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Asymptotics and Bootstrap for Transformed Panel Data Regressions

Author

Listed:
  • Liangjun Su

    () (School of Economics, Singapore Management University)

  • Zhenlin Yang

    () (School of Economics, Singapore Management University)

Abstract

This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference can be enormous.

Suggested Citation

  • Liangjun Su & Zhenlin Yang, 2009. "Asymptotics and Bootstrap for Transformed Panel Data Regressions," Working Papers 03-2009, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:03-2009
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    References listed on IDEAS

    as
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    Cited by:

    1. Jin, Fei & Lee, Lung-fei, 2015. "On the bootstrap for Moran’s I test for spatial dependence," Journal of Econometrics, Elsevier, vol. 184(2), pages 295-314.

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    More about this item

    Keywords

    Asymptotics; Bootstrap; Quasi-MLE; Transformed panels; Variancecovariance matrix estimate.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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