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Published online by Cambridge University Press: 06 July 2012
This paper generalizes the results for the Bridge estimator of Huang, Horowitz, and Ma (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular, we show that the Bridge estimator isoracle efficient. It can correctly distinguish between relevant and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation.
In the case of more explanatory variables than observations we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables if the error terms are Gaussian. Furthermore, a partial orthogonality condition of the same type as in Huang et al. (2008) is needed to restrict the dependence between relevant and irrelevant variables.
The author thanks Svend Erik Graversen, Niels Haldrup, Michael Jansson, Jørgen Hoffmann-Jørgensen, Adrian Pagan, Timo Teräsvirta, Allan Würtz, the co-editor, and an anonymous referee for help, comments, and suggestions. Also thanks to Joel Horowitz for responding quickly to my e-mails. All errors and shortcomings are my responsibility. Financial support from CREATES funded by the Danish National Research Foundation is gratefully acknowledged.