Modeling the Volatility of Real GDP Growth: The Case of Japan Revisited

Previous studies (e.g., Hamori, 2000; Ho and Tsui, 2003; Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perronâs (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.

Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and GARCH Models

This paper revisits the issue of conditional volatility in real GDP growth rates for Canada, Japan, the United Kingdom, and the United States. Previous studies find high persistence in the volatility. This paper shows that this finding largely reflects a nonstationary variance. Output growth in the four countries became noticeably less volatile over the past few decades. In this paper, we employ the modified ICSS algorithm to detect structural change in the unconditional variance of output growth. One structural break exists in each of the four countries. We then use generalized autoregressive conditional heteroskedasticity (GARCH) specifications modeling output growth and its volatility with and without the break in volatility. The evidence shows that the time-varying variance falls sharply in Canada, Japan, and the U.K. and disappears in the U.S., excess kurtosis vanishes in Canada, Japan, and the U.S. and drops substantially in the U.K., once we incorporate the break in the variance equation of output for the four countries. That is, the integrated GARCH (IGARCH) effect proves spurious and the GARCH model demonstrates misspecification, if researchers neglect a nonstationary unconditional variance.

The Effects of Trade Orientation and Human Capital on Total Factor Productivity

We study the effects of trade orientation and human capital on total factor productivity for a pooled cross-section, time-series sample of developed and developing countries. We first estimate total factor productivity from a parsimonious specification of the aggregate production function involving output per worker, capital per worker, and the labor force, both with and without the stock of human capital. Then we consider a number of potential determinants of total factor productivity growth including several measures of trade orientation as well as a measure of human capital. We find that a high degree of openness benefits total factor productivity and that human capital contributes to total factor productivity only after our measure of openness passes some threshold level. Before that threshold, increases in human capital actually depress total factor productivity. Finally, we also consider the issue of convergence of real GDP per worker and total factor productivity, finding more evidence of convergence for the latter than for the former.

The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis

Recently, Fagiolo et al. (2008) find fat tails of economic growth rates after adjusting outliers, autocorrelation and heteroskedasticity. This paper employs US quarterly real output growth, showing that this finding of fat tails may reflect the Great Moderation. That is, leptokurtosis disappears after GARCH adjustment once we incorporate the break in the variance equation.