Abstract:
Both across countries and within countries regulators face the challenging task of finding the appropriate response to the actions of other regulators. This task has informed active research on the gains from monetary policy coordination across countries, as described in detail by Canzoneri & Henderson (1991). Strategic interactions also arise within a country when different regulators are assigned or pursue distinct objectives. For instance, the expansion and reorganization of regulatory responsibilities spurred by the Financial Crisis has been
approached differently across countries. In the United States the Dodd-Frank Act substantially increased the macroprudential responsibilities of the Federal Reserve. In the United Kingdom, the Financial Services Act 2012 established an independent Financial Policy Committee as a subsidiary of the
Bank of England, with some policymakers participating in both the Monetary and the Financial Policy Committee. By contrast, in the euro area monetary policy tasks are strictly separated from macroprudential and supervisory tasks, although both functions involve the European Central Bank. Other
examples include the interaction between fiscal and monetary authorities or games between countries about improving global competitiveness by setting tariffs and taxes across countries.
To facilitate the study of strategic interactions between regulators, we develop a toolbox that characterizes the welfare-maximizing cooperative Ramsey policies under full commitment and open-loop Nash games. The toolbox is designed to extend Dynare, a convenient and popular modeling environment.1 Our work augments the single regulator framework of Lopez-Salido & Levin (2004).2 The general framework for the policy games that we consider distinguishes between two groups of actors: the first group of private agents acts optimally given the (expected) path of the policy instruments; the second group consists of the policymakers, who determine policies taking into account the private sector's response to the implemented policies. Taking as input a set of equilibrium conditions given arbitrary rules for the reactions of the policy instruments, our toolbox replaces those rules with either the welfare-maximizing Ramsey policies or with the policies for the open-loop Nash game.
To showcase the wide applicability of our toolbox, we consider two examples that provide some new results regarding the gains from cooperative policies. The first example is a two-country monetary model that closely follows Clarida et al. (2002), Benigno & Benigno (2006), and Corsetti et al. (2010). These authors characterize the optimal monetary policies under cooperation and open-Loop Nash games between two monetary policy authorities in a dynamic general equilibrium model with sticky prices. If we take a linear approximation to the policymakers' first-order conditions around the optimal deterministic steady state of the model, we confirm that our toolbox produces the same results as the linear-quadratic approach in Benigno & Benigno (2006) and Corsetti et al. (2010). A key advantage of our toolbox is the automation of the analytical derivation of the cooperative and open-loop Nash policies, once the actions of the private agents are characterized. We replicate key insights from Benigno & Benigno (2006) and Corsetti et al. (2010) and extend their results by considering alternative policy instruments. Beyond the replication of existing results, we show that the choice of inflation measure that is used as an instrument for monetary policy can imply quantitatively important differences for the gains from cooperation. Experimentation with alternative policy instruments is seldom attempted with a linear-quadratic approach as it entails tedious and error-prone analytical manipulations, but comes at no cost with our toolbox.
The second example considers the workhorse New Keynesian model with financial frictions of Gertler & Karadi (2011). An agency problem on financial intermediaries has two important effects. First, the problem inefficiently limits the provision of credit. Second, the agency problem also magnifies the reaction of the economy to shocks through familiar financial accelerator mechanisms. We extend the model of Gertler & Karadi (2011) to include a transfer tax between households and firms. Within that model, we consider a game between a financial regulator and a monetary policy authority. The policy instrument of the central bank is the inflation rate; the policy instrument of the financial regulator is the transfer tax. The objectives of the two regulators reflect the preferences of households, but in both cases include an extra term. The central bank has an objective biased towards stabilizing inflation. The financial regulator has an objective biased towards stabilizing the provision of credit. We characterize optimal cooperative Ramsey and open-loop Nash policies. We constrain the choice of biases so that the cooperative policies with the skewed objectives come close to replicating the allocations under policies that maximize the welfare of the representative household. Nonetheless, the strategic interaction between regulators lead to large and persistent deviations from cooperative outcomes and imply substantial welfare losses.
