the rest of the coefficient are in line with the baseline regression. An exception is ASSETS/GDPwhich changes sign.
Finally, Table 10 presents the results when alternative definitions of banks' capital are included in the model. To account for the non-linearity of the estimated effect of banks' capital, this variable is interacted with different dummy variables. The first one is whether $$ {\text{CAP}}_{i,t-1}$$ is larger or equal to the 25th percentile of the distribution of CAP in country i. The second one is whether $$ {\text{CAP}}_{i,t-1}$$ is larger or equal to 4% and the third one whether is larger or equal to 6%. As could be seen from the results reported in Table 10 (columns 2-4) none of this non-linear transformations capture the nonlinear effect of capital as none of the estimated coefficients is significant. The estimated effect of the other variables is in line with the baseline specifications.
This results correspond to countries and may not be compared in a straight forward way to the results from individual banks, as studying aggregate banks balance sheets it is not possible to identify movements between individual institutions. In fact, estimates pick up the multiplier effect of financial transactions. For example, a bank grants a loan to a client, who deposits part of the funds or spend them and the recipient deposits the proceeds in a domestic bank. Then the latter bank may grant a loan with the cycle continuing.
This section presents robustness tests to the benchmark regression reported above. First, real deposit rates are included instead of the ratio of interest expenses to deposits to control for the cost of deposits. Second, the ratio of large banks' assets to total assets, LARGE, is included to control for the structure of the bank sector. Third, LOAN PROVISIONS, defined as the ratio of provisions on loans to total loans is used instead of PROVISIONS to control for the riskiness of borrowers. Finally, alternative measures to control for real activity are considered. Table 11 presents the first set of robustness checks. Table 12 reports further robustness checks for the way real economic activity is controlled for in the model. Once again the table starts with the benchmark estimation results (column 1). Column 2 considers changes in real GDP, $$ \Delta$$GDP$$ _{it}$$ instead of changes in aggregate demand.
In sum, these robustness checks lend support to the main finding of the paper that banks' equity capital is a significant determinant of subsequent credit growth and that neither profits or liquidity display a significant role at the country level for OECD countries.
This paper presented estimates of the effect of banks' financial position on credit growth for a sample of 29 OECD countries. The identification relied on the assumption that country-year innovations to the growth rate of loans are independent of predetermined variables and past values of endogenous variables, and that these innovations are not serially correlated. The paper discussed how to adapt GMM estimators, designed for "short" panels, to the present context where the data is organized in a "square" panel. The main issue is on building suitable instrument sets without using too many instruments that will render the instruments invalid and generate other estimation problems. It was argued that the system GMM estimator was to be preferred due to the presence of highly persistent series and an instrument set using two lags of independent variables collapsed to economize on the number of instruments was chosen. The empirical results shows that among capital, profits and liquidity at the end of the previous year, capital is the most important predictor of credit growth in the current year. The relationship between capital and credit growth is non-linear. Point estimates from the preferred econometric specification imply that at the sample mean a one standard deviation increase (decrease) is associated with an increase (decrease) of 0.8 (0.3) percentage points in credit growth upon impact and 1.6 (0.6) percentage points in the long-run. These results were found robust to the definition of the variables included in the model as well as changes in the set of controls used in the estimation.
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The model to be estimated is given in equation (2). Recall $$ {\Delta\ell}_{it} = \alpha {\Delta\ell}_{i,t-1} + \beta' X_{i,t} + \mu_t + \mu_i + v_{it}$$,. Here I present descriptive statistics for the variables Sample according to availability of information model (2).
Source: Own elaboration based on OECD Bank Statistics and OECD Main Economic Indicators.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 5.711 | -20.933 | 32.421 | 11.584 |
Austria | 5.990 | 5.329 | 6.650 | 0.934 |
Belgium | 3.976 | -5.812 | 14.950 | 4.482 |
Canada | 3.628 | -2.244 | 12.314 | 4.010 |
Chile | 7.093 | -2.092 | 14.543 | 5.215 |
Czech Republic | 2.011 | -8.000 | 13.552 | 8.701 |
Denmark | 5.244 | -11.747 | 20.829 | 8.182 |
Finland | 2.369 | -16.775 | 33.663 | 11.687 |
France | 1.255 | -9.579 | 6.575 | 4.168 |
Germany | 4.572 | -3.734 | 9.845 | 2.984 |
Greece | 13.733 | -2.059 | 43.961 | 12.271 |
Hungary | 13.757 | 7.276 | 24.391 | 5.195 |
Iceland | 13.134 | 0.091 | 36.726 | 11.987 |
Ireland | 21.998 | 3.607 | 48.989 | 15.727 |
Italy | 4.826 | -3.094 | 12.803 | 4.605 |
Japan | -1.056 | -10.214 | 3.597 | 3.497 |
Korea | 12.331 | -23.941 | 42.042 | 13.408 |
Mexico | -2.156 | -15.492 | 14.075 | 9.874 |
Netherlands | 6.768 | -10.407 | 25.206 | 9.322 |
New Zealand | 8.270 | 4.884 | 13.115 | 2.422 |
Norway | 7.509 | -8.376 | 22.119 | 7.550 |
Poland | 6.805 | 0.690 | 20.959 | 7.604 |
Portugal | 10.508 | -5.987 | 23.041 | 9.589 |
Slovak Republic | 4.234 | -27.145 | 22.349 | 17.626 |
Spain | 5.194 | -9.749 | 11.833 | 5.292 |
Sweden | 4.110 | -23.421 | 21.980 | 10.602 |
Switzerland | 3.289 | -11.442 | 14.617 | 5.513 |
United Kingdom | 9.240 | -3.652 | 42.383 | 11.132 |
United States | 2.575 | -13.480 | 14.648 | 5.817 |
All | 5.812 | -27.145 | 48.989 | 9.092 |
Source: Own elaboration based on OECD Bank Statistics.
1 ROE defined as zero when equity is negative.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 9.152 | -0.913 | 35.149 | 8.414 |
Austria | 8.003 | 7.603 | 8.402 | 0.565 |
Belgium | 9.267 | 3.555 | 21.667 | 4.073 |
Canada | 12.720 | 4.963 | 16.834 | 2.668 |
Chile | 13.011 | 8.844 | 15.691 | 1.974 |
Czech Republic | 9.744 | 0.755 | 14.147 | 5.212 |
Denmark | 6.774 | -21.384 | 25.622 | 9.676 |
Finland | 0.014 | -49.504 | 24.228 | 19.523 |
France | 6.150 | -1.291 | 10.283 | 3.808 |
Germany | 6.114 | 3.696 | 8.894 | 1.084 |
Greece | 14.109 | 7.045 | 21.901 | 3.939 |
Hungary | 15.414 | 10.529 | 19.884 | 3.620 |
Iceland | 8.737 | -0.883 | 14.852 | 4.750 |
Ireland | 13.356 | 10.452 | 15.937 | 1.526 |
Italy | 7.307 | 1.208 | 12.842 | 3.306 |
Japan | -1.992 | -22.388 | 15.085 | 12.182 |
Korea | -0.023 | -79.028 | 18.174 | 24.044 |
Mexico | 6.920 | -5.008 | 20.079 | 7.252 |
Netherlands | 10.864 | -11.195 | 18.023 | 6.267 |
New Zealand | 16.752 | 6.839 | 23.283 | 4.244 |
Norway | 5.033 | -113.774 | 17.897 | 25.325 |
Poland | 10.240 | 4.742 | 16.572 | 4.742 |
Portugal | 7.084 | 5.770 | 9.528 | 1.227 |
Slovak Republic | 12.174 | -29.391 | 26.495 | 18.135 |
Spain | 8.600 | 1.356 | 11.697 | 2.072 |
Sweden | 9.999 | 2.052 | 39.752 | 8.498 |
Switzerland | 8.415 | 0.308 | 16.402 | 3.671 |
United Kingdom | 13.102 | 1.117 | 21.013 | 5.898 |
United States | 9.698 | 0.000 | 14.043 | 4.197 |
All | 8.536 | -113.774 | 39.752 | 10.692 |
Source: Own elaboration based on OECD Bank Statistics.
