# Financial predictors of real activity and the financial accelerator B

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3 4. Long-horizon regressions A. Mody, M.P. Taylor / Economics Letters 82 (2004) The dependent variable in the basic regressions is the annualized cumulative percentage change in real GDP: j k y tþk u 400 k ðy tþk y t Þ ð1þ where k denotes the forecasting horizon in quarters and y t is the logarithm of the level of real GDP. The k-period change in the logarithm of GDP, is multiplied by (400/k) to ensure that the percentage growth rate is expressed in annualized terms. The slope of the nominal yield curve is measured by the difference between the yield on 10-year US government bonds (R t ) and the 3-month US Treasury bill rate (r t ), while the high yield spread is measured as the difference between the Merrill Lynch high yield bond index ( Q t ) and the 10-year government bond yield. The basic regression equations are therefore of the form: j k y tþk ¼ a k þ b k ðr t r t Þþg tþk ; ð2þ for the term spread regressions, and j k y tþk ¼ c k þ d k ðq t R t Þþe tþk ; ð3þ for the high yield spread regressions, where g t + k and e t + k are the forecast errors. The possibility of moving average errors in overlapping forecast errors was allowed for by using a method-of-moments correction to the estimated covariance matrix Term spread regressions The results of estimating Eq. (2) for forecast horizons up to 20 quarters are given in Table 1 for the sample period 1975Q1 1987Q4. They are in line with previous empirical evidence in that the estimated slope coefficient is significantly different from zero at the 5% level or better at every horizon up to 16 quarters, and the R 2 statistics are impressive, with as much as 60% of the variation in the cumulative growth in GDP explained. On the other hand, the estimation results for the more recent period, 1988Q1 2001Q4, shown in Table 2, reveal the apparently complete breakdown in this relationship: the estimated slope coefficients are in every case insignificantly different from zero at the 5% level and the R 2 statistics are, in every case, extremely low High yield spread regressions The estimates of the long-horizon regressions, Eq. (3), are given in Table 3. The predictive content of the high yield spread is quite striking. In every case except the longest horizon of 5 years, the estimated

4 170 A. Mody, M.P. Taylor / Economics Letters 82 (2004) Table 1 The term spread as a predictor of real activity, 1975Q1 1987Q4 Forecast horizon k b k R 2 S.E. (%) (2.966) (4.845) (5.968) (6.471) (6.436) (6.579) (6.801) (7.562) (9.326) (3.992) (2.424) (1.680) (1.912) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions. slope coefficient is strongly significantly different from zero at the 5% level or lower. In terms of goodness-of-fit, the high yield spread seems to have strongest predictive content at around three to four quarters ahead, although in terms of the significance of the coefficients, it seems to predict well from horizons as short as one quarter to horizons as long as 18 quarters. Table 2 The term spread as a predictor of real activity, 1988Q1 2001Q4 Forecast horizon k b k R 2 S.E. (%) (0.655) (1.118) (1.154) (1.007) (0.824) (0.744) (0.649) (0.614) (0.610) (0.732) (1.140) (1.326) (1.335) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions.

5 A. Mody, M.P. Taylor / Economics Letters 82 (2004) Table 3 The high yield spread as a predictor of real activity, 1988Q1 2001Q4 Forecast horizon k d k R 2 S.E. (%) ( 4.696) ( 5.297) ( 4.545) ( 3.495) ( 3.044) ( 3.227) ( 3.464) ( 3.496) ( 3.264) ( 6.089) ( 3.568) ( 2.273) ( 1.319) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions. The negative sign of the estimated coefficients, as well as their significance, is in line with the predictions of the theory of the financial accelerator, in that the high yield spread should interact countercyclically with real activity according to the theory. 5. Conclusion In this letter, we have documented the breakdown in the nominal term spread as a predictor of economic activity during the 1990s. On the other hand, we have also provided the first long-horizon regression evidence that the high yield spread does act as a significant predictor of economic activity over the 1990s. The breakdown in the nominal term spread as a predictor of real activity after 1987 may perhaps be related to the sharp decline in the level and volatility of inflation during the 1990s or to a shift in US monetary policy behavior (see, e.g., Clarida, 2001). However, the lack of a firm theoretical foundation for this relationship has always been somewhat problematic. As Plosser and Rouwenhorst (1994, p. 138) note, there is no a priori reason why one should expect the slope of the term structure of interest rates to predict future real activity particularly well. On the other hand, there is an a priori reason why one should expect the high yield spread to predict future real activity. Viewed as a proxy for the premium on external financing, the theory of the financial accelerator implies that it should do exactly that, and that it should, moreover be countercyclically related to real activity. Our results therefore provide further empirical evidence for the presence of a US financial accelerator.

6 172 A. Mody, M.P. Taylor / Economics Letters 82 (2004) References Bernanke, B.S., Gertler, M., Gilchrist, S., The financial accelerator in a quantitative business cycle framework. In: Taylor, J.B., Woodford, M. (Eds.), Handbook of Macroeconomics: Amsterdam, New York and Oxford: Elsevier Science, North-Holland. Clarida, R.H., The empirics of monetary policy rules in open economies. International Journal of Finance and Economics 6, Dotsey, M., The predictive content of the interest rate term spread for future economic growth. Economic Quarterly 84, (Federal Reserve Bank of Richmond). Gertler, M., Lown, C.S., The information in the high-yield bond spread for the business cycle: evidence and some implications. Oxford Review of Economic Policy 15, Plosser, C.I., Rouwenhurst, K.G., International term structures and real economic growth. Journal of Monetary Economics 33,

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