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Penalized quantile regression for dynamic panel data

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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]
Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 51844773
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Statistical+Planning+%26+Inference%22">Journal of Statistical Planning & Inference</searchLink>. Nov2010, Vol. 140 Issue 11, p3476-3497. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Panel+analysis%22">Panel analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+variables%22">Mathematical variables</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1016/j.jspi.2010.05.008
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 3476
    Subjects:
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Panel analysis
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Mathematical variables
        Type: general
      – SubjectFull: Linear statistical models
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Simulation methods & models
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      – TitleFull: Penalized quantile regression for dynamic panel data
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            NameFull: Montes-Rojas, Gabriel V.
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              M: 11
              Text: Nov2010
              Type: published
              Y: 2010
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