Keywords: Interest Rates, Forecasting, GDP Growth, Term Premiums, Probit.
Abstract: The slope of the Treasury yield curve has often been cited as a leading economic indicator, with inversion of the curve being thought of as a harbinger of a recession. In this paper, I consider a number of probit models using the yield curve to forecast recessions. Models that use both the level of the federal funds rate and the term spread give better insample fit, and better outofsample predictive performance, than models with the term spread alone. There is some evidence that controlling for a term premium proxy as well may also help. I discuss the implications of the current shape of the yield curve in the light of these results, and report results of some tests for structural stability and an evaluation of outofsample predictive performance.
JEL Classification: C22, E37, E43.
1. Introduction
The slope of the Treasury yield curve has often been cited as a leading economic indicator, with inversion of the curve being thought of as a harbinger of a recession. Of course, growth, recessions, and interest rates are all endogenous and any association among them is purely a reduced form correlation. However, historically, the threemonth less tenyear term spread has exhibited a negative statistical relationship with real GDP growth over subsequent quarters, and a positive statistical relationship with the odds of a recession (see, for example, Estrella and Hardouvelis (1991) and Estrella and Mishkin (1996, 1998) and the references therein). The same is true for other similar measures of the difference between short and longterm interest rates. The term spread is an important part of several indexes of widely followed leading indicators, including that of the Conference Board and the leading index and recession index of Stock and Watson (1989, 1993). The issue is quite topical because the yield curve is currently very flat, and actually modestly inverted between about one and five years.
The simplest theoretical rationale for why term spreads might be a useful leading indicator is that under the expectations hypothesis (neglecting term premiums), the term spread (shortterm rates less longterm yields) measures the difference between current shortterm interest rates and the average of expected future shortterm interest rates over a relatively long horizon. The term spread is thus a measure of the stance of monetary policy (relative to longrun expectations). The higher is the term spread, the more restrictive is current monetary policy, and the more likely is a recession over the subsequent quarters.
Even with this rationale that neglects term premiums, it is not clear that the spread of shortterm interest rates over the yield on a longterm bond should necessarily capture all the information in the yield curve about the likelihood of a recession. There is no fundamental reason why a rise in the level of current shortterm interest rates must have the same predictive content for the likelihood of a recession as a fall in average expected future nominal interest rates over, say, the next ten years. But using the term spread as the sole explanatory variable has precisely this implication.
Moreover, neglecting term premiums seems inappropriate, as it is clear that term premiums exist, and are timevarying, and are typically increasing in the maturity of the bond, complicating the interpretation of spreads between short and longterm Treasury yields. Hamilton and Kim (2002), and Ang, Piazzesi, and Wei (2006) have argued that the term premium and expectations hypothesis components of the term spread have quite different statistical correlations with future growth. This makes sense theoretically; an exogenous decline in the term premium, ceteris paribus, makes financial conditions more accommodative and so stimulates growth while flattening the yield curve. The federal funds rate is a measure of the stance of monetary policy that is less complicated by the effects of term premiums. More generally, the shape of the yield curve contains information about term premiumsin fact this is essentially the source of our ability to predict excess returns on longermaturity bonds.
These considerations motivate asking if there is more information in the shape of the yield curve for future growth prospects than simply considering a term spread, such as the threemonth over tenyear term spread. In this paper, I focus just on predicting recessions, rather than on the closely related question of growth forecasting. I consider a number of probit models for forecasting the binary variable that is one if there is an NBER recession in the subsequent quarters, and zero otherwise. The baseline model uses just the threemonth over tenyear term spread. I then consider augmenting by the level of nominal federal funds rate, and some other yield curve variables including a term premium proxy, similar to the approach that Ang, Piazzesi and Wei (2006) found fruitful in the context of forecasting GDP growth. The probit regressions that include the federal funds rate and the threemonth over tenyear term spread provide better in sample fit, and better outofsample predictive performance, than those regressions using the term spread alone. And, whereas the probit regression using the term spread alone currently predicts quite high odds of a recession, the probit regressions including the level of the federal funds rate do not.
