2009
DOI: 10.1016/j.japwor.2008.10.002
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Modeling the volatility of real GDP growth: The case of Japan revisited

Abstract: 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 Perrons (1998, 2003)… Show more

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Cited by 23 publications
(19 citation statements)
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References 65 publications
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“…The normality test (Jarque–Bera) is, however, not fulfilled in the majority of equations. My results are similar to Fang and Miller's (), who also fail the normality test in most cases. Non‐normality may result in standard errors that are inappropriate for inference.…”
Section: Resultssupporting
confidence: 88%
“…The normality test (Jarque–Bera) is, however, not fulfilled in the majority of equations. My results are similar to Fang and Miller's (), who also fail the normality test in most cases. Non‐normality may result in standard errors that are inappropriate for inference.…”
Section: Resultssupporting
confidence: 88%
“…According to the break test results, the Czech Republic and Romania have two and Serbia has one structural break in the mean of the series, but all countries have at least one break in the variance. In order to avoid poor specification in CGARCH-M models, we used dummy variables to accommodate the extraordinary changes, similarly as Fang andMiller (2009), Cunado et al (2006), Ewing and Malik (2013) tackled their problems with breaks in GARCH models. For every break in the mean and variance equation a dummy variable was created and inserted into the models.…”
Section: Research Data and Test For Multiple Structural Breaksmentioning
confidence: 99%
“…If GARCH models totally capture unconditional skewness and leptokurticity, the standardized residuals should reflect a normal distribution, as emphasized by Fang and Miller (2009). Otherwise, if standardized residuals still register heterogeneous behaviour after GARCH process utilization, then empirical data are probably polluted with structural breaks.…”
Section: Generating Inflation Uncertainty Seriesmentioning
confidence: 99%
“…The procedure was conducted in similar way as presented in the research of Cunado, Biscarri, and de Gracia (2006), Fang and Miller (2009), Ewing and Malik (2013), that is, dummy variable takes unity from the break date onward and zero otherwise. We determined the time of structural breaks without prior knowledge of time period, utilizing a statistical technique of Bai and Perron (2003).…”
Section: Generating Inflation Uncertainty Seriesmentioning
confidence: 99%