IDEAS home Printed from https://ideas.repec.org/p/ucr/wpaper/201905.html
   My bibliography  Save this paper

Stein-like Shrinkage Estimation of Panel Data Models with Common Correlated Effects

Author

Listed:
  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

  • Bai Huang

    (CUFE)

  • Aman Ullah

    (UCR)

Abstract

This paper examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.

Suggested Citation

  • Tae-Hwy Lee & Bai Huang & Aman Ullah, 2018. "Stein-like Shrinkage Estimation of Panel Data Models with Common Correlated Effects," Working Papers 201905, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201905
    as

    Download full text from publisher

    File URL: https://economics.ucr.edu/repec/ucr/wpaper/201905.pdf
    File Function: First version, 2018
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    Endogeneity; Panel data; Fixed effect; Common correlated effects; Shrinkage; Model averaging; Local asymptotics; Hausman test.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucr:wpaper:201905. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelvin Mac (email available below). General contact details of provider: https://edirc.repec.org/data/deucrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.