The plan for the remainder of this paper is as follows. The data sources, alternative probit models, and prediction results are described in section 2. Structural stability is tested in section 3. Outofsample predictive performance is in section 4. Section 5 concludes.
2. Recession Prediction Using the Yield Curve: Alternative Probit Models
I consider four alternative models for probit regressions forecasting an NBER recession at some point in the next quarters. The first model, model A, is:
As argued above, the expectations hypothesis and term premium components of the slope of the yield curve may have quite different implications for future growth. Controlling for the level of the federal funds rate is at best an indirect way of accounting for this. Recently, Cochrane and Piazzesi (2005), building on work of Fama and Bliss (1987) and Campbell and Shiller (1991), find that a single linear combination of the term structure of forward rates has substantial predictive power for the excess returns from holding an year bond for one year, over those from holding a oneyear bond (for from 2 to 5). This "return forecasting factor" is a measure of the term premium on longerterm bonds. As a direct way to control for the different implications of the expectations hypothesis and term premium components of the yield curve, I consider using the term spread, the level of the funds rate, and Cochrane and Piazzesi's return forecasting factor as predictors of an NBER recession. This is model D, the specification for which is:
Each of the models is estimated using data from 1964Q1 to 2005Q4. The start date follows Fama and Bliss (1987), Ang, Piazzesi, and Wei (2006) and others. Some researchers have estimated regressions with data back as far as 1952, but data on longterm yields before 1964 may be unreliable because at that time there were very few long maturity bonds that did not have prices distorted by being either callable or "flower bonds" (redeemable at par in payment of estate taxes). The results are shown in Tables 1, 2 and 3 for horizons h=2, 4 and 6 quarters, respectively. The estimation method and construction of standard errors (taking account of the overlapping nature of the forecasts) are described in the appendix.
2.1 Results
Turning to the results, in model A, the coefficient on the threemonth over tenyear term spread is statistically highly significant at all three horizons, reaffirming the underlying historical statistical association. In model B, both the federal funds rate and term spread are highly significant at all horizons. The fit of the regression, judging from the pseudo Rsquared (which does not penalize model size) and the Bayes information criterion (which does penalize model size) is substantially better than using the term spread alone. In model C, where both the nominal and real funds rates are included, the model prefers the nominal funds rate and the real funds rate is not significant at any conventional significance level. In model D, the coefficient on the federal funds rate is once again significantly positive at each horizon. Meanwhile, the coefficient on the return forecasting factor is significantly negative at the six quarter horizon, but is not significant at shorter horizons. Judging from the Bayes Information Criterion, model B (using the term spread and the level of the funds rate alone) is the best fitting model at all horizons. I conclude that models that use both the level of the federal funds rate and the term spread give better insample fit than models with the term spread alone. There is some evidence that controlling for the return forecasting factor (term premium proxy) as well may help further.
2.2 A Few Historical Episodes and Current Implications
Figure 1 shows the fitted probabilities of a recession from models A, B, C, and D at the fourquarterahead horizon. NBER recessions are shown as the shaded regions. All of the models have generally quite good fit, with actual recessions following periods when the fitted probability of a recession was high. However, model A, which does not control for the level of the funds rate, predicted nearly even odds of a recession in 1995 and 1998, but no recession occurred in the subsequent four quarters. The other models, which do control for the level of the funds rate, predicted lower odds of a recession at those dates. Like today, 1995 and 1998 were episodes of flat yield curves where the level of the funds rate was not however especially high (though the funds rate was higher then than it is today). On the other hand, model A gave lower odds of a recession in the runup to the 1990 recession than models that control for the level of the funds rate, and of course a recession did occur. The shape of the yield curve that has historically been the strongest predictor of recessions involves an inverted yield curve with a high level of the funds rate. Model A does not take this into account, while the other models do and these examples illustrate a few cases where that turned out to be right.
Not surprisingly, the models currently however have quite different implications. Model A now puts the odds of a recession in the next four quarters at over 50 percent. Models B, C, and D predict odds of a recession of around 20 percent, which is actually in the range of the unconditional probability of a recession in any fourquarter period. This more optimistic, and arguably more reasonable, prediction is consistent with the odds of a recession reported in the most recent Survey of Professional Forecasters (February 2006).
3. Structural Stability
