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Aggregation of Linear Models for Panel Data

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Author Info
Alexandre Petkovic
David Veredas

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Abstract

We study the impact of individual and temporal aggregation in linear static and dynamic models for panel data in terms of model specification and efficiency of the estimated parameters. Model wise we find that i) individual aggregation does not affect the model structure but temporal aggregation may introduce residual autocorrelation, and ii) individual aggregation entails heteroskedasticity while temporal aggregation does not. Estimation wise we find that i) in the static model, estimation by least squares with the aggregated data entails a decrease in the efficiency of