Country | mean | min | p25 | max | st. dev. |
---|---|---|---|---|---|
Australia | 10.096 | 7.063 | 9.918 | 12.344 | 1.196 |
Austria | 4.621 | 4.504 | 4.504 | 4.737 | 0.164 |
Belgium | 3.071 | 2.384 | 2.545 | 3.957 | 0.514 |
Canada | 5.279 | 4.185 | 5.099 | 5.877 | 0.411 |
Chile | 8.517 | 7.276 | 8.316 | 9.199 | 0.459 |
Czech Republic | 8.483 | 6.013 | 8.202 | 10.643 | 1.695 |
Denmark | 7.628 | 5.512 | 6.542 | 9.930 | 1.352 |
Finland | 6.820 | 5.044 | 6.126 | 10.823 | 1.622 |
France | 4.260 | 3.124 | 3.996 | 5.064 | 0.539 |
Germany | 3.793 | 3.271 | 3.557 | 4.242 | 0.310 |
Greece | 5.732 | 2.443 | 4.552 | 9.886 | 2.343 |
Hungary | 9.326 | 8.999 | 9.088 | 9.785 | 0.262 |
Iceland | 7.321 | 6.410 | 6.734 | 7.980 | 0.600 |
Ireland | 5.911 | 4.985 | 5.690 | 6.681 | 0.582 |
Italy | 6.435 | 3.887 | 6.116 | 8.035 | 0.965 |
Japan | 3.951 | 2.837 | 3.338 | 5.260 | 0.665 |
Korea | 5.775 | 3.583 | 4.098 | 8.867 | 1.874 |
Mexico | 7.349 | 5.298 | 6.389 | 9.713 | 1.256 |
Netherlands | 3.878 | 2.668 | 3.605 | 4.601 | 0.524 |
New Zealand | 5.700 | 3.676 | 4.805 | 7.686 | 1.218 |
Norway | 5.457 | 2.904 | 4.544 | 7.295 | 1.245 |
Poland | 9.492 | 8.348 | 9.151 | 10.204 | 0.694 |
Portugal | 9.863 | 8.227 | 9.012 | 11.584 | 1.029 |
Slovak Republic | 7.325 | 3.733 | 4.808 | 13.049 | 2.970 |
Spain | 7.862 | 6.564 | 7.222 | 9.472 | 0.704 |
Sweden | 5.762 | 4.268 | 5.342 | 7.163 | 0.796 |
Switzerland | 5.904 | 4.531 | 5.622 | 6.807 | 0.661 |
United Kingdom | 4.560 | 3.256 | 4.051 | 5.995 | 0.715 |
United States | 6.730 | -11.666 | 5.543 | 10.345 | 4.058 |
All | 6.087 | -11.666 | 4.481 | 13.049 | 2.234 |
Source: Own elaboration based on OECD Bank Statistics.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 7.096 | 3.457 | 10.048 | 1.823 |
Austria | 16.025 | 15.956 | 16.094 | 0.098 |
Belgium | 29.528 | 23.251 | 34.169 | 2.485 |
Canada | 17.304 | 10.224 | 26.325 | 5.343 |
Chile | 16.060 | 10.811 | 18.998 | 2.686 |
Czech Republic | 23.766 | 20.422 | 26.887 | 2.389 |
Denmark | 24.411 | 18.335 | 29.137 | 3.627 |
Finland | 16.673 | 8.471 | 23.459 | 4.572 |
France | 16.710 | 7.789 | 22.866 | 4.940 |
Germany | 17.598 | 12.352 | 23.981 | 3.638 |
Greece | 33.412 | 28.895 | 36.661 | 2.411 |
Hungary | 16.430 | 14.107 | 18.731 | 1.692 |
Iceland | 13.562 | 9.330 | 19.061 | 2.940 |
Ireland | 23.902 | 19.189 | 29.521 | 3.961 |
Italy | 14.829 | 9.132 | 22.755 | 4.208 |
Japan | 19.669 | 14.343 | 27.225 | 4.925 |
Korea | 17.291 | 12.491 | 24.983 | 3.265 |
Mexico | 26.933 | 15.634 | 33.526 | 6.694 |
Netherlands | 21.291 | 11.601 | 30.992 | 5.450 |
New Zealand | 11.114 | 5.436 | 20.354 | 4.288 |
Norway | 15.747 | 8.100 | 34.108 | 7.802 |
Poland | 22.104 | 20.396 | 23.218 | 1.033 |
Portugal | 21.373 | 15.000 | 27.348 | 3.973 |
Slovak Republic | 25.821 | 14.275 | 36.199 | 6.663 |
Spain | 18.756 | 12.621 | 24.787 | 3.224 |
Sweden | 21.514 | 11.579 | 29.731 | 5.432 |
Switzerland | 14.995 | 9.636 | 23.524 | 4.822 |
United Kingdom | 14.950 | 6.944 | 20.924 | 5.029 |
United States | 19.119 | 13.943 | 23.386 | 3.373 |
All | 18.924 | 3.457 | 36.661 | 6.833 |
Source: Own elaboration based on OECD Bank Statistics.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 8.758 | 5.860 | 16.371 | 3.434 |
Austria | 8.607 | 8.531 | 8.683 | 0.107 |
Belgium | 20.617 | 9.881 | 30.662 | 5.659 |
Canada | 6.998 | 2.823 | 11.909 | 2.705 |
Chile | 11.880 | 5.509 | 20.631 | 5.383 |
Czech Republic | 4.129 | 2.597 | 6.514 | 1.567 |
Denmark | 10.115 | 5.795 | 14.292 | 2.913 |
Finland | 9.517 | 3.611 | 16.466 | 4.041 |
France | 21.712 | 11.786 | 31.949 | 6.392 |
Germany | 9.316 | 7.219 | 12.288 | 1.347 |
Greece | 10.019 | 4.212 | 14.074 | 3.142 |
Hungary | 8.298 | 6.783 | 10.822 | 1.305 |
Iceland | 11.769 | 6.526 | 22.087 | 5.259 |
Ireland | 9.511 | 7.908 | 11.460 | 1.239 |
Italy | 11.190 | 5.527 | 17.236 | 3.199 |
Japan | 2.907 | 0.339 | 8.207 | 2.615 |
Korea | 6.984 | 3.954 | 11.871 | 2.139 |
Mexico | 20.288 | 9.442 | 47.965 | 11.394 |
Netherlands | 9.225 | 5.599 | 12.257 | 1.865 |
New Zealand | 6.863 | 4.175 | 11.662 | 2.255 |
Norway | 9.879 | 4.655 | 18.819 | 3.845 |
Poland | 7.280 | 4.034 | 12.997 | 4.041 |
Portugal | 11.265 | 8.505 | 14.163 | 1.675 |
Slovak Republic | 5.707 | 2.947 | 12.361 | 3.216 |
Spain | 9.180 | 3.973 | 12.763 | 2.462 |
Sweden | 12.141 | 4.219 | 21.127 | 4.475 |
Switzerland | 7.339 | 3.709 | 11.212 | 2.169 |
United Kingdom | 6.881 | 4.053 | 11.084 | 1.883 |
United States | 6.870 | 2.003 | 12.947 | 2.954 |
All | 9.991 | 0.339 | 47.965 | 5.684 |
Source: Own elaboration based on OECD Bank Statistics and OECD Main Economic Indicators.
1 Real effective lending rates calculated as nominal rates minus effective inflation in the year.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 7.060 | 4.282 | 10.869 | 1.931 |
Austria | 5.284 | 5.069 | 5.499 | 0.304 |
Belgium | 6.924 | 3.922 | 10.572 | 1.727 |
Canada | 4.889 | 1.929 | 9.283 | 2.070 |
Chile | 7.133 | 3.369 | 15.059 | 3.492 |
Czech Republic | 4.066 | 2.449 | 5.840 | 1.341 |
Denmark | 7.663 | 4.627 | 11.458 | 1.901 |
Finland | 5.106 | 2.565 | 9.217 | 1.986 |
France | 5.796 | 4.465 | 7.589 | 1.055 |
Germany | 8.101 | 6.757 | 9.192 | 0.658 |
Greece | 9.644 | -2.515 | 16.568 | 5.625 |
Hungary | 4.165 | 1.127 | 6.081 | 1.515 |
Iceland | 10.466 | 9.014 | 11.664 | 0.847 |
Ireland | 0.997 | -0.806 | 5.153 | 2.142 |
Italy | 6.517 | 3.157 | 11.253 | 2.614 |
Japan | 2.450 | 0.531 | 4.437 | 1.088 |
Korea | 4.358 | 2.234 | 8.583 | 1.970 |
Mexico | 5.880 | 1.514 | 24.433 | 6.183 |
Netherlands | 2.511 | 0.671 | 5.490 | 1.660 |
New Zealand | 7.904 | 6.161 | 9.578 | 1.122 |
Norway | 6.335 | 0.543 | 11.922 | 2.821 |
Poland | 7.118 | 4.178 | 12.952 | 3.652 |
Portugal | 8.424 | 2.854 | 14.529 | 3.160 |
Slovak Republic | 3.463 | -0.095 | 7.123 | 2.199 |
Spain | 4.886 | 0.560 | 11.114 | 3.161 |
Sweden | 6.546 | 2.861 | 12.826 | 2.390 |
Switzerland | 2.759 | -0.930 | 4.710 | 1.239 |
United Kingdom | 4.345 | 1.012 | 8.679 | 1.896 |
United States | 5.194 | 1.663 | 8.730 | 1.864 |
All | 5.725 | -2.515 | 24.433 | 3.101 |
Source: Own elaboration based on OECD Bank Statistics.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 0.704 | 0.141 | 2.052 | 0.680 |
Austria | 0.733 | 0.722 | 0.744 | 0.016 |
Belgium | 0.638 | -0.079 | 1.307 | 0.368 |
Canada | 0.549 | 0.158 | 1.331 | 0.288 |
Chile | 1.111 | 0.519 | 2.045 | 0.438 |
Czech Republic | -1.628 | -2.923 | 0.574 | 1.408 |
Denmark | 1.959 | 0.623 | 3.611 | 1.017 |
Finland | 0.172 | -0.105 | 0.813 | 0.278 |
France | 0.870 | 0.367 | 1.780 | 0.466 |
Germany | 0.618 | 0.200 | 0.946 | 0.195 |
Greece | 1.186 | 0.651 | 1.866 | 0.441 |
Hungary | 0.411 | -0.084 | 0.662 | 0.227 |
Iceland | 1.490 | 0.947 | 3.166 | 0.731 |
Ireland | 0.196 | 0.076 | 0.298 | 0.062 |
Italy | 1.197 | 0.260 | 1.823 | 0.429 |
Japan | 0.564 | 0.046 | 1.602 | 0.500 |
Korea | 1.524 | 0.585 | 3.018 | 0.795 |
Mexico | 1.962 | 0.946 | 3.645 | 0.977 |
Netherlands | 0.305 | 0.093 | 0.810 | 0.166 |
New Zealand | 0.198 | -0.141 | 1.042 | 0.317 |
Norway | 0.924 | -0.161 | 4.791 | 1.135 |
Poland | 1.881 | 0.585 | 3.088 | 0.971 |
Portugal | 2.476 | 1.070 | 4.867 | 1.355 |
Slovak Republic | -0.395 | -4.010 | 7.255 | 3.278 |
Spain | 1.406 | 0.452 | 3.151 | 0.585 |
Sweden | 0.076 | -6.792 | 2.027 | 1.912 |
Switzerland | 1.001 | 0.372 | 1.797 | 0.399 |
United Kingdom | 0.912 | 0.307 | 2.655 | 0.739 |
United States | 0.761 | 0.305 | 1.545 | 0.371 |
All | 0.864 | -6.792 | 7.255 | 1.047 |
Source: Own elaboration based on OECD Main Economic Indicators, IFS, and National Sources.