The usefulness of our toolbox is not limited to solving the particular examples above. Following the approach in Dixit & Lambertini (2003), differences in objectives are fertile ground to explore the strategic interactions between policymakers. For instance, the solution under coordinated optimal monetary and fiscal policies explored in Schmitt-Grohe & Uribe (2004) could be readily extended for strategic interactions after allowing for small differences in the objectives of the monetary and fiscal authorities. More recent examples of stylized models that set the stage for strategic interactions between policymakers include Costinot et al. (2014), who illustrate the use of capital controls to manipulate the terms of trade and Brunnermeier & Sannikov (2014), who show how capital controls may improve welfare in a model with financial frictions (but who do not consider a non-cooperative solution). Furthermore, our toolbox greatly facilitates the analysis of more fully articulated models. Examples include Bergin & Corsetti (2013), who introduce firm entry into a two-country model to study how the resulting production relocation externality influences monetary policy, and Fujiwara & Teranishi (2013), who allow for nominal rigidities in loan contracts. Finally, the optimal policy implications for models with numerous empirically relevant features (such as consumption habits, capital accumulation, investment adjustment costs, incomplete financial markets, sticky wages) as in the two-country model of Coenen et al. (2007) can also be analyzed and extended with the help of our toolbox.
The rest of the paper is organized as follows. Section 2 outlines the algorithm for calculating cooperative optimal policy and extends the algorithm to the calculation of optimal policies in open-loop Nash games. Section 3 applies the algorithm to an open-economy model where each country wishes to maximize welfare through controlling inflation, and Section 4 considers the application of our algorithm to a model with a monetary authority and a macroprudential regulator. Section 5 concludes. An Appendix with details on the toolbox is provided.
This section covers three topics: 1) it defines an equilibrium under cooperative Ramsey policies; 2) it defines an equilibrium under an open-loop Nash game; and 3) it spells out the relationship between our solution approach and the linear-quadratic approach.
In maximizing the policy objectives subject to the structural equations of the private sector our toolbox employs a Lagrangian approach. The exact nonlinear first-order conditions that characterize the optimal policies under cooperation and the open-loop Nash game, respectively, are obtained by symbolic differentiation. Each system of equations is then approximated around its deterministic steady state using higher order perturbation methods. An alternative approach to characterizing optimal policies uses linear-quadratic (LQ) techniques. The LQ approach involves finding a purely quadratic approximation of each policymakers' objective function which is then optimized subject to a linear approximation of the structural equations of the model. Following Benigno & Woodford (2012), Levine et al. (2008) and Debortoli & Nunes (2006) we show how the LQ approach relates to the approach underlying our numerical procedure and that the LQ approach delivers the same solution if the nonlinear output of our toolbox is approximated to the first order.
Policy games distinguish between two groups of actors. We label the first group "private agents." Private agents act optimally given the (expected) path of the policy instruments. The second group consists of the policymakers who determine policies taking into account the private sector's response to the implemented policies. With more than one policymaker, strategic interaction between the policymakers can cause the outcomes of the dynamic game to deviate from the welfare-maximizing cooperative policy. For simplicity, we restrict the exposition to the case of two policymakers (or players). Furthermore, each policymaker is assumed to have only one instrument.
Let the vector of endogenous variables be denoted by
, which is
partitioned as
. The variable
is the policy
instrument of player
, respectively. The exogenous variables are captured by the vector
. For given sequences of the policy instruments
, the remaining
endogenous variables need to
satisfy the
structural conditions that characterize an equilibrium
To complete our framework, we need to describe how policies are determined. The intertemporal preferences of player are given by
with the generic utility function
required to be concave. Under cooperation, the two players maximise the joint welfare function
for given weights
and
. We normalise the welfare weights to satisfy
=1. Absent cooperation, each policymaker considers his own preferences only.
The welfare-maximizing Ramsey policy with full commitment is derived from the maximization program
The first-order conditions for this problem can be obtained by differentiating the Lagrangian problem of the form
Taking derivatives of
with respect to the
endogenous variables in
delivers
first order conditions. Additionally, taking derivatives with respect to
delivers again the
private sector conditions. In total, there are
conditions and
variables. Since the generic instruments
and
are added to the model equations through definitions of the form
where
is player
's actual policy instrument, taking derivatives with
respect of
and
returns the Lagrange multipliers associated with
these definitions. Here, we assume that
is the Lagrange multiplier attached to the definition of player
's
instrument. In sum, the Ramsey equilibrium process
satisfies
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Equations (4) and (7) can now be replaced by a local approximation around the optimal steady state
of desired order. The resulting system of (higher-order) difference equations can easily be solved by standard algorithms.
To define an open-loop Nash equilibrium, let
denote the sequence of policy choices by player
before and after, but not including period
. An open-loop Nash equilibrium is a sequence
with the property that for all
,
maximises player
objective function subject to the structural
equations of the economy for given sequences
and
, where
denotes the sequence of policy moves by all players other than player
. Each player's action is the best response to the other players' best responses.