Some authors have conjectured that the relationship between the yield curve and growth may have changed in recent years. Giacomini and Rossi (2005) and Estrella, Rodrigues, and Schich (2003) find evidence that the predictive power of the yield curve for growth has weakened since the 1980s. The latter paper however also tests for a structural break in the relationship between the term spread and a recession dummy and does not find a significant break.
Given the limited number of recessions in the United States over the last forty years, estimating a model allowing for all of the parameters to shift does not seem appropriate; the models as they stand are already quite richly parameterized. However, Lagrange multiplier tests for parameter stability require estimation of only the restricted model, without parameter breaks. These tests include the test of Nyblom (1989) and the supLM test of Andrews (1993) and are described in a bit more detail in the technical appendix. The structural stability test statistics are reported in Table 4 for models 1, 2, and 3 and horizons 2, 4, and 6. Neither test is significant, even at the 10 percent level, for any model or horizon. Consistent with the results of Estrella, Rodrigues, and Schich (2003), I find no evidence for a structural break in the relationship between different measures of the shape of the yield curve and the binary recession dummy.^{6}Failure to reject a null hypothesis does not of course mean that it is true. Tests can have poor power, and I suspect that with the small number of recessions in this sample, the tests might fail to detect even quite notable parameter instability. The instability in the relationship between the yield curve and output growth underscores this possibility. Nevertheless, I do not have much evidence for timevariation in parameters in the association between yield curve variables and recessions.
4. OutofSample Prediction of Recessions and Expansions
A stringent test of any forecast that guards against the danger of overfitting is to consider pseudooutofsample predictive performance. For each model, and each horizon, I recursively compute predicted recession probabilities in each quarter, beginning with the forecast made in 1980Q1. I then consider the root mean square error of these predictions. That is, if is the fitted probability of a recession between quarter and quarter , estimated using data available at time , then the root mean square prediction error is
5. Conclusions
Consistent with recent work by Ang, Piazzesi, and Wei (2005) on forecasting growth, I have found that there is more information in the shape of the yield curve about the likely odds of a recession than that provided by the term spread alone. Probit models forecasting recessions that use both the level of the federal funds rate and the term spread give better insample fit, and better outofsample predictive performance, than models with the term spread alone. There is some evidence that controlling in addition for Cochrane and Piazzesi's (2005) measure of expected excess returns on longermaturity bonds may also help. The shape of the yield curve that has historically been the strongest predictor of recessions involves an inverted yield curve with a high level of the nominal funds rate. Currently, the yield curve is flat, not owing to a historically high level of the federal funds rate, but rather, to a low level of distanthorizon forward rates due in turn to some combination of low inflation expectations, low expected equilibrium real rates, and/or low term premiums. And a decline in term premiums seems to explain much of the fall in distanthorizon forward rates over the last couple of years, judging from multifactor termstructure models (Kim and Wright (2005)), or simply the comparison of the yield curve with surveyexpectations for shortterm interest rates at distant horizons. While a probit model using the term spread alone predicts high odds of a recession in the next four quarters, the other probit models that I estimate, which all control for the level of the funds rate, do not. This gives formal empirical support to a view that has been widely expressed by commentators that the present flatness of the yield curve is a reflection of low term premiums rather than especially tight monetary policy, and this flatness accordingly does not seem to herald a sharp slowdown.
In this regard, it is noteworthy that Australia, and especially the United Kingdom have had downward sloping yield curves for some time, apparently owing to low term premiums globally and to heavy special demand for longer duration assets from pension funds in the United Kingdom, rather than especially tight monetary policy. Both economies, however, have continued to expand robustly. Further analysis of the correlations between the shape of the yield curve and growth in foreign industrialized countries is an important topic that is left for future research.
This appendix explains some of the econometric methods that I use in this paper for estimating a probit model with standard errors that are robust to serial correlation, and for constructing LM tests for structural stability in the probit model that are also robust to serial correlation. None of this is new, but the methods are not available in canned packages and are described here for completeness.
Each probit model that I estimate is of the form
The general formula for the asymptotic variance of justidentified GMM applies in this context and thus
where
Turning to the tests for parameter constancy, the Nyblom (1989) LM test is given by
Model  A  B  C  D 