1 Real effective long term rates calculated as nominal rates minus effective inflation in the year. Nominal long term rates corresponds to 10 year government bonds or similar. For Chile indexed bonds yields are used. Year averages.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 4.835 | 1.253 | 8.211 | 2.230 |
Austria | 3.950 | 3.791 | 4.110 | 0.226 |
Belgium | 4.449 | 0.566 | 7.331 | 1.838 |
Canada | 4.466 | 1.233 | 8.405 | 1.967 |
Chile | 4.571 | 2.550 | 7.330 | 1.720 |
Czech Republic | 2.465 | 1.568 | 4.006 | 1.045 |
Denmark | 5.749 | 2.412 | 10.264 | 2.109 |
Finland | 5.466 | 2.440 | 9.053 | 2.118 |
France | 4.361 | 1.964 | 6.701 | 1.448 |
Germany | 4.326 | 2.131 | 6.288 | 1.113 |
Greece | 3.662 | -7.233 | 9.825 | 4.351 |
Hungary | 1.432 | -1.215 | 3.186 | 1.720 |
Iceland | 5.591 | 2.763 | 8.000 | 1.550 |
Ireland | 1.567 | -0.079 | 4.839 | 1.632 |
Italy | 4.371 | 1.332 | 7.997 | 2.166 |
Japan | 2.098 | 0.088 | 3.673 | 1.066 |
Korea | 4.711 | 0.862 | 8.871 | 2.694 |
Mexico | 4.845 | -1.568 | 16.744 | 4.321 |
Netherlands | 2.704 | 0.796 | 4.976 | 1.071 |
New Zealand | 4.535 | 2.122 | 7.387 | 1.468 |
Norway | 4.179 | -1.344 | 7.436 | 2.115 |
Poland | 4.382 | 3.034 | 5.451 | 1.022 |
Portugal | 3.970 | 2.033 | 7.289 | 1.647 |
Slovak Republic | -0.349 | -3.696 | 3.808 | 2.685 |
Spain | 4.523 | 1.263 | 7.956 | 2.143 |
Sweden | 4.684 | 1.248 | 7.788 | 1.942 |
Switzerland | 1.853 | -1.057 | 4.106 | 1.185 |
United Kingdom | 4.206 | 0.977 | 6.707 | 1.433 |
United States | 3.872 | 0.897 | 8.138 | 1.906 |
All | 4.010 | -7.233 | 16.744 | 2.303 |
Source: Own elaboration based on OECD Main Economic Indicators and IFS.
Note: Computed as the log changes of real stock market indices for domestic markets in each country. All indices deflated by domestic CPIs.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 3.236 | -6.930 | 13.666 | 6.803 |
Austria | 0.214 | -10.571 | 10.999 | 15.252 |
Belgium | 7.258 | -21.659 | 35.240 | 15.062 |
Canada | 3.965 | -24.217 | 28.142 | 12.499 |
Chile | 2.663 | -32.983 | 27.590 | 16.487 |
Czech Republic | 14.235 | -33.835 | 40.992 | 30.287 |
Denmark | 8.933 | -21.735 | 49.235 | 18.504 |
Finland | 8.229 | -57.899 | 61.773 | 36.661 |
France | 2.791 | -26.417 | 29.879 | 18.528 |
Germany | 4.919 | -29.558 | 31.118 | 18.865 |
Greece | 4.746 | -43.945 | 67.555 | 30.773 |
Hungary | 4.473 | -33.609 | 43.942 | 26.845 |
Iceland | 12.757 | -38.385 | 46.953 | 23.057 |
Ireland | 7.517 | -24.900 | 31.969 | 18.579 |
Italy | 1.165 | -39.873 | 69.081 | 26.233 |
Japan | -3.807 | -35.093 | 24.585 | 18.928 |
Korea | 1.083 | -54.076 | 66.551 | 28.792 |
Mexico | 9.665 | -42.743 | 34.965 | 22.416 |
Netherlands | 1.642 | -36.478 | 38.252 | 23.711 |
New Zealand | 1.412 | -33.298 | 20.973 | 12.657 |
Norway | 8.587 | -25.134 | 42.439 | 21.201 |
Poland | 11.236 | -33.104 | 36.536 | 25.433 |
Portugal | 3.381 | -27.630 | 43.256 | 24.531 |
Slovak Republic | 14.402 | -12.408 | 69.236 | 26.099 |
Spain | 6.883 | -22.058 | 62.705 | 22.421 |
Sweden | 10.666 | -37.691 | 57.308 | 23.887 |
Switzerland | 5.185 | -28.461 | 33.663 | 16.974 |
United Kingdom | 3.317 | -21.184 | 20.634 | 12.746 |
United States | 6.194 | -15.271 | 26.400 | 11.281 |
All | 5.427 | -57.899 | 69.236 | 20.635 |
Source: Own elaboration based on OECD Main Economic Indicators.
Note: Computed as the log changes of the sum of real private consumption (household and non-profits), real government final consumption and real gross fixed capital formation.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 3.832 | 0.268 | 6.111 | 1.947 |
Austria | 2.282 | 2.024 | 2.539 | 0.364 |
Belgium | 2.014 | -0.880 | 4.503 | 1.353 |
Canada | 3.038 | -1.303 | 5.315 | 1.680 |
Chile | 4.576 | -4.657 | 10.219 | 4.180 |
Czech Republic | 3.234 | 1.792 | 4.536 | 1.130 |
Denmark | 1.893 | -3.230 | 7.291 | 2.492 |
Finland | 1.692 | -6.278 | 6.297 | 4.275 |
France | 1.865 | -0.560 | 3.818 | 1.236 |
Germany | 1.722 | -1.752 | 4.469 | 1.699 |
Greece | 2.999 | -1.141 | 5.869 | 2.129 |
Hungary | 3.404 | -0.915 | 9.159 | 3.437 |
Iceland | 4.088 | -2.549 | 12.425 | 4.364 |
Ireland | 6.659 | 3.537 | 9.185 | 2.401 |
Italy | 1.452 | -4.536 | 4.557 | 2.096 |
Japan | 0.923 | -2.229 | 2.975 | 1.215 |
Korea | 4.118 | -15.019 | 10.001 | 5.758 |
Mexico | 2.929 | -13.258 | 8.283 | 5.671 |
Netherlands | 2.213 | -3.490 | 5.316 | 2.184 |
New Zealand | 3.609 | -1.107 | 7.261 | 2.296 |
Norway | 2.689 | -1.609 | 6.415 | 2.207 |
Poland | 2.945 | -0.387 | 6.857 | 2.584 |
Portugal | 3.855 | -0.548 | 6.553 | 2.117 |
Slovak Republic | 4.507 | -0.598 | 8.413 | 3.213 |
Spain | 3.146 | -2.748 | 7.471 | 2.728 |
Sweden | 1.712 | -4.057 | 4.488 | 2.074 |
Switzerland | 1.602 | -1.708 | 3.429 | 1.289 |
United Kingdom | 2.844 | -1.676 | 6.905 | 1.827 |
United States | 3.214 | -0.542 | 6.202 | 1.606 |
All | 2.695 | -15.019 | 12.425 | 2.774 |
Source: Own elaboration based on OECD Bank Statistics and OECD Main Economic Indicators.
Country | mean | min | max | st. dev. |
---|---|---|---|---|
Australia | 105.178 | 95.213 | 118.581 | 7.749 |
Austria | 247.809 | 241.259 | 254.359 | 9.263 |
Belgium | 291.774 | 219.571 | 365.779 | 35.033 |
Canada | 140.348 | 110.685 | 180.169 | 21.132 |
Chile | 100.584 | 82.520 | 136.346 | 15.642 |
Czech Republic | 109.361 | 97.288 | 124.122 | 11.223 |
Denmark | 114.330 | 77.119 | 148.415 | 20.222 |
Finland | 122.673 | 97.217 | 146.150 | 15.154 |
France | 236.109 | 224.426 | 254.429 | 8.765 |
Germany | 168.793 | 116.938 | 268.464 | 49.278 |
Greece | 69.855 | 50.866 | 104.403 | 20.704 |
Hungary | 73.632 | 59.700 | 94.819 | 13.226 |
Iceland | 88.277 | 54.556 | 147.149 | 35.562 |
Ireland | 337.898 | 147.228 | 500.307 | 116.351 |
Italy | 155.749 | 117.336 | 222.295 | 30.046 |
Japan | 166.883 | 141.734 | 225.223 | 24.361 |
Korea | 94.198 | 56.608 | 131.700 | 27.326 |
Mexico | 46.236 | 33.261 | 61.285 | 8.326 |
Netherlands | 384.141 | 213.657 | 597.025 | 129.745 |
New Zealand | 137.906 | 103.211 | 186.810 | 26.750 |
Norway | 80.465 | 52.668 | 159.743 | 24.827 |
Poland | 57.889 | 56.830 | 59.240 | 1.058 |
Portugal | 148.646 | 103.170 | 197.532 | 36.319 |
Slovak Republic | 85.934 | 77.060 | 93.154 | 6.226 |
Spain | 139.707 | 105.691 | 176.582 | 21.246 |
Sweden | 97.837 | 69.222 | 144.522 | 22.638 |
Switzerland | 409.050 | 258.519 | 660.909 | 119.501 |
United Kingdom | 149.411 | 73.870 | 392.820 | 84.457 |
United States | 93.180 | 79.512 | 113.440 | 12.628 |
All | 160.274 | 33.261 | 660.909 | 108.564 |
Here I present an analysis of the time series properties of the individual series used in the benchmark model. To facilitate comparison dependent variables are the explanatory variables used in the benchmark regressions with the same timing convention and restricting the sample to the sample of model (2).