With policymakers needing to specify a complete contingent plan at time 0 for their respective instrument variable
for
, under the open-loop equilibrium concept,
the problem can be reinterpreted as a static game allowing us to recast each player's optimization problem as an optimal control problem given the policies of the remaining players. As under the static Nash equilibrium concept, player
restricts attention to his own objective function and the maximisation program is given by
Notice that the full set of equations includes the
structural equations
twice. Since in equilibrium all players face the same values of the non-policy variables
, an interior Nash equilibrium
satisfies the following
conditions for
Adopting the timeless perspective is again key to obtaining time-invariant decision rules. The optimal response of each player given the policies of the other player derived from the optimal control problem at time 0 is not necessarily time consistent. Last, the
deterministic steady state is found as for the cooperative case by exploiting the linearity of the system (14)-(18) in the Lagrange
multipliers.
An alternative approach to solve optimal policy problems uses linear-quadratic (LQ) techniques. In the case of a single decision maker, the LQ approach involves finding a purely quadratic approximation of the policymaker's objective function which is then optimized subject to a linear approximation of the structural equations of the model.Benigno & Woodford (2012) and Levine et al. (2008) and Debortoli & Nunes (2006) discuss necessary and sufficient conditions for a "correct LQ approximation" to the optimization problem stated in equation (2) to exist. In contrast to the early literature the approach followed here does not require the steady state of the model to be efficient.4
To see the connection between the LQ approach and the approach followed in our toolbox, assume we were interested in the solution to the problem stated in (2) obtained from the linear approximation of the first order conditions (4) to
(7) around the optimal steady state. Under the timeless perspective, the first order conditions with respect to the endogenous variables can then be approximated by
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We first illustrate our toolbox for a two-country monetary model that closely follows Benigno & Benigno (2006) and Corsetti et al. (2010). These authors characterize the optimal monetary policies both with and without cooperation between two central banks in dynamic general equilibrium models with sticky prices. To this end, they derive the true linear quadratic approximation of the model. As discussed in Section 2.4, for given choice of policy instruments and strategies of the players, the linear-quadratic approach delivers the same output as our toolbox if we take a linear approximation of the first-order conditions of the two central banks around the deterministic steady state.
The two countries are equal in size and symmetric in their economic structure. We only describe the economy of country 1 in detail.
Following Benigno & Benigno (2006) and Corsetti et al. (2010) each country is populated by a continuum of households. Each of them engages in the production of a specific good for which the household uses its own labor as the sole
input. The good produced by household carries the index
. Before describing
the production and pricing of goods in detail, we first set up the household's optimization problem for given labor and production choices,
and
with financial markets being complete at the domestic and the international level
Consumption utility is derived from consuming a domestic good,
, and a foreign good,
, according to
Competitive producers of the domestic good, , aggregate a variety of intermediate goods,
, produced by the home country's households using the production technology
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(24) |
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(25) |
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(26) |
Each household produces exactly one variety and engages in monopolistic competition with all other households. A household chooses its price so as to maximize its utility.
Following Calvo (1983) the probability of adjusting prices in a given period is
.
Assuming household uses a linear technology to produce good
, it
is
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(27) |
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(28) |
Aggregating over households, market-clearing for the domestic good requires
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(29) |
Bonds are in zero net-supply, requiring
and
. Finally, the budget constraint of the government is balanced in every period by adjusting lump-sum taxes,
, to the stochastic government purchases,
. The share of government consumption in output,
, evolves according to
Appendix B displays the set of structural equations associated with the model in (22)-(30) that characterize the private sector equilibrium conditions. Using the notation introduced in Section 2.1, the endogenous variables are collected in the vector
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(31) |
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(32) |
For illustration, we assume as in Benigno & Benigno (2006) that the policymakers use producer price inflation rates and
as instruments.5 Augmenting the
set of conditions (67)-(91) in Appendix B by the two definitions
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The parameterization of the model is provided in Table 1. The choices are comparable to those in Benigno & Benigno (2006) and Corsetti et al. (2010). Most notably, by setting the coefficient governing
the intertemporal elasticity of substitution equal to 2 and fixing the
elasticity of substitution between traded goods at 2 , the home and foreign good are substitutes in the utility function the household. Steady-state imports are about 15% of GDP, which
reflects home-biased preferences, given that the two countries are equal in size and symmetric. Accordingly, the countries are equally weighted in the global welfare function.
