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The Stambaugh bias in panel predictive regressions
This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions. Monte Carlo evidence shows that the bias and resulting size distortions can be severe. A new bias-corrected estimator is proposed, which is shown to work well in finite samples and to lead to approximately normally distributed t-statistics. Overall, the results show that the econometric issues associated with predictive regressions when using time-series data to a large ...
Estimation of average local-to-unity roots in heterogenous panels
This paper 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. This median estimator has a small asymptotic bias that can be eliminated almost completely by a bias correction procedure. The asymptotic normality of the estimator is proved. The methods proposed in the paper provide a useful way of summarizing the persistence in a panel data set, as well as a complement to more ...
The illusive quest: do international capital controls contribute to currency stability?
We investigate the effectiveness of capital controls in insulating economies from currency crises, focusing in particular on both direct and indirect effects of capital controls and how these relationships may have changed over time in response to global financial liberalization and the greater mobility of international capital. We predict the likelihood of currency crises using standard macroeconomic variables and a probit equation estimation methodology with random effects. We employ a comprehensive panel data set comprised of 69 emerging market and developing economies over 1975?2004. Both ...
Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators
In this paper, we examine the use of Box-Tiao*s (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen*s (1995) ML-based method suffers from severe small sample size distortions. Furthermore, the distributions of empirical economic and financial time series tend to display fat tails, heteroskedasticity and skewness that are inconsistent with the usual distributional assumptions of likelihood-based approach. The testing statistic based on Box-Tiao*s ...