Notes: 1Fixed Effects (FE) and difference GMM regressions eliminate country effects by taking differences. R2 for FE correspond to the within R2. Heteroskedasticity robust standard errors in parentheses. ***, **, * denote significant at 1%, 5% and 10%, respectively.
Dependent Variable: $$ {\Delta \ell }_{it}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.273*** (0.0811) |
0.154* (0.0832) |
0.350*** (0.1000) |
0.300*** (0.0970) |
0.285*** (0.0976) |
0.369*** (0.105) |
0.324*** (0.0987) |
0.312*** (0.100) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.230*** (0.0766) |
0.132** (0.0534) |
0.327*** (0.0816) |
0.277*** (0.0659) |
0.259*** (0.0666) |
0.324*** (0.0866) |
0.277*** (0.0727) |
0.266*** (0.0735) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.504 | 0.285 | 0.678 | 0.577 | 0.544 | 0.693 | 0.601 | 0.578 |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.219 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.429 | 0.173 | 0.0699 | 0.292 | 0.188 | 0.110 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.201 | 0.345 | 0.435 | 0.235 | 0.409 | 0.465 | ||
Arellano-Bond for AR(3) | 0.212 | 0.262 | 0.285 | 0.229 | 0.276 | 0.287 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.315 | 0.247 | ||||||
Number observations | 464 | 464 | 448 | 448 | 448 | 464 | 464 | 464 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Notes: 1Fixed Effects (FE) and difference GMM regressions eliminate country effects by taking differences. R2 for FE correspond to the within R2. Heteroskedasticity robust standard errors in parentheses. ***, **, * denote significant at 1%, 5% and 10%, respectively.
Dependent Variable: $$ {\text{ROE}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{ROE}}_{i,t-2}$$ |
0.518*** (0.145) |
0.427*** (0.0738) |
0.493*** (0.105) |
0.519*** (0.0831) |
0.533*** (0.0825) |
0.476*** (0.109) |
0.496*** (0.0908) |
0.505*** (0.0916) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.0697 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.849 | 0.858 | 0.923 | 0.943 | 0.956 | 0.985 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.873 | 0.894 | 0.906 | 0.855 | 0.872 | 0.880 | ||
Arellano-Bond for AR(3) | 0.687 | 0.681 | 0.675 | 0.688 | 0.683 | 0.678 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.350 | 0.273 | ||||||
Number observations | 479 | 479 | 463 | 463 | 463 | 479 | 479 | 479 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{CAP}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{CAP}}_{i,t-2}$$ |
0.828*** (0.0922) |
0.535*** (0.120) |
0.341 (0.240) |
0.347 (0.242) |
0.348 (0.243) |
0.359* (0.193) |
0.373* (0.197) |
0.371* (0.195) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.551 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.042 | 0.086 | 0.208 | 0.147 | 0.175 | 0.318 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.001 | 0.001 | 0.001 | 0.005 | 0.005 | 0.005 | ||
Arellano-Bond for AR(3) | 0.005 | 0.005 | 0.005 | 0.006 | 0.005 | 0.006 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.696 | 0.342 | ||||||
Number observations | 480 | 480 | 464 | 464 | 464 | 480 | 480 | 480 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{BSL}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{BSL}}_{i,t-2}$$ |
0.943*** (0.0159) |
0.836*** (0.0342) |
1.182*** (0.165) |
1.054*** (0.133) |
1.051*** (0.134) |
1.144*** (0.0743) |
1.052*** (0.0574) |
1.057*** (0.0606) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.269 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.004 | 0.002 | 0.00322 | 0.0216 | 0.00312 | 0.00721 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.434 | 0.383 | 0.381 | 0.442 | 0.409 | 0.410 | ||
Arellano-Bond for AR(3) | 0.646 | 0.604 | 0.604 | 0.628 | 0.594 | 0.595 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.896 | 0.778 | ||||||
Number observations | 480 | 480 | 464 | 464 | 464 | 480 | 480 | 480 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{DEPOSIT COSTS}}_{i,t-2}$$ |
0.881*** (0.0457) |
0.657*** (0.101) |
1.139*** (0.201) |
1.086*** (0.155) |
0.992*** (0.136) |
0.766*** (0.169) |
0.760*** (0.148) |
0.753*** (0.122) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.470 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.223 | 0.360 | 0.004 | 0.082 | 0.220 | 0.003 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.395 | 0.392 | 0.386 | 0.255 | 0.259 | 0.273 | ||
Arellano-Bond for AR(3) | 0.731 | 0.742 | 0.762 | 0.792 | 0.800 | 0.806 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.855 | 0.698 | ||||||
Number observations | 477 | 477 | 459 | 459 | 459 | 477 | 477 | 477 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{LENDING RATE}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{LENDING RATE}}_{i,t-2}$$ |
0.745*** (0.0903) |
0.517*** (0.115) |
0.559*** (0.0878) |
0.557*** (0.0957) |
0.535*** (0.0994) |
0.587*** (0.125) |
0.593*** (0.124) |
0.585*** (0.125) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.315 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.567 | 0.915 | 0.571 | 0.105 | 0.338 | 0.364 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.721 | 0.719 | 0.722 | 0.743 | 0.744 | 0.745 | ||
Arellano-Bond for AR(3) | 0.254 | 0.236 | 0.232 | 0.255 | 0.247 | 0.247 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.669 | 0.509 | ||||||
Number observations | 476 | 476 | 473 | 473 | 473 | 476 | 476 | 476 |
Number countries | 29 | 29 | 28 | 28 | 28 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{PROVISIONS}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{PROVISIONS}}_{i,t-2}$$ |
0.666*** (0.0948) |
0.474*** (0.0655) |
0.589*** (0.0702) |
0.639*** (0.0564) |
0.633*** (0.0574) |
0.607*** (0.0664) |
0.670*** (0.0571) |
0.666*** (0.0578) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.405 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.923 | 0.072 | 0.194 | 0.981 | 0.123 | 0.282 | ||
Hansen test | 0.999 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.591 | 0.608 | 0.607 | 0.606 | 0.624 | 0.624 | ||
Arellano-Bond for AR(3) | 0.211 | 0.213 | 0.212 | 0.200 | 0.202 | 0.201 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.509 | 0.349 | ||||||
Number observations | 479 | 479 | 463 | 463 | 463 | 479 | 479 | 479 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{LONG TERM RATE}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{LONG TERM RATE}}_{i,t-2}$$ |
0.609*** (0.104) |
0.453*** (0.142) |
0.591 (0.573) |
0.479 (0.375) |
0.366 (0.301) |
0.735*** (0.216) |
0.685*** (0.182) |
0.648*** (0.184) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.197 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.019 | 0.052 | 0.028 | 0.080 | 0.135 | 0.088 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.888 | 0.891 | 0.933 | 0.862 | 0.849 | 0.844 | ||
Arellano-Bond for AR(3) | 0.261 | 0.291 | 0.345 | 0.254 | 0.260 | 0.270 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.642 | 0.568 | ||||||
Number observations | 470 | 470 | 461 | 461 | 461 | 470 | 470 | 470 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{STOCK RETURNS}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{STOCK RETURNS}}_{i,t-2}$$ |
0.224*** (0.0742) |
0.186*** (0.0439) |
0.402* (0.209) |
0.350 (0.218) |
0.351 (0.217) |
0.477** (0.212) |
0.442** (0.222) |
0.435* (0.224) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.0966 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.805 | 0.709 | 0.910 | 0.928 | 0.936 | 0.982 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.859 | 0.953 | 0.949 | 0.762 | 0.820 | 0.832 | ||
Arellano-Bond for AR(3) | 0.575 | 0.590 | 0.591 | 0.552 | 0.554 | 0.554 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.495 | 0.502 | ||||||
Number observations | 477 | 477 | 475 | 475 | 475 | 477 | 477 | 477 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\Delta \text{AGG. DEMAND}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\Delta \text{AGG. DEMAND}}_{i,t-2}$$ |
0.402*** (0.0949) |
0.304*** (0.0703) |
0.423** (0.212) |
0.379** (0.175) |
0.375** (0.167) |
0.344* (0.204) |
0.304 (0.187) |
0.305 (0.190) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.128 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.284 | 0.453 | 0.726 | 0.335 | 0.324 | 0.541 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.987 | 0.903 | 0.891 | 0.822 | 0.726 | 0.730 | ||