The model results are well-known in the literature and provide a benchmark to assess the output of our toolbox. Below we review key insights from Benigno & Benigno (2006) and Corsetti et al. (2010). All of these insights are matched by the output of our toolbox.
In the face of technology shocks the welfare-maximising policy under cooperation replicates the flexible price allocations for the two-country model laid out above. As in closed economy models, the "divine coincidence" applies for "efficient shocks" - see Blanchard & Galí (2007): technology shocks move quantities and prices in the same direction relative to the flexible price economy and the central bank does not face a trade-off between inflation and output gap stabilisation.
A different picture emerges when the economy experiences a markup or cost-push shock, i.e., an "inefficient disturbance." As is the case in a closed economy model, the cooperating policymakers cannot perfectly stabilise the economy. In response to a positive cost-push shock, the output gap turns negative, whereas inflation is positive.
If policymakers do not cooperate across borders, prices and quantities will in general differ from those under cooperation. Each country has the ability to influence the terms of trade through its monetary policy stance. Hence, except for specific parameter choices, the (open-loop) Nash equilibrium does not replicate the flexible-price allocations even for efficient shocks.
Figures 1 and 2 show the responses to a positive technology shock and a cost-push shock under the welfare-maximizing cooperative policy and under an open-loop Nash game. Figure 1 shows that the responses to a technology shock under the two policies are quite close. However, there are some notable differences. As in Benigno & Benigno (2006) and Corsetti et al. (2010), output price inflation is perfectly stabilized under the cooperative policy and the output response coincides with its counterpart in a flexible price model (not shown) for both countries. In the open-loop Nash game, inflation and output gaps are not perfectly stabilized. Yet the differences are minor as commonly seen in the literature.
Under the cost-push shock, shown in Figure 2, the two policies differ both qualitatively and quantitatively. Neither policy completely stabilizes output price inflation and the output gaps.6 As shown in Corsetti et al. (2010), for our parameterization the home country's real exchange rate appreciates and its terms of trade improve by more under the open-loop Nash policies than under the cooperative policy. Furthermore, the spillover effects are larger.
To assess the reliability of the results produced by our toolbox, we confirm that its output under a first-order approximation coincides with the results produced by the linear-quadratic approaches in Benigno & Benigno (2006) and Corsetti et al. (2010).7
Exploiting the flexibility of our toolbox, we can easily analyze how the choice of strategy space impacts the outcomes of the open-loop Nash game. To this end we compare the baseline case, in which each country uses producer price inflation as its policy instrument, to a case in which both policymakers use consumer price inflation as the instrument. We focus on the open-loop Nash game, as the choice of instrument does not affect the outcomes under the cooperative policy in this model.
Figure 3 compares the impulse responses to a cost push shock for the open-loop Nash games under the alternative choice of instruments. Strikingly, the differences in outcomes implied by the two instruments in the games are even greater than the differences between the cooperative and open-loop Nash outcomes in Figure 2. This might not be too surprising. The optimal cooperative policy comes close to stabilizing domestic price inflation in the face of a domestic mark-up shock, but the use of consumption price inflation as the instrument implies more dramatic exchange rate movements and and larger spillover effects. The domestic policymaker does not internalize the reverberation of his actions onto the objective of the foreign policymaker. Accordingly, competitive interactions between the domestic and foreign policymakers become stronger as, in turn, the foreign policymaker reacts to the spillover effects with a blunt instrument.
As a summary statistic, the gains from cooperation are a modest 0.003% of consumption when domestic price inflation is the instrument, and a much more sizable 0.7% when consumption price inflation is the instrument.
Our toolbox can also be applied to policy games in a closed economy. We lay out a policy game between a central bank and a financial regulator in a model following Gertler & Karadi (2011). In addition to nominal rigidities, the economy features financial frictions. Non-financial firms are prevented from issuing equity to households directly, but have to go through financial intermediaries, referred to as "banks," in order to raise funds. Due to an agency problem, however, banks are limited in their ability to attract deposits and issue credit to non-financial firms. Accordingly, credit is under-supplied, and the reactions to shocks are amplified by the familiar financial-accelerator mechanism.
The representative household consists of a continuum of members. A fraction of its members supplies labor to firms and returns the wage earned to the household. The remaining fraction
works as bankers. The household utility function is
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(36) |
Financially constrained bankers have an incentive to retain earnings. To prevent the financial constraint from becoming irrelevant by the retention of bank earnings, a banker ceases operations next period with the i.i.d. probability . Upon exiting, bankers transfer retained earnings to the households and become workers. Each period
workers are selected to become bankers. These new bankers receive a startup transfer from the family. By construction, the fraction of household members in each
group is constant over time.
is net funds transferred to the household from its banker members; that is, funds transferred from existing bankers minus the funds transferred to new
bankers (measured by
). See Appendix C for details.
