This paper 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; tests of whether the dividend-price ratio predicts stock returns is a prototypical example. I show that, by pooling the data, these econometric issues can be dealt with more easily. When no individual intercepts are included in the pooled regression, the pooled estimator has an asymptotically normal distribution and standard tests can be performed. However, when fixed effects are included in the specification, a second order bias in the fixed effects estimator arises from the endogeneity and persistence of the regressors. A new estimator based on recursive demeaning is proposed and its asymptotic normality is derived; the procedure requires no knowledge of the degree of persistence in the regressors and thus sidesteps the main inferential problems in the time-series case. Since forecasting regressions are typically applied to financial or macroeconomic data, the traditional panel data assumption of cross-sectional independence is likely to be violated. New methods for dealing with common factors in the data are therefore also developed. The analytical results derived in the paper are supported by Monte Carlo evidence.
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Paper provided by Göteborg University, Department of Economics in its series Working Papers in Economics with number
160.
Length: 42 pages Date of creation: 02 Feb 2005 Date of revision: Handle: RePEc:hhs:gunwpe:0160
Contact details of provider: Postal: Department of Economics, School of Business, Economics and Law, Göteborg University Box 640, SE 405 30 GÖTEBORG, Sweden Phone: 031-773 10 00 Web page: http://www.handels.gu.se/econ/ More information through EDIRC
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Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
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