Arellano-Bond for AR(3) | 0.655 | 0.604 | 0.604 | 0.584 | 0.530 | 0.528 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.323 | 0.273 | ||||||
Number observations | 479 | 479 | 478 | 478 | 478 | 479 | 479 | 479 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\text{ASSETS/GDP}}_{i,t-1}$$ | (1) OLS |
(2) FE |
(3) difference GMM 2 collapsed |
(4) difference GMM 4 collapsed |
(5) difference GMM 6 collapsed | (6) system GMM 2 collapsed |
(7) system GMM 4 collapsed |
(8) system GMM 6 collapsed |
---|---|---|---|---|---|---|---|---|
$$ {\text{ASSETS/GDP}}_{i,t-2}$$ |
1.038*** (0.0115) |
0.986*** (0.0182) |
0.837*** (0.110) |
0.835*** (0.111) |
0.833*** (0.110) |
1.106*** (0.0219) |
1.106*** (0.0230) |
1.104*** (0.0229) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
$$ \sigma^2_{\mu_i} / \sigma^2_{v_{it}}$$ | 0.574 | |||||||
H0: joint validity of moment restrictions | ||||||||
Sargan test | 0.086 | 0.119 | 0.308 | 0.034 | 0.0401 | 0.081 | ||
Hansen test | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||
H0: residuals are serially uncorrelated | ||||||||
Arellano-Bond for AR(2) | 0.229 | 0.229 | 0.227 | 0.252 | 0.252 | 0.252 | ||
Arellano-Bond for AR(3) | 0.167 | 0.166 | 0.169 | 0.146 | 0.146 | 0.148 | ||
Number of instruments | 30 | 32 | 34 | 32 | 34 | 36 | ||
R2 | 0.985 | 0.941 | ||||||
Number observations | 479 | 479 | 462 | 462 | 462 | 479 | 479 | 479 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\Delta \ell }_{it}$$ | (1) 4 lags | (2) 4 collapsed | (3) 12 collapsed | (4) all collapsed |
---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.318*** (0.054) |
0.308** (0.126) |
0.222*** (0.072) |
0.214*** (0.071) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.050 (0.066) |
0.117 (0.101) |
0.117** (0.058) |
0.054 (0.061) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.074 (0.151) |
-1.072 (2.228) |
1.722 (2.122) |
0.384 (0.718) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.008 (0.016) |
0.032 (0.127) |
-0.143 (0.142) |
-0.031 (0.056) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.076 (0.050) |
-0.019 (0.177) |
0.006 (0.144) |
-0.009 (0.081) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.243** (0.102) |
-0.367* (0.217) |
-0.311* (0.175) |
-0.288** (0.132) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
-0.068 (0.315) |
0.084 (1.263) |
0.450 (0.678) |
-0.212 (0.450) |
$$ {\text{LENDING RATE}}_{it}$$ |
0.424** (0.177) |
0.213 (0.731) |
0.324 (0.457) |
0.666*** (0.243) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
0.491** (0.237) |
0.465 (0.737) |
0.794** (0.399) |
0.396 (0.345) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.044** (0.017) |
0.112 (0.082) |
0.074* (0.040) |
0.052*** (0.017) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
1.250*** (0.217) |
0.372 (0.452) |
0.780*** (0.248) |
1.161*** (0.230) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.005 (0.004) |
0.004 (0.015) |
0.011 (0.010) |
0.014* (0.008) |
H0: $$ {\text{CAP}}_{i,t-1} = 0$$ | ||||
$$ {\text{CAP}}^2_{i,t-1} = 0$$ [p-value] | [0.852] | [0.777] | [0.509] | [0.854] |
Year effects | Yes | Yes | Yes | Yes |
Country effects1 | Yes | Yes | Yes | Yes |
Number observations | 480 | 480 | 480 | 480 |
Number countries | 29 | 29 | 29 | 29 |
Number of instruments | 480 | 73 | 169 | 383 |
H0: joint validity of moment restrictions | ||||
Sargan [p-value] | [0.655] | [0.363] | [0.739] | [0.137] |
Hansen [p-value] | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||
Arellano-Bond for AR(2) [p-value] | [0.003] | [0.017] | [0.004] | [0.004] |
Arellano-Bond for AR(3) [p-value] | [0.028] | [0.095] | [0.037] | [0.043] |
Notes: *Canada all banks chained with commercial banks for 1982-1987. Greece all banks chained with large commercial banks for 1979-1988. Mexico all banks chained with commercial banks for 1990-1999. US all banks chained with the sum of commercial, saving and cooperative banks for 1979.
Number of | ||||
---|---|---|---|---|
# | Country | Bank Group | Sample | observations |
1 | Australia | All banks | 1987-2003 | 17 |
2 | Austria | All banks | 1988-2008 | 21 |
3 | Belgium | All banks | 1982-2009 | 28 |
4 | Canada* | All banks | 1983-2009 | 27 |
5 | Chile | All banks | 1991-2009 | 19 |
6 | Czech Republic | All banks | 1994-2005 | 12 |
7 | Denmark | All banks | 1980-2008 | 29 |
8 | Finland | All banks | 1980-2009 | 30 |
9 | France | All banks | 1989-2008 | 20 |
10 | Germany | All banks | 1980-2008 | 29 |
11 | Greece* | Commercial banks | 1980-2009 | 30 |
12 | Hungary | Commercial banks | 1995-2008 | 14 |
13 | Iceland | All banks | 1980-2003 | 24 |
14 | Ireland | All banks | 1996-2008 | 13 |
15 | Italy | All banks | 1985-2009 | 25 |
16 | Japan | All banks | 1990-2008 | 19 |
17 | Korea | All banks | 1991-2008 | 18 |
18 | Luxembourg | All banks | 1980-2008 | 29 |
19 | Mexico* | All banks | 1991-2009 | 19 |
20 | Netherlands | All banks | 1980-2009 | 30 |
21 | New Zealand | All banks | 1991-2009 | 19 |
22 | Norway | All banks | 1980-2009 | 30 |
23 | Poland | All banks | 1994-2008 | 15 |
24 | Portugal | Commercial banks | 1980-2008 | 29 |
25 | Slovak Republic | All banks | 1997-2009 | 13 |
26 | Spain | All banks | 1980-2008 | 29 |
27 | Sweden | All banks | 1980-2008 | 29 |
28 | Switzerland | All banks | 1980-2008 | 29 |
29 | Turkey | Commercial banks | 1982-2009 | 28 |
30 | United Kingdom | Large commercial banks | 1985-2008 | 24 |
31 | United States* | All banks | 1980-2007 | 28 |
All | All | 726 | ||
Average | Average | 23.42 |
Country | Observations | Sample Period | |
---|---|---|---|
1 | Australia | 13 | 1991 - 2003 |
2 | Austria | 2 | 1998 - 1999 |
3 | Belgium | 25 | 1983 - 2007 |
4 | Canada | 25 | 1984 - 2008 |
5 | Chile | 14 | 1996 - 2009 |
6 | Czech Republic | 5 | 2001 - 2005 |
7 | Denmark | 22 | 1981 - 2002 |
8 | Finland | 16 | 1988 - 2004 |
9 | France | 15 | 1990 - 2004 |
10 | Germany | 22 | 1981 - 2002 |
11 | Greece | 13 | 1986 - 2003 |
12 | Hungary | 8 | 2001 - 2008 |
13 | Iceland | 10 | 1994 - 2003 |
14 | Ireland | 9 | 1997 - 2005 |
15 | Italy | 24 | 1986 - 2009 |
16 | Japan | 18 | 1991 - 2008 |
17 | Korea | 17 | 1992 - 2008 |
18 | Mexico | 12 | 1995 - 2007 |
19 | Netherlands | 16 | 1994 - 2009 |
20 | New Zealand | 17 | 1992 - 2008 |
21 | Norway | 27 | 1981 - 2008 |
22 | Poland | 6 | 2001 - 2006 |
23 | Portugal | 11 | 1989 - 1999 |
24 | Slovak Republic | 8 | 2000 - 2007 |
25 | Spain | 22 | 1981 - 2002 |
26 | Sweden | 25 | 1981 - 2005 |
27 | Switzerland | 28 | 1981 - 2008 |
28 | United Kingdom | 23 | 1986 - 2008 |
29 | United States | 27 | 1981 - 2007 |
All | All | 480 | 1981 - 2009 |
Average | Average | 16.55 | 1989.72 - 2005.55 |
Min | Min | 2 | 1981 - 1999 |
Max | Max | 28 | 2001 - 2009 |
Source: Own elaboration based on OECD Bank Statistics and OECD Main Economic Indicators.
Country | $$ {\Delta \ell }_{it}$$ | $$ {\text{ROE}}_{i,t-1}$$ | $$ {\text{CAP}}_{i,t-1}$$ | $$ {\text{BSL}}_{i,t-1}$$ |
---|---|---|---|---|
Australia | 5.711 | 9.152 | 10.096 | 7.096 |
Austria | 5.990 | 8.003 | 4.621 | 16.025 |
Belgium | 3.976 | 9.267 | 3.071 | 29.528 |
Canada | 3.628 | 12.720 | 5.279 | 17.304 |
Chile | 7.093 | 13.011 | 8.517 | 16.060 |
Czech Republic | 2.011 | 9.744 | 8.483 | 23.766 |
Denmark | 5.244 | 6.774 | 7.628 | 24.411 |
Finland | 2.369 | 0.014 | 6.820 | 16.673 |
France | 1.255 | 6.150 | 4.260 | 16.710 |
Germany | 4.572 | 6.114 | 3.793 | 17.598 |
Greece | 13.733 | 14.109 | 5.732 | 33.412 |
Hungary | 13.757 | 15.414 | 9.326 | 16.430 |
Iceland | 13.134 | 8.737 | 7.321 | 13.562 |
Ireland | 21.998 | 13.356 | 5.911 | 23.902 |
Italy | 4.826 | 7.307 | 6.435 | 14.829 |
Japan | -1.056 | -1.992 | 3.951 | 19.669 |
Korea | 12.331 | -0.023 | 5.775 | 17.291 |
Mexico | -2.156 | 6.920 | 7.349 | 26.933 |
Netherlands | 6.768 | 10.864 | 3.878 | 21.291 |
New Zealand | 8.270 | 16.752 | 5.700 | 11.114 |
Norway | 7.509 | 5.033 | 5.457 | 15.747 |
Poland | 6.805 | 10.240 | 9.492 | 22.104 |
Portugal | 10.508 | 7.084 | 9.863 | 21.373 |
Slovak Republic | 4.234 | 12.174 | 7.325 | 25.821 |
Spain | 5.194 | 8.600 | 7.862 | 18.756 |
Sweden | 4.110 | 9.999 | 5.762 | 21.514 |
Switzerland | 3.289 | 8.415 | 5.904 | 14.995 |
United Kingdom | 9.240 | 13.102 | 4.560 | 14.950 |
United States | 2.575 | 9.698 | 6.730 | 19.119 |
All | 5.812 | 8.536 | 6.087 | 18.924 |
Source: Own elaboration based on OECD Bank Statistics, OECD Main Economic Indicators, IFS, and National Sources.
