Panel data tests of return models with applications to global stock returns.

摘要

After the introduction, the first essay (Chapter 2) of this dissertation analyzes econometric inference in predictive regressions in a panel data setting. In a traditional time-series framework, estimation and testing are often made difficult by the endogeneity and near persistence of many forecasting variables. I show that by pooling the data these econometric issues can be dealt with more easily; the summing up over the cross-section in the pooled estimator eliminates the usual near unit-root asymptotic distributions found in the time-series case and enables standard inferential procedures.; In the second essay (Chapter 3), I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend and earnings price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock-returns in OECD countries. In contrast to the interest rate variables, no strong or consistent evidence of predictability is found when considering the earnings- and dividend-price ratios as predictors.; In this essay, I also develop new asymptotic results for long-run regressions with over-lapping observations. Typically, auto-correlation robust estimation of the standard errors is used to perform inference in long-run regressions. However, these robust estimators tend to perform poorly in finite samples since the serial correlation induced in the error terms by overlapping data is often very strong. In a time-series setting, I show that rather than using robust standard errors, the standard t-statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data. These long-run results are also extended to the panel data case.; The last essay (Chapter 4) considers the estimation of average autoregressive roots-near-unity in panels where the time-series have heterogenous local-to-unity parameters. The pooled estimator is shown to have a potentially severe bias and a robust median based procedure is proposed instead. The methods proposed in this essay provide a useful way of summarizing the persistence in a panel data set.