Bank takes in deposits,
, from households and invests into
non-financial firms through an equity contract. Continuing banks do not consume but accumulate all earnings. Due to taxes/subsidies on equity, the bank operates with the amount
, where
is the tax rate and
is the equity of bank
. Since assets equal liabilities on the bank balance sheet
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(40) |
Absent financial frictions, the bank expands its balance sheet when the expected discounted excess return on loans,
, is positive. To limit the ability of banks to attract deposits, Gertler & Karadi (2011)
introduce the following agency problem. At the beginning of each period, a banker can choose to transfer a fraction
of assets to his household. If the banker makes this transfer,
depositors will force the bank into bankruptcy and recover the remaining fraction
of assets. Thus, households will deposit funds with bank
only if the expected terminal wealth,
exceeds the fraction of assets that can be diverted,
, in period
As shown in Appendix C a bank's ability to attract deposits is directly related to its net worth. At the aggregate level this relationship is shown to obey
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(43) | ||
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(44) |
The representative firm uses capital and labor to produce its output
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(46) |
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(47) |
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(48) |
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(49) |
The zero profit condition implies that the return on shares is given by
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(50) |
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(51) |
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(52) |
To support an environment with nominal price rigidities, we introduce an intermediate layer of firms between producing-firms and firms that assemble the final goods. Each intermediate firm acquires the product of a producing firm and applies a stamp to it that differentiates it from those of
others. In choosing the optimal resale price an intermediate firm faces adjustment costs as in Rotemberg (1982)
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(53) |
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(54) |
Physical capital accumulates according to
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(55) |
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(56) |
The aggregate resource constraint requires
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(58) |
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(59) |
Appendix C displays the set of structural equations associated with the model in (35)-(57) that characterize the private sector equilibrium conditions. Using the notation introduced in Section 2.1, the endogenous variables are collected in the vector
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(60) |
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(61) |
In the following, the central bank uses inflation, , as instrument whereas the financial regulator uses the tax on bank capital,
.8By augmenting the set of conditions (67)-(91) in Appendix C with the two definitions
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Table 2 summarises the parameter choices for the subsequent experiments. Most parameters are set at values commonly found in the literature. The parameter in the adjustment cost function for prices is set at 1281 . With this value in place the (linearized) Phillips curve features the same slope as that of a model with
Calvo contracts and an expected contract duration of one year. Inflation is set to zero in the steady state and the subsidy to the intermediate goods producers is set to remove monopolistic distortions in the steady state. The parameters governing the banking sector mimic those in Gertler & Karadi (2011). The survival probability for banks is set at 0.95 implying an average horizon of bankers of ten years. The steady-state ratio of loans
to equity is set equal to 4 . For ease of exposition, we abstract from steady-state distortions by setting the interest rate spread between loans and deposits (
) equal to zero.9 These choices imply that the resource transfer to new
banks as a fraction of total loans,
, is 0.0101 and the portion of net worth that the bank management
can divert,
, is 0.25 .
When setting up the policy problem under cooperation, the objectives of the individual policymakers receive equal weight in the joint objective function, i.e.,
. Positive values of the parameters
and
introduce biases into the objective functions of the central bank and the macroprudential regulator as described below.
Figure 4 shows the responses to a contraction in technology under alternative policies. The shock considered brings down technology by 1 percent in the first quarter. Subsequently, technology follows its auto-regressive process.
We first consider the cooperative policy between the two regulators that maximize the utility of the representative household defined in equation (35). The solid lines in Figure 4 denote the responses for this case. The instruments are so
powerful that, for a technology shock, the policymakers replicate the allocations that obtain in the frictionless real business cycle model. Due to the financial friction, absent intervention from the financial regulator, banks are undercapitalized after the contractionary technology shock. An
infusion of cash into the banks (i.e., a negative bank transfer ) can prop up the equity position,
, and expand lending next period. At the same time, nominal rigidities call for a slight increase in the policy interest rate to prevent inflation from rising inefficiently. Notice that the welfare-maximizing cooperative policy completely stabilizes the
expected spread between the bank return on investment and its cost of funding (the loan rate
minus the deposit
) in the next period and in all future
periods. The same policy also achieves full inflation stabilization.
With identical objectives for the two regulators, the open-loop Nash and cooperative policies coincide. However, in practice, different regulators are assigned or pursue different objectives. We assume objectives for the two regulators that are biased versions of the preferences of the representative agent. Moreover, we restrict attention to a particular formulation of biased objectives that, under cooperative policies, yields minor differences relative to the welfare-maximizing policies (as quantified below). Accordingly, the objective of the monetary policy regulator is biased towards inflation stabilization
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(64) |
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(65) |
As can be seen from Figure 4 the differences between the cooperative policies with biased and unbiased objectives are relatively minor. The bias implies that the macroprudential regulator is overzealous in stabilizing the interest rate spread for banks when the shock occurs. Conversely, the monetary policy regulator accepts small deviations from full stabilization of inflation. Similarly, all other allocations remain close to their counterparts under the welfare-maximizing cooperative policies with biased objectives.
By contrast, an open-loop Nash game with the same biased objectives yields outcomes that are drastically different. To understand the extent of these differences, consider the side effects of a policy that, in reaction to a decline in technology, pushes up the equity position of banks. Higher equity positions allow banks to expand credit and push up investment and aggregate demand. In the presence of nominal rigidities, this expansion in demand leads to higher resource utilization and higher marginal costs of production, which spill cause inflation to rise. In reaction to the same decline in technology, monetary policy will want to curb the inflationary effects of the shocks and increase policy rates. However, higher policy rates bring up the cost of funding for banks and by reducing profitability ultimately reduce the amount of funds available to support lending.
Accordingly, as the macroprudential regulator recognizes that the monetary policy regulator will move to push up rates, he counteracts that action by pushing up the transfer from households to banks (shown as a negative movement in Figure 4). In turn, the monetary policy regulator will have an incentive to increase policy interest rates by more, realizing that the macroprudential regulator will step up the recapitalization of banks. Effectively, the different biases in the objectives push each regulator to discount the reverberations of his own actions onto the objectives of the other regulator. Ultimately, as shown in Figure 4, the strategic interactions lead to an excessive recapitalization of banks, unnecessarily aggressive tightening in monetary policy, and stark deviations from the allocations under the welfare-maximizing cooperative policies and substantial welfare losses.
The top panel of Figure 5 confirms that the welfare losses from adopting biased objectives are small for cooperative policies for a broad range of the parameters that govern the biases. By contrast, the bottom panel of the figure shows that the welfare gains from cooperative policies increase substantially with the bias towards spread stabilization. With biased objectives, the welfare costs of open-loop Nash policies relative to the welfare maximizing policies can be orders of magnitude higher than the losses from allowing for biased objectives under cooperative policies relative to the case of unbiased objectives. Notice also that these welfare costs are orders of magnitudes larger than the welfare costs of business cycles reported in Lucas (2003).
Our results point to two implications for the design of institutional arrangements. Firstly, bringing different regulatory functions under the same institution fosters the recognition of alternative objectives and avoids potentially large welfare losses from strategic interaction. When this solution is politically not feasible, our results argue for devising broader objectives for each regulator as way to minimize the welfare-reducing impact of strategic behavior.
Studying strategic interaction between policymakers has a long tradition in macroeconomics. However, obtaining the relevant first order conditions that characterize the problem under consideration can be complicated. A popular approach is to solve the problem using linear-quadratic (LQ) techniques. Purely quadratic objective functions are derived for each policymaker; the first order conditions of the problem are then obtained by optimizing the quadratic objectives subject to linear approximations of the structural economic relationships. Unfortunately, this approach becomes laborious and potentially error-prone for larger models.
A more direct approach is to obtain the first order conditions by using the nonlinear structural equations of the model and the nonlinear objective functions assigned to the policymakers. Our toolbox fully automates this procedure using symbolic differentiation. The quadratic approximations to the policymakers' objective functions can in principle be retrieved from the output of our toolbox. Any changes to an existing model such as allowing for cooperation between policymakers instead of playing out an open-loop Nash game or changing the policy instruments assigned to the policymakers imply a new set of first order conditions that is easily generated by our toolbox.
We apply the toolbox introduced in this paper to the well-known case of monetary policy coordination in a two-country model and replicate the features highlighted in the literature. Both the optimal monetary policies with and without coordination are characterized with the help of impulse response functions and we show how the choice of policy instruments influences profoundly the outcomes of an open-loop Nash game. We also apply the toolbox to address strategic interaction between a macroprudential regulator and a central bank in the a model with financial friction. The analysis points to potentially large welfare losses stemming from the lack of coordination between policymakers even if technology shocks are the only source of fluctuations.
Parameter | Used to Determine | Parameter | Used to Determine |
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discount factor |
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intertemporal consumption elasticity |
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labor supply elasticity |
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steady-state labor supply to fix ![]() |
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trade subst. elasticity |
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home bias in consumption |
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Calvo price parameter |
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subst. elasticity of varieties |
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steady-state subsidy to producers |
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steady-state inflation |
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persistence of tech. shock |
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std. of tech. shock |
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persistence of cost push shock |
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std. of cost push shock |
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persistence of gov. spending shock |
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std. of gov. spending shock |
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share of gov. spending |
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|
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weight on home country in Ramsey |
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weight on foreign country in Ramsey |
Parameter | Used to Determine | Parameter | Used to Determine |
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discount factor |
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consumption habits |
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labor supply elasticity |
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steady-state labor supply to fix ![]() |
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share of capital in production |
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capital depreciation rate |
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subst. elasticity of varieties |
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subsidy to producers |
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price adjustment cost |
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steady-state inflation |
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investment adjustment cost |
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share of gov. spending |
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persistence of tech. shock |
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std. of tech. shock |
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weight of fin. reg. in Ramsey |
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weight of non. pol. in Ramsey |
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add. term in fin. reg. utility |
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add. term in mon. pol. utility |
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steady-state ratio loans to net worth |
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steady-state interest rate spread |
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probability of bank survival |
Parameter | Used to Determine | Parameter | Used to Determine |
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diversion parameter |
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resource transfer to new banks |
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shift parameter in utility function |
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The toolbox includes additional programs that may be of use to researchers interested in comparing the effects of shocks across models:
The replication codes for Figures 1 to 3 are stored in the folder BBCDL_model. The codes for Figures 4 and 5 are provided in the folder GK_model.
BBDCLmodelcomp.mod is the Dynare file containing the original model described in equations (67) to (91) with variables to be log-linearized where appropriate, i.e., the variables are surrounded by the expression exp(). This model file is ready for being processed by our toolbox. In particular, notice
The model file is accompanied by three user-provided Matlab m-files
All relevant files for the Ramsey and the open-loop Nash problem are created by calling convertmodfiles via CREATE_RAMSEY_AND_NASH in the folder BBCDL_model. The first line in this script augments the Matlab path to include our toolbox. Output price inflation is denoted by c1pid and c2pid for countries 1 and 2 , respectively. Consumer price inflation is labeled c1dcore and c2dcore. The files associated with any specific model carry the instrument labels in the file name.
For example, the files needed to compute the solution to the Nash problem using output price inflation as instruments are
Notice, that our toolbox assigns the default values
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The script BBCDLfigure1 generates the impulse responses shown in Figures 1 and 2 and BBCDLfigure2 generates Figure 3. The model names are set under the string variables
stem and modnam1 and modnam2. The variable nperiods fixes the number of periods for the impulse response functions. titlelist fixes the subplot titles, ylabels sets the labels for the y-axis. The desired shocks for computing impulse responses
are set in shocknamevector. Finally, the variables to be plotted are picked in line_ramsey and line_nash, respectively.
The function makeirfsecondorder computes the impulse responses implementing pruning. The final argument in this function fixes the order of approximation (first or second
order).
Finally, the folder LQ_BBCDL_model contains the model described in Appendix B.3. The file call_LQBBCDL computes the impulse responses to a cost push shock for the linear quadratic model stored in LQBBCDL.mod and compares them to those derived from the toolbox output BBCDLmodelcomp_ramsey_c1pid_c2pid.mod.
rbcb_monprud.mod is the Dynare file containing the original model with biased objectives described in equations (107) to (132).15 This model file is ready for being processed by our toolbox. In particular, notice
The model file is accompanied by three user-provided Matlab m-files
All relevant files for the Ramsey and the open-loop Nash problem are created by calling convertmodfiles via CREATE_RAMSEY_AND_NASH located in the folder GK_model. The first line in this script augments the Matlab path to include our toolbox. Inflation is denoted by infl and the bank transfer by bt. The files associated with any specific model carry the instrument labels in the file name.
For example, the files needed to compute the solution to the Nash problem using output price inflation as instruments are
Notice, that our toolbox assigns the default values
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The script GKfigure1 generates Figure 4. The model names are set under the string variables stem and modnam1 and modnam2. The variable nperiods fixes the number of periods for the impulse response functions. titlelist
fixes the subplot titles, ylabels sets the labels for the y-axis. The desired shocks for computing impulse responses are set in shocknamevector. Finally, the variables to be plotted are picked in line_ramsey and line_nash, respectively.
The function makeirfsecondorder computes the impulse responses implementing pruning. The final argument in this function fixes the order of approximation (first or second
order).
Figure 5 is generated by calling the script GKfigure2. The welfare gains from cooperation are expressed as the percent increase in consumption needed under the open-loop Nash game to make households equally well-off as they are under the Ramsey outcomes. The means of the welfare variables is computed by simulating each economy for a large number of periods using the Dynare command stoch_simul with order=2, and invoking pruning.
Changes in the value of the bias parameters and
are
communicated through the global variables overwrite_param_names and overwrite. Overwriting the parameters set in the original parameter files occurs the respective steady-state files.
Finally, some last words are in place when regenerating the model files by passing rbcb_monprud.mod through our toolbox. The default number of simulation periods in stoch_simul is set to zero. Furthermore, the block defining the variance of the innovations is commented out. To run stochastic simulations using GKfigure2 these default feature need to be adjusted appropriately.
To preserve the option of passing parameter values through the global variables overwrite_param_names and overwrite, the steady-state files created by the toolbox have to be edited manually following the template in rbcb_monprud_steadystate.16
Under complete financial markets, the endogenous variables are summarized in the vector
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(66) |
The following equations result from the households' optimization problems:
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(68) |
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(70) |
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(71) |
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(72) |
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(73) |
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(74) |
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(75) |
Profit maximisation by the intermediaries implies the following set of conditions:
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(76) |
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(77) |
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is the steady-state (gross) inflation rate
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(78) |
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(79) |
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(80) |
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(81) |
The goods market clearing conditions are:
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(82) |
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(83) |
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(84) |
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(85) |
The period utility functions are:
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(86) |
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(88) |
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(89) |
We briefly describe the additional equations if consumer price inflation is used as instruments. Using consumer price inflation,
as the policy instrument, we need to define consumer price inflation by relating the relative price of consumption
to producer price inflation:
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(90) |
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(91) |
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(92) |
Corsetti et al. (2010) deviate from the setup in Benigno & Benigno (2006) by allowing for home bias, but by eliminating government spending. In the following, we allow for home bias, abstract form government spending, and focus on
the case of the efficient steady state in order to restate the model presented in Corsetti et al. (2010) using our notation. Absent home bias (
), this model coincides with the one in Benigno & Benigno (2006) for equally-sized countries.
The set of relevant structural relationships of the economy can be reduced to the following set of equations if the model is (log-)linearised around its deterministic steady state
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Relative consumption and the real exchange rate gaps are determined as
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By taking the true linear-quadratic approximation to the utility function, Corsetti et al. (2010) show that the loss function under symmetry is given by
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(96) |
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(97) |
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(98) |
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(99) |
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(100) |
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(101) |
The endogenous variables are summarized in the vector
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(102) |
We provide a complete list of the conditions characterising the private sector equilibrium for given policies for the model described in the main text. At the end of this appendix we will also provide the derivations for equations (42) to (45).
The following equations result from the households' optimization problem:
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(104) |
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(105) |
The following equations result from the banks:
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(106) |
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(107) |
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The following equations result from the basic producers:
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(108) |
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(109) |
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(110) |
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(111) |
The following equations result from the variety producers:
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(112) |
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(113) |
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(114) |
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(115) |
The following equations result from the physical capital producers:
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(116) |
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(117) |
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(118) |
The aggregate resource constraint requires
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(119) |
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(120) |
In addition, we define:
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(121) |
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(122) |
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(123) |
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(124) |
The period utility functions are
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(125) |
The policy rules followed by the central bank and the macroprudential regulator that will subsequently be replaced by the first order conditions of the policymakers are:
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(127) |
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(128) |
We begin by restating the expected terminal wealth of a bank as
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(129) |
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(130) |
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(131) |
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(135) |
In oder to aggregate over banks, we make use of the fact that all banks have access to the same investment opportunities as we will show now.
will be equalized across surviving firms, and similarly for
. Substitute
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(136) |
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(137) |
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(138) |
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(139) |
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(140) |
Finally, aggregate net worth is the sum of the net worth of two groups: old and new bankers. Bankers that survive from period to period
will have aggregate net worth equal to
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(143) |
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(144) |
Current aggregate net worth is then the sum of net worth carried from the previous period by surviving firms plus the net worth of new entrants, or
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(145) |