DEPOSIT | PROVI- | LENDING | LONG-TERM | STOCK | $$ \Delta$$ AGG. | ASSETS | |
---|---|---|---|---|---|---|---|
Country | COSTS$$ _{i,t-1}$$ | SIONS$$ _{i,t-1}$$ | RATE$$ _{it}$$ | RATE$$ _{it}$$ | RETURNS$$ _{it}$$ | DEMAND$$ _{it}$$ | TO GDP$$ _{it}$$ |
Australia | 8.758 | 0.704 | 7.060 | 4.835 | 3.236 | 3.832 | 105.178 |
Austria | 8.607 | 0.733 | 5.284 | 3.950 | 0.214 | 2.282 | 247.809 |
Belgium | 20.617 | 0.638 | 6.924 | 4.449 | 7.258 | 2.014 | 291.774 |
Canada | 6.998 | 0.549 | 4.889 | 4.466 | 3.965 | 3.038 | 140.348 |
Chile | 11.880 | 1.111 | 7.133 | 4.571 | 2.663 | 4.576 | 100.584 |
Czech Republic | 4.129 | -1.628 | 4.066 | 2.465 | 14.235 | 3.234 | 109.361 |
Denmark | 10.115 | 1.959 | 7.663 | 5.749 | 8.933 | 1.893 | 114.330 |
Finland | 9.517 | 0.172 | 5.106 | 5.466 | 8.229 | 1.692 | 122.673 |
France | 21.712 | 0.870 | 5.796 | 4.361 | 2.791 | 1.865 | 236.109 |
Germany | 9.316 | 0.618 | 8.101 | 4.326 | 4.919 | 1.722 | 168.793 |
Greece | 10.019 | 1.186 | 9.644 | 3.662 | 4.746 | 2.999 | 69.855 |
Hungary | 8.298 | 0.411 | 4.165 | 1.432 | 4.473 | 3.404 | 73.632 |
Iceland | 11.769 | 1.490 | 10.466 | 5.591 | 12.757 | 4.088 | 88.277 |
Ireland | 9.511 | 0.196 | 0.997 | 1.567 | 7.517 | 6.659 | 337.898 |
Italy | 11.190 | 1.197 | 6.517 | 4.371 | 1.165 | 1.452 | 155.749 |
Japan | 2.907 | 0.564 | 2.450 | 2.098 | -3.807 | 0.923 | 166.883 |
Korea | 6.984 | 1.524 | 4.358 | 4.711 | 1.083 | 4.118 | 94.198 |
Mexico | 20.288 | 1.962 | 5.880 | 4.845 | 9.665 | 2.929 | 46.236 |
Netherlands | 9.225 | 0.305 | 2.511 | 2.704 | 1.642 | 2.213 | 384.141 |
New Zealand | 6.863 | 0.198 | 7.904 | 4.535 | 1.412 | 3.609 | 137.906 |
Norway | 9.879 | 0.924 | 6.335 | 4.179 | 8.587 | 2.689 | 80.465 |
Poland | 7.280 | 1.881 | 7.118 | 4.382 | 11.236 | 2.945 | 57.889 |
Portugal | 11.265 | 2.476 | 8.424 | 3.970 | 3.381 | 3.855 | 148.646 |
Slovak Republic | 5.707 | -0.395 | 3.463 | -0.349 | 14.402 | 4.507 | 85.934 |
Spain | 9.180 | 1.406 | 4.886 | 4.523 | 6.883 | 3.146 | 139.707 |
Sweden | 12.141 | 0.076 | 6.546 | 4.684 | 10.666 | 1.712 | 97.837 |
Switzerland | 7.339 | 1.001 | 2.759 | 1.853 | 5.185 | 1.602 | 409.050 |
United Kingdom | 6.881 | 0.912 | 4.345 | 4.206 | 3.317 | 2.844 | 149.411 |
United States | 6.870 | 0.761 | 5.194 | 3.872 | 6.194 | 3.214 | 93.180 |
All | 9.991 | 0.864 | 5.725 | 4.010 | 5.427 | 2.695 | 160.274 |
Notes: 1Fixed Effects (FE) and Difference GMM regressions eliminate country effects by taking differences. R2 for FE corresponds to the within R2. k lags means k lags are used to instrument each explanatory variable, i.e. $$ x_{i,t-1}, \ldots, x_{i,t-k}$$ are used as instruments for $$ \Delta x_{it}$$ when xit is a predetermined variable and $$ x_{i,t-2}, \ldots, x_{i,t-1-k}$$ are used as instruments for $$ \Delta x_{it}$$ when xit is an endogenous variable. Heteroskedasticity robust standard errors in parentheses. P-values in brackets. ***, **, * denote significant at 1%, 5% and 10%, respectively.
Dependent Variable: $$ {\Delta \ell }_{it}$$ | (1) OLS |
(2) FE |
(3) 2 lags |
(4) 6 lags |
(5) 2 collapsed |
(6) 6 collapsed |
---|---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.318*** (0.0590) |
0.188** (0.0799) |
0.182** (0.0745) |
0.182** (0.0745) |
0.106 (0.0799) |
0.148* (0.0802) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0505 (0.0746) |
0.0446 (0.0623) |
0.0446 (0.0582) |
0.0451 (0.0580) |
-0.00231 (0.0714) |
-0.00934 (0.0566) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.0741 (0.156) |
-0.178 (0.145) |
-0.203 (0.126) |
-0.198 (0.128) |
-0.434 (0.541) |
-0.508 (0.392) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.00751 (0.0191) |
0.0384* (0.0214) |
0.0373* (0.0206) |
0.0382* (0.0199) |
0.0312 (0.0709) |
0.0341 (0.0405) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.0759 (0.0537) |
0.177* (0.0991) |
0.191* (0.0980) |
0.194** (0.0961) |
0.0149 (0.398) |
0.0564 (0.320) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.243*** (0.0695) |
-0.0519 (0.152) |
-0.0710 (0.147) |
-0.0674 (0.148) |
-0.222 (0.236) |
-0.314 (0.289) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
-0.0681 (0.488) |
-0.428 (0.533) |
-0.484 (0.499) |
-0.479 (0.498) |
-0.727 (1.154) |
-0.632 (0.738) |
$$ {\text{LENDING RATE}}_{it}$$ |
0.424** (0.188) |
0.106 (0.271) |
0.0875 (0.263) |
0.0939 (0.261) |
1.500* (0.856) |
0.913 (0.731) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
0.491* (0.254) |
1.068** (0.390) |
1.150*** (0.393) |
1.148*** (0.393) |
-0.0499 (1.014) |
0.573 (0.837) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.0436* (0.0245) |
0.0269 (0.0201) |
0.0298 (0.0186) |
0.0303 (0.0187) |
0.205*** (0.0600) |
0.150*** (0.0484) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
1.250*** (0.188) |
1.079*** (0.211) |
1.101*** (0.196) |
1.097*** (0.194) |
0.694 (0.489) |
0.515 (0.345) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00548 (0.00389) |
-0.0309*** (0.0111) |
-0.0325*** (0.0113) |
-0.0324*** (0.0110) |
-0.103** (0.0463) |
-0.0761*** (0.0291) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.882] | [0.021] | [0.004] | [0.004] | [0.059] | [0.018] |
H0: joint validity of moment restrictions | ||||||
Sargan test | [0.134] | [0.146] | [0.053] | [0.252] | ||
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] | ||
H0: residuals are serially uncorrelated | ||||||
Arellano-Bond for AR(2) | [0.004] | [0.004] | [0.013] | [0.014] | ||
Arellano-Bond for AR(3) | [0.054] | [0.054] | [0.235] | [0.170] | ||
Number of instruments | 444 | 446 | 52 | 100 | ||
R2 | 0.462 | 0.435 | ||||
Number observations | 480 | 480 | 446 | 446 | 446 | 446 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 |
Notes: 1Fixed Effects (FE) regressions eliminate country effects by taking first differences. R2 for FE corresponds to the within R2. k lags means k lags are used to instrument each explanatory variable, i.e. $$ x_{i,t-1}, \ldots, x_{i,t-k}$$ are used as instruments for $$ \Delta x_{it}$$ when xit is a predetermined variable and $$ x_{i,t-2}, \ldots, x_{i,t-1-k}$$ are used as instruments for $$ \Delta x_{it}$$ when xit is an endogenous variable. Heteroskedasticity robust standard errors in parentheses. P-values in brackets. ***, **, * denote significant at 1%, 5% and 10%, respectively.
