Monetary Shocks and Real Exchange Rate Hysteresis: Evidence from the G-7 Countries

Long-run monetary neutrality specifies that nominal disturbances do not affect long-run real exchange rates. However, the "over depreciation" of the US dollar in the late 1980s, after its strong appreciation earlier in the decade, suggested to a number of observers that nominal disturbances alter long-run real exchange rates; that is, money supply shocks entail real exchange rate hysteresis. Using data from the G-7 countries and the post-1973 float, the paper measures the long-run effects of relative money supply disturbances on real US dollar exchange rates. Little evidence of hysteretic monetary policy effects is found.     

Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns

In this article we examine the structural stability of predictiveregression models of U.S. quarterly aggregate real stock returnsover the postwar era. We consider predictive regressions modelsof S&P 500 and CRSP equal-weighted real stock returns basedon eight financial variables that display predictive abilityin the extant literature. We test for structural stability usingthe popular Andrews SupF statistic and the Bai subsample procedurein conjunction with the Hansen heteroskedastic fixed-regressorbootstrap. We also test for structural stability using the recentlydeveloped methodologies of Elliott and Müller, and Baiand Perron. We find strong evidence of structural breaks infive of eight bivariate predictive regression models of S&P500 returns and some evidence of structural breaks in the threeother models. There is less evidence of structural instabilityin bivariate predictive regression models of CRSP equal-weightedreturns, with four of eight models displaying some evidenceof structural breaks. We also obtain evidence of structuralinstability in a multivariate predictive regression model ofS&P 500 returns. When we estimate the predictive regressionmodels over the different regimes defined by structural breaks,we find that the predictive ability of financial variables canvary markedly over time.     

Are Real GDP Levels Nonstationary? Evidence from Panel Data Tests

Ever since the seminal paper of Nelson and Plosser (1982), researchers have focused on the potential nonstationarity of important macroeconomic variables, and unit root tests are now a standard procedure in empirical analyses. While there are many findings of unit roots in macroeconomic variables using the popular augmented Dickey and Fuller (1979) test, this test has low power against near-unit-root alternatives. Recently, panel data procedures have been proposed as an avenue to increased power. This paper applies panel unit root tests to international real GDP and real GDP per capita data. The results overwhelmingly indicate that international real GDP and real GDP per capita levels are nonstationary.     

Structural breaks and GARCH models of exchange rate volatility

We investigate the empirical relevance of structural breaks for GARCH models of exchange rate volatility using both in‐sample and out‐of‐sample tests. We find significant evidence of structural breaks in the unconditional variance of seven of eight US dollar exchange rate return series over the 1980–2005 period—implying unstable GARCH processes for these exchange rates—and GARCH(1,1) parameter estimates often vary substantially across the subsamples defined by the structural breaks. We also find that it almost always pays to allow for structural breaks when forecasting exchange rate return volatility in real time. Combining forecasts from different models that accommodate structural breaks in volatility in various ways appears to offer a reliable method for improving volatility forecast accuracy given the uncertainty surrounding the timing and size of the structural breaks. Copyright © 2008 John Wiley & Sons, Ltd.     

Valuation ratios and long‐horizon stock price predictability

Using annual data for 1872–1997, this paper re‐examines the predictability of real stock prices based on price–dividend and price–earnings ratios. In line with the extant literature, we find significant evidence of increased long‐horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected at longer horizons based on bootstrapped critical values constructed from linear representations of the data. While increased statistical power at long horizons in finite samples provides a possible explanation for the pattern of predictability in the data, we find via Monte Carlo simulations that the power to detect predictability in finite samples does not increase at long horizons in a linear framework. An alternative explanation for the pattern of predictability in the data is nonlinearities in the underlying data‐generating process. We consider exponential smooth‐transition autoregressive models of the price–dividend and price–earnings ratios and their ability to explain the pattern of stock price predictability in the data. Copyright © 2005 John Wiley & Sons, Ltd.