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Asymptotics for random effects models with serial correlation

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Author Info
Jimmy Skoglund () (Department of Economic Statistics, Stockholm School of Economics)
Sune Karlsson () (Department of Economic Statistics, Stockholm School of Economics)

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Abstract

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models. Consistent estimation and asymptotic normality as N and/or T grows large is established for a comprehensive specification which allows for serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. The consistency and asymptotic normality properties of all commonly used random effects models are obtained as special cases of the comprehensive model. When N or T \rightarrow \infty only a subset of the parameters are consistent and asymptotic normality is established for the consistent subsets.

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Publisher Info
Paper provided by International Conferences on Panel Data in its series 10th International Conference on Panel Data, Berlin, July 5-6, 2002 with number A6-1.

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Date of creation: Mar 2002
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Handle: RePEc:cpd:pd2002:a6-1

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For technical questions regarding this item, or to correct its listing, contact: (Sune Karlsson).

Related research
Keywords: Panel data error components consistency asymptotic normality maximum likelihood.

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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  1. Magnus, Jan R & Woodland, Alan D, 1988. "On the Maximum Likelihood Estimation of Multivariate Regression Models Containing Serially Correlated Error Components," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 707-25, November. [Downloadable!] (restricted)
  2. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January. [Downloadable!] (restricted)
  3. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February. [Downloadable!] (restricted)
  4. Sune Karlsson & Jimmy Skoglund, 2004. "Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects," Empirical Economics, Springer, vol. 29(1), pages 79-88, January. [Downloadable!] (restricted)
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  5. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation, Yale University. [Downloadable!]
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  6. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September. [Downloadable!] (restricted)
  7. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June. [Downloadable!] (restricted)
  8. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January. [Downloadable!] (restricted)
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