Predictive regressions with panel data
Erik Hjalmarsson ()
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Erik Hjalmarsson: Department of Economics, Postal: Yale University
No 160, Working Papers in Economics from University of Gothenburg, Department of Economics
Abstract:
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.
Keywords: Predictive regression; panel data; pooled regression; cross-sectional dependence; stock return predictability; fully modified estimation; local-to-unity (search for similar items in EconPapers)
JEL-codes: C22 C23 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2005-02-02
New Economics Papers: this item is included in nep-cfn and nep-ecm
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Citations: View citations in EconPapers (2)
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http://hdl.handle.net/2077/2765 (text/html)
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Working Paper: Predictive regressions with panel data (2006) 
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