The Long-Run Relationship between Money, Nominal GDP, and the Price Level in Venezuela: 1950 to 1996

This paper explores whether a significant long-run relationship exists between money and nominal GDP and between money and the price level in the Venezuelan economy. We apply time-series econometric techniques to annual data for the Venezuelan economy for 1950 to 1996. An important feature of our analysis is the use of tests for unit roots and cointegration with structural breaks. Certain characteristics of the Venezuelan experience suggest that structural breaks may be important. Since the economy depends heavily on oil revenue, oil price shocks have had important influences on most macroeconomic variables. Also since the economy possesses large foreign debt, the world debt crisis that exploded in 1982 had pervasive effects on the Venezuelan economy. Radical changes in economic policy and political instability may have also significantly affected the movement of the macroeconomy. We find that a long-run relationship exists between narrow money (M1) and nominal GDP, the GDP deflator, and the CPI when one makes allowances for one or two structural breaks. We do not find such long-run relationships when broad money (M2) is used.

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.

Optimal Test for Parameter Instability When the Unstable Process and the Error Distribution are Unknown

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does not provide asymptotic power gains. I first derive a test under known error distribution, which is asymptotically equivalent to LR tests for correctly identified unstable parameter processes under suitable conditions. The conditions are weak enough to cover a wide range of unstable processes such as various types of structural breaks and time varying parameter processes. The test is then extended to semiparametric models in which the underlying distribution in unknown but treated as unknown infinite dimensional nuisance parameter. The semiparametric test is adaptive in the sense that its asymptotic power function is equivalent to the power envelope under known error distribution.