Dependent Variable:
$$ {\Delta \ell }_{it}$$ |
(1) OLS |
(2) FE |
(3) Difference GMM 2 collapsed |
(4) Difference GMM 6 collapsed |
(5) 2 collapsed |
(6) System GMM 6 collapsed |
---|---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.215*** (0.0632) |
0.151** (0.0630) |
0.153** (0.0742) |
0.170** (0.0711) |
0.243*** (0.0603) |
0.232*** (0.0623) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.267*** (0.0629) |
0.208*** (0.0515) |
0.204*** (0.0634) |
0.213*** (0.0565) |
0.241*** (0.0618) |
0.241*** (0.0506) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0519 (0.0614) |
0.0465 (0.0578) |
0.0296 (0.0815) |
0.0181 (0.0564) |
0.0598 (0.0651) |
0.0617 (0.0549) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.173 (0.156) |
-0.223* (0.124) |
-0.747 (0.466) |
-0.569** (0.278) |
-0.389* (0.221) |
-0.253 (0.225) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0127 (0.0191) |
0.0389** (0.0188) |
0.00241 (0.0642) |
0.0221 (0.0416) |
0.0521 (0.0355) |
0.0603** (0.0294) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.107* (0.0553) |
0.211** (0.0950) |
0.0661 (0.421) |
0.168 (0.274) |
0.0886 (0.185) |
0.0703 (0.153) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.297*** (0.0765) |
-0.113 (0.142) |
-0.367 (0.266) |
-0.467 (0.301) |
-0.486*** (0.158) |
-0.398*** (0.147) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
-0.0787 (0.453) |
-0.334 (0.495) |
-0.0118 (1.025) |
-0.00792 (0.672) |
0.288 (0.678) |
0.335 (0.463) |
$$ {\text{LENDING RATE}}_{it}$$ |
0.410** (0.187) |
0.0466 (0.248) |
1.744 (1.078) |
0.580 (0.700) |
1.521* (0.840) |
0.454 (0.535) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
0.548** (0.269) |
1.163*** (0.370) |
-0.0436 (1.191) |
0.755 (0.754) |
-0.405 (0.895) |
0.826 (0.614) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.0493** (0.0230) |
0.0347 (0.0208) |
0.225*** (0.0677) |
0.152*** (0.0460) |
0.243*** (0.0602) |
0.162*** (0.0416) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
1.193*** (0.164) |
1.093*** (0.215) |
0.560 (0.434) |
0.462 (0.304) |
0.723* (0.380) |
0.551** (0.229) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00477 (0.00399) |
-0.0291*** (0.0104) |
-0.0702 (0.0549) |
-0.0369 (0.0353) |
0.00939 (0.0138) |
0.00749 (0.0118) |
Year effects | Yes | Yes | Yes | Yes | Yes | Yes |
Country effects1 | No | Yes | Yes | Yes | Yes | Yes |
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.482 | 0.359 | 0.356 | 0.383 | 0.483 | 0.473 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.538] | [0.028] | [0.033] | [0.048] | [0.029] | [0.067] |
H0: joint validity of moment restrictions | ||||||
Sargan test | [0.717] | [0.742] | [0.771] | [0.886] | ||
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] | ||
H0: residuals are serially uncorrelated | ||||||
Arellano-Bond for AR(2) | [0.691] | [0.754] | [0.983] | [0.985] | ||
Arellano-Bond for AR(3) | [0.611] | [0.642] | [0.701] | [0.700] | ||
Number of instruments | 52 | 100 | 65 | 113 | ||
R2 | 0.510 | 0.467 | ||||
Number observations | 464 | 464 | 430 | 430 | 464 | 464 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable:
$$ {\Delta \ell }_{it}$$ |
(1) |
(2) ROE |
(3) CAP |
(4) BSL |
(5) $$ {\text{ROE}}^2$$ |
(6) $$ {\text{BSL}}^2$$ |
---|---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.233*** (0.0604) |
0.266*** (0.0609) |
0.247*** (0.0614) |
0.227*** (0.0583) |
0.235*** (0.0638) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.241*** (0.0581) |
0.249*** (0.0607) |
0.235*** (0.0661) |
0.230*** (0.0607) |
0.235*** (0.0590) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.0554 (0.0649) |
0.165* (0.0997) |
0.0568 (0.0631) |
||
$$ {\text{ROE}}^2_{i,t-1}$$ |
0.00174* (0.00103) |
|||||
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
-0.295 (0.264) |
-0.465** (0.230) |
-0.414* (0.244) |
||
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
0.0515 (0.0398) |
0.0558* (0.0331) |
0.0453 (0.0349) |
||
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.0553 (0.211) |
0.0957 (0.184) |
0.116 (0.744) |
||
$$ {\text{BSL}}^2_{i,t-1}$$ |
-0.00152 (0.0183) |
|||||
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.562*** (0.178) |
-0.479*** (0.184) |
-0.568*** (0.184) |
-0.486*** (0.150) |
-0.529*** (0.153) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
0.416 (0.658) |
-0.0590 (0.611) |
0.0967 (0.638) |
0.550 (0.608) |
0.375 (0.638) |
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
1.713* (0.890) |
1.770** (0.865) |
1.661** (0.846) |
1.339 (0.857) |
1.802** (0.789) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
-0.737 (0.951) |
-0.611 (0.879) |
-0.921 (0.946) |
-0.319 (0.867) |
-0.652 (0.876) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
0.224*** (0.0716) |
0.242*** (0.0686) |
0.239*** (0.0646) |
0.237*** (0.0582) |
0.258*** (0.0587) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
0.833** (0.407) |
0.893** (0.402) |
0.858** (0.404) |
0.620 (0.384) |
0.865** (0.350) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
0.00901 (0.0126) |
0.0106 (0.0140) |
0.00921 (0.0121) |
0.00892 (0.0129) |
0.0111 (0.0133) |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | [0.076] | [0.019] | [0.023] | ||
H0: $$ {\text{ROE}}_{i,t-1} = {\text{ROE}}^2_{i,t-1} = 0$$ | [0.189] | |||||
H0: $$ {\text{BSL}}_{i,t-1} = {\text{BSL}}^2_{i,t-1} = 0$$ | [0.950] | |||||
H0: joint validity of moment restrictions | ||||||
Sargan test | [0.771] | [0.693] | [0.638] | [0.689] | [0.869] | [0.871] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||||
Arellano-Bond for AR(2) | [0.983] | [0.972] | [0.922] | [0.967] | [0.859] | [0.985] |
Number of instruments | 65 | 56 | 59 | 56 | 68 | 71 |
Number observations | 464 | 464 | 464 | 464 | 464 | 464 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable:
$$ {\Delta \ell }_{it}$$ |
(1) |
(2) ROE even if E < 0 |
(3) ROA |
(4) ROA and LEVERAGE |
---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.243*** (0.0603) |
0.223*** (0.0542) |
0.233*** (0.0591) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.241*** (0.0618) |
0.233*** (0.0599) |
0.236*** (0.0602) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.0594 (0.0652) |
||
ROA$$ _{i,t-1}$$ |
1.917 (1.451) |
1.882 (1.845) |
||
LEVERAGE$$ _{i,t-1}$$ |
-1.570 (1.849) |
|||
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
-0.399* (0.223) |
-0.433* (0.245) |
-15.40 (21.27) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
0.0528 (0.0357) |
0.0505 (0.0307) |
0.799 (1.142) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.0884 (0.185) |
0.0655 (0.180) |
0.0630 (0.148) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.486*** (0.158) |
-0.506*** (0.149) |
-0.415* (0.217) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
0.285 (0.677) |
0.561 (0.829) |
0.0378 (1.095) |
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
1.522* (0.840) |
1.484* (0.841) |
1.475* (0.864) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
-0.407 (0.895) |
-0.313 (0.841) |
-0.379 (0.826) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
0.243*** (0.0602) |
0.236*** (0.0595) |
0.264*** (0.0642) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
0.724* (0.380) |
0.603 (0.425) |
0.730* (0.415) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
0.00939 (0.0138) |
0.0112 (0.0144) |
0.00900 (0.0149) |
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.483 | 0.483 | 0.457 | 0.469 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | [0.029] | [0.025] | [0.692] |
H0: ROA$$ _{i,t-1} =$$ LEVERAGE$$ _{i,t-1} = 0$$ | [0.490] | |||
H0: joint validity of moment restrictions | ||||
Sargan test | [0.771] | [0.771] | [0.629] | [0.810] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||
Arellano-Bond for AR(2) | [0.983] | [0.983] | [0.984] | [0.947] |
Number of instruments | 65 | 65 | 65 | 68 |
Number observations | 464 | 464 | 464 | 463 |
Number countries | 29 | 29 | 29 | 29 |
Dependent Variable:
$$ {\Delta \ell }_{it}$$ |
(1) |
(2) BSL * SMALL |
(3) restricted sample |
(4) SEC + RES ASSETS |
(5) DEPOSITS ASSETS |
---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.358*** (0.0713) |
0.405*** (0.0925) |
0.228*** (0.0628) |
0.243*** (0.0599) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.0558 (0.0858) |
0.227*** (0.0819) |
0.232*** (0.0577) |
0.265*** (0.0477) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.414** (0.175) |
-0.291*** (0.0987) |
0.0681 (0.0650) |
0.0874 (0.0661) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
-0.963*** (0.348) |
-0.674* (0.394) |
-0.392* (0.235) |
-0.358 (0.250) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
0.0145 (0.0363) |
0.0292 (0.0357) |
0.0491 (0.0338) |
0.0472 (0.0332) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.191 (0.183) |
|||
$$ {\text{BSL}}_{i,t-1}*\text{SMALL}_{i,t-1}$$ |
0.00874* (0.00526) |
||||
(SEC+RES)/ASSETS$$ _{i,t-1}$$ |
