The Great Moderation and the Relationship between Output Growth and Its Volatility

This study examines the effect of the Great Moderation on the relationship between U.S. output growth and its volatility over the period 1947 to 2006. First, we consider the possible effects of structural change in the volatility process. In so doing, we employ GARCH-M and ARCH-M specifications of the process describing output growth rate and its volatility with and without a one-time structural break in volatility. Second, our data analyses and empirical results suggest no significant relationship between the output growth rate and its volatility, favoring the traditional wisdom of dichotomy in macroeconomics. Moreover, the evidence shows that the time-varying variance falls sharply or even disappears once we incorporate a one-time structural break in the unconditional variance of output starting 1982 or 1984. That is, the integrated GARCH effect proves spurious. Finally, a joint test of a trend change and a one-time shift in the volatility process finds that the one-time shift dominates.

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.

A Dynamic Analysis: the Smile and Display of Dentition during Speech

The objective of this retrospective study is to follow up on a previous Dynamic Smile Analysis and videographically analyze and develop averages for soft tissue norms with respect to the display of dentition during speech. These values would then be compared cross-sectionally across different age groups to see whether changes attributable to the aging process could be seen. A secondary objective was to compare averages for soft tissue norms in the display of dentition during speech to averages for soft tissue norms in the display of dentition during the smile. Materials and Method: Records from a previous study in which video equipment was used to capture video for 26 1 subjects were re-evaluated to find appropriate frames to analyze for speech. Two frames for each subject were selected; one frame representing the maximal display of maxillary incisors during speech and the second representing the widest transverse display of dentition during speech. After excluding 40 subjects the data for the remaining 221 subjects was analyzed. These averages were then compared to averages attained in the previous study to compare the display of the dentition during speech to the display of the dentition during smile. Results: On average, a difference in 1.29 mm was seen in the display of the maxillary incisors during speech at maximal display and during the smile. An average of 7.23 mm of maxillary incisors is readily visible during maximum display of maxillary incisors during speech, as compared to 8.52 mm during the smile. The constructed smile index was also smaller when measured during the speech when compared to the smile index by an average of 2.58 units. Conclusion: This study helps to establish age-related dynamic norms for the display of dentition during speech. The dynamic measures indicate that the display of dectition is greater, on average, during the smile than at speech.

Dynamic Smile Analysis : Changes with Age

Introduction: The objective of this study was to define age-related changes in the human smile. The areas of interest were: upper lip length at smile and repose, upper lip thickness at smile and repose, maxillary incisal display at smile, interlabial gap height at smile, smile index, percent buccal corridors, intercommissural width at rest, smile height, and smile arc. A secondary objective was to study the perioral changes from rest to smile and compare them on the basis of age. Materials and Method: Video equipment was used to capture video for 261 subjects. Two frames for each subject were selected; one frame representing the lips and rest and the second representing the widest smile. After excluding 40 subjects the data for the remaining 221 subjects was analyzed. Results: There was a decrease of 1.5 to 2 mm in the maxillary incisor display during smile, with increase in age. Smile index significantly increased with increase in age. Most (78%) subjects displayed an average smile height. No subjects in the 50 and over age group displayed a high smile while no subjects in the 15-19 year old age group presented with a low smile. All the dynamic measures indicated there was a pattern of decreasing change from rest to smile especially evident after the 30-39 year old age group. Conclusions: This study helps to establish age related dynamic norms. As the person ages the smile gets narrower vertically and wider transversely. The dynamic measures indicate that the muscles' ability to create a smile decreases with increasing age.

Total Factor Productivity and Monetary Policy: Evidence from Conditional Volatility

This paper empirically assesses whether monetary policy affects real economic activity through its affect on the aggregate supply side of the macroeconomy. Analysts typically argue that monetary policy either does not affect the real economy, the classical dichotomy, or only affects the real economy in the short run through aggregate demand new Keynesian or new classical theories. Real business cycle theorists try to explain the business cycle with supply-side productivity shocks. We provide some preliminary evidence about how monetary policy affects the aggregate supply side of the macroeconomy through its affect on total factor productivity, an important measure of supply-side performance. The results show that monetary policy exerts a positive and statistically significant effect on the supply-side of the macroeconomy. Moreover, the findings buttress the importance of countercyclical monetary policy as well as support the adoption of an optimal money supply rule. Our results also prove consistent with the effective role of monetary policy in the Great Moderation as well as the more recent rise in productivity growth.

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.