Three Month less TenYear Spread  0.60
(3.96) 
0.42
(2.84) 
0.41
(2.64) 
0.62
(1.34) 
Federal Funds Rate  0.24
(3.05) 
0.20
(2.03) 
0.18
(1.06) 

Real Federal Funds Rate  0.10
(0.91) 

Excess Bond Return Forecasting Factor  0.07
(0.48) 

Mc Fadden RSquared  0.22  0.39  0.39  0.39 
Bayes Information Criterion  69.25  58.11  60.25  60.51 
Notes: This table shows the coefficient estimates, Mc Fadden Rsquared and Bayes Information criterion from the maximum likelihood estimation of the probit regressions at a horizon of two quarters. Entries in parentheses are tstatistics, constructed using NeweyWest standard errors. The sample is 1964Q12005Q4, as discussed in the text. The Bayes Information Criterion is where is the maximized loglikelihood, is the number of parameters and is the sample size.
Model  A  B  C  D 

Three Month less TenYear Spread  0.74
(4.31) 
0.76
(4.45) 
0.76
(4.24) 
0.55
(1.18) 
Federal Funds Rate  0.35
(3.46) 
0.36
(2.85) 
0.43
(2.10) 

Real Federal Funds Rate  0.00
(0.02) 

Excess Bond Return Forecasting Factor  0.07
(0.48) 

Mc Fadden RSquared  0.29  0.50  0.50  0.50 
Bayes Information Criterion  73.18  55.74  58.29  58.15 
Notes: As for Table 1, except that the horizon is four quarters.
Model  A  B  C  D 

Three Month less TenYear Spread  0.75
(4.24) 
0.81
(3.73) 
0.84
(3.70) 
0.07
(0.17) 
Federal Funds Rate  0.36
(3.19) 
0.39
(2.86) 
0.66
(3.38) 

Real Federal Funds Rate  0.06
(0.54) 

Excess Bond Return Forecasting Factor  0.29
(2.03) 

Mc Fadden RSquared  0.29  0.48  0.48  0.50 
Bayes Information Criterion  78.48  61.50  63.83  62.03 
Notes: As for Table 1, except that the horizon is six quarters.
Horizon  2 quarters  2 quarters  2 quarters  4 quarters  4 quarters  4 quarters  6 quarters  6 quarters  6 quarters  

Model  A  B  C  D  A  B  C  D  A  B  C  D 
Nyblom  0.23  0.44  0.51  0.49  0.26  0.46  0.55  0.51  0.27  0.42  0.48  0.62 
Andrews  4.91  6.48  7.88  6.42  7.15  6.80  7.97  6.73  5.51  7.44  7.42  7.29 
Notes: This table reports the Andrews and Nyblom Lagrange Multiplier tests for structural stability. None of these tests is significant, even at the 10 percent significance level.
Horizon  2 quarters  2 quarters  2 quarters  4 quarters  4 quarters  4 quarters  6 quarters  6 quarters  6 quarters  

Model  A  B  C  D  A  B  C  D  A  B  C  D 
0.36  0.33  0.37  0.34  0.37  0.34  0.39  0.34  0.38  0.38  0.40  0.37 
Notes: This table reports the root mean square error of the fitted recession probability as a predictor of the binary dummy that is 1 if and only if a recession subsequently occurred over three different subsamples.
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