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Penalized quantile regression for dynamic panel data

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Bibliographic Details
Title: Penalized quantile regression for dynamic panel data
Authors: Galvao, Antonio F.1 agalvao@uwm.edu, Montes-Rojas, Gabriel V.2 Gabriel.Montes-Rojas.1@city.ac.uk
Source: Journal of Statistical Planning & Inference. Nov2010, Vol. 140 Issue 11, p3476-3497. 22p.
Subjects: Regression analysis, Panel analysis, Bayesian analysis, Mathematical variables, Linear statistical models, Monte Carlo method, Simulation methods & models
Abstract: Abstract: This paper studies penalized quantile regression for dynamic panel data with fixed effects, where the penalty involves l 1 shrinkage of the fixed effects. Using extensive Monte Carlo simulations, we present evidence that the penalty term reduces the dynamic panel bias and increases the efficiency of the estimators. The underlying intuition is that there is no need to use instrumental variables for the lagged dependent variable in the dynamic panel data model without fixed effects. This provides an additional use for the shrinkage models, other than model selection and efficiency gains. We propose a Bayesian information criterion based estimator for the parameter that controls the degree of shrinkage. We illustrate the usefulness of the novel econometric technique by estimating a “target leverage” model that includes a speed of capital structure adjustment. Using the proposed penalized quantile regression model the estimates of the adjustment speeds lie between 3% and 44% across the quantiles, showing strong evidence that there is substantial heterogeneity in the speed of adjustment among firms. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: This paper studies penalized quantile regression for dynamic panel data with fixed effects, where the penalty involves l 1 shrinkage of the fixed effects. Using extensive Monte Carlo simulations, we present evidence that the penalty term reduces the dynamic panel bias and increases the efficiency of the estimators. The underlying intuition is that there is no need to use instrumental variables for the lagged dependent variable in the dynamic panel data model without fixed effects. This provides an additional use for the shrinkage models, other than model selection and efficiency gains. We propose a Bayesian information criterion based estimator for the parameter that controls the degree of shrinkage. We illustrate the usefulness of the novel econometric technique by estimating a “target leverage” model that includes a speed of capital structure adjustment. Using the proposed penalized quantile regression model the estimates of the adjustment speeds lie between 3% and 44% across the quantiles, showing strong evidence that there is substantial heterogeneity in the speed of adjustment among firms. [Copyright &y& Elsevier]
ISSN:03783758
DOI:10.1016/j.jspi.2010.05.008