-0.0588 (0.131) |
||||
DEPOSITS/ASSETS$$ _{i,t-1}$$ |
-0.174* (0.0908) |
||||
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.241 (0.251) |
-0.644** (0.313) |
-0.489*** (0.152) |
-0.563*** (0.171) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
-0.00191 (1.138) |
-1.859* (1.037) |
0.289 (0.659) |
0.593 (0.582) |
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
-0.531 (0.541) |
0.0682 (0.531) |
1.833** (0.911) |
1.824* (0.952) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
0.556 (1.107) |
1.279 (0.819) |
-0.462 (0.893) |
-0.405 (0.818) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
-0.0357 (0.0887) |
0.122 (0.0814) |
0.233*** (0.0606) |
0.207*** (0.0682) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
-0.271 (0.785) |
-0.771 (0.863) |
0.868** (0.379) |
0.802** (0.395) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
-0.0233** (0.0116) |
-0.0205 (0.0166) |
0.0113 (0.0150) |
-0.00333 (0.0143) |
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.483 | 0.414 | 0.632 | 0.460 | 0.508 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | [0.00008] | [0.046] | [0.022] | [0.068] |
H0: joint validity of moment restrictions | |||||
Sargan test | [0.771] | [0.002] | [0.002] | [0.779] | [0.423] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | |||||
Arellano-Bond for AR(2) | [0.983] | [0.940] | [0.171] | [0.970] | [0.992] |
Number of instruments | 65 | 65 | 65 | 65 | 65 |
Number observations | 464 | 249 | 249 | 464 | 464 |
Number countries | 29 | 18 | 18 | 29 | 29 |
Dependent Variable: $$ {\Delta \ell }_{it}$$ | (1) | (2) | (3) | (4) |
---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.240*** (0.0586) |
0.237*** (0.0635) |
0.232*** (0.0625) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.240*** (0.0626) |
0.244*** (0.0619) |
0.236*** (0.0605) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.0474 (0.0660) |
0.0555 (0.0628) |
0.0591 (0.0644) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
|||
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
|||
$$ {\text{CAP}}_{i,t-1}*({\text{CAP}}_{i,t-1}\geq P25)$$ |
0.0754 (0.807) |
|||
$$ {\text{CAP}}_{i,t-1}*({\text{CAP}}_{i,t-1}\geq 4\%)$$ |
0.292 (0.363) |
|||
$$ {\text{CAP}}_{i,t-1}*({\text{CAP}}_{i,t-1}\geq 6\%)$$ |
0.258 (0.218) |
|||
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.105 (0.212) |
0.0919 (0.185) |
0.0753 (0.177) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.478*** (0.174) |
-0.509*** (0.168) |
-0.551*** (0.171) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
0.198 (0.671) |
0.427 (0.708) |
0.504 (0.715) |
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
1.404* (0.798) |
1.561* (0.869) |
1.707** (0.861) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
-0.476 (0.889) |
-0.464 (0.931) |
-0.544 (0.933) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
0.210*** (0.0551) |
0.233*** (0.0640) |
0.241*** (0.0650) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
0.669* (0.368) |
0.740* (0.400) |
0.883** (0.375) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
0.00550 (0.0112) |
0.00917 (0.0133) |
0.0128 (0.0146) |
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.483 | 0.479 | 0.482 | 0.467 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | |||
H0: joint validity of moment restrictions | ||||
Sargan test | [0.771] | [0.542] | [0.767] | [0.700] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||
Arellano-Bond for AR(2) | [0.983] | [0.985] | [0.947] | [0.998] |
Number of instruments | 65 | 62 | 62 | 62 |
Number observations | 464 | 464 | 464 | 464 |
Number countries | 29 | 29 | 29 | 29 |
Dependent Variable:
$$ {\Delta \ell }_{it}$$ |
(1) |
(2) DEPOSIT RATE |
(3) LARGE |
(4) restricted sample |
(5) LOAN PROVISIONS |
(6) restricted sample |
---|---|---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.300*** (0.0708) |
0.350*** (0.0709) |
0.350*** (0.106) |
0.247*** (0.0757) |
0.225*** (0.0732) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.265*** (0.0477) |
0.0783 (0.0841) |
0.212** (0.0825) |
0.302*** (0.0533) |
0.306*** (0.0551) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.0607 (0.0633) |
0.407** (0.183) |
-0.301*** (0.0730) |
-0.0339 (0.0594) |
0.0414 (0.0614) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
-0.110 (0.186) |
-0.876*** (0.310) |
-0.648* (0.358) |
-0.135 (0.234) |
-0.256 (0.287) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
0.0790** (0.0317) |
0.0243 (0.0367) |
0.0142 (0.0409) |
0.107*** (0.0347) |
0.102*** (0.0377) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.395** (0.186) |
0.163 (0.190) |
0.204 (0.178) |
0.538*** (0.179) |
0.453** (0.201) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.370 (0.228) |
-0.297 (0.208) |
-0.388*** (0.140) |
-0.506*** (0.116) |
|
DEPOSIT RATE$$ _{it}$$ |
1.280 (0.823) |
|||||
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
-0.0816 (0.859) |
0.155 (1.105) |
-1.972** (0.854) |
0.324 (0.754) |
|
LOAN PROVISIONS$$ _{i,t-1}$$ |
-1.476* (0.819) |
|||||
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
0.471 (0.794) |
-0.330 (0.470) |
0.0128 (0.471) |
1.366** (0.652) |
1.711** (0.840) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
-0.284 (1.250) |
0.634 (0.714) |
1.184 (0.876) |
-0.252 (0.676) |
-0.0590 (0.773) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
0.225*** (0.0463) |
-0.00534 (0.0797) |
0.0745 (0.0960) |
0.228*** (0.0686) |
0.231*** (0.0695) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
0.928*** (0.356) |
-0.178 (0.735) |
-0.599 (0.797) |
0.746*** (0.242) |
0.721** (0.315) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
0.00618 (0.0134) |
-0.0196** (0.00992) |
-0.0221* (0.0132) |
0.0151 (0.0104) |
0.0189 (0.0145) |
LARGE$$ _{i,t-1}$$ |
-0.0645 (0.0585) |
|||||
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.483 | 0.565 | 0.429 | 0.562 | 0.549 | 0.530 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | [0.031] | [0.000] | [0.079] | [0.00002] | [0.000001] |
H0: joint validity of moment restrictions | ||||||
Sargan test | [0.771] | [0.648] | [0.002] | [0.0005] | [0.317] | [0.206] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||||
Arellano-Bond for AR(2) | [0.983] | [0.433] | [0.894] | [0.223] | [0.669] | [0.664] |
Number of instruments | 65 | 65 | 68 | 65 | 65 | 65 |
Number observations | 464 | 443 | 254 | 254 | 354 | 354 |
Number countries | 29 | 29 | 29 | 29 | 29 | 29 |
Dependent Variable: $$ {\Delta \ell }_{it}$$ | (1) | (2) | (3) | (4) |
---|---|---|---|---|
$$ {\Delta\ell}_{i,t-1}$$ |
0.243*** (0.0603) |
0.266*** (0.0629) |
0.254*** (0.0636) |
0.216*** (0.0605) |
$$ {\Delta\ell}_{i,t-2}$$ |
0.241*** (0.0618) |
0.243*** (0.0635) |
0.219*** (0.0724) |
0.253*** (0.0614) |
$$ {\text{ROE}}_{i,t-1}$$ |
0.0598 (0.0651) |
0.0804 (0.0658) |
0.0333 (0.0710) |
0.0694 (0.0684) |
$$ {\text{CAP}}_{i,t-1}$$ |
-0.389* (0.221) |
-0.490** (0.200) |
-0.547* (0.314) |
-0.343 (0.217) |
$$ {\text{CAP}}^2_{i,t-1}$$ |
0.0521 (0.0355) |
0.0608 (0.0388) |
0.0354 (0.0338) |
0.0598** (0.0296) |
$$ {\text{BSL}}_{i,t-1}$$ |
0.0886 (0.185) |
0.140 (0.219) |
0.256 (0.235) |
0.299* (0.182) |
$$ {\text{DEPOSIT COSTS}}_{i,t-1}$$ |
-0.486*** (0.158) |
-0.481*** (0.166) |
-0.402 (0.248) |
-0.266 (0.225) |
$$ {\text{PROVISIONS}}_{i,t-1}$$ |
0.288 (0.678) |
0.362 (0.702) |
-0.0184 (0.624) |
0.498 (0.666) |
$$ {\text{LENDING RATE}}_{it}$$ |
1.521* (0.840) |
1.081 (0.861) |
1.629* (0.890) |
1.317* (0.766) |
$$ {\text{LONG TERM RATE}}_{it}$$ |
-0.405 (0.895) |
-0.187 (0.947) |
-0.892 (0.916) |
-0.626 (0.865) |
$$ {\text{STOCK RETURNS}}_{it}$$ |
0.243*** (0.0602) |
0.280*** (0.0733) |
0.251*** (0.0702) |
0.242*** (0.0482) |
$$ {\text{ASSETS/GDP}}_{i,t-1}$$ |
0.00939 (0.0138) |
0.00526 (0.0145) |
0.00311 (0.0114) |
0.00264 (0.0120) |
$$ {\Delta \text{AGG. DEMAND}}_{it}$$ |
0.723* (0.380) |
0.771* (0.418) |
||
$$ \Delta$$GDP$$ _{it}$$ |
0.340 (0.600) |
|||
$$ \Delta$$CONSUMPTION$$ _{it}$$ |
-0.0997 (1.062) |
|||
$$ \Delta$$INVESTMENT$$ _{it}$$ |
0.236 (0.402) |
|||
$$ \Delta$$GOVERNMENT$$ _{it}$$ |
1.395* (0.785) |
|||
INFLATION$$ _{i,t-1}$$ |
-0.404 (0.287) |
|||
UNEMPLOYMENT$$ _{it}$$ |
-0.398 (0.344) |
|||
$$ {\Delta\ell}_{i,t-1} + {\Delta\ell}_{i,t-2}$$ | 0.483 | 0.509 | 0.474 | 0.469 |
H0: $$ {\text{CAP}}_{i,t-1} = {\text{CAP}}^2_{i,t-1} = 0$$ | [0.029] | [0.003] | [0.093] | [0.011] |
H0: $$ \Delta$$CONSUMPTION$$ _{it} = \Delta$$INVESTMENT$$ _{it} = \Delta$$GOVERNMENT$$ _{it} = 0$$ | [0.028] | |||
H0: joint validity of moment restrictions | ||||
Sargan test | [0.771] | [0.889] | [0.942] | [0.676] |
Hansen test | [1.000] | [1.000] | [1.000] | [1.000] |
H0: residuals are serially uncorrelated | ||||
Arellano-Bond for AR(2) | [0.983] | [0.986] | [0.728] | [0.898] |
Number of instruments | 65 | 65 | 71 | 71 |
Number observations | 464 | 464 | 464 | 462 |
Number countries | 29 | 29 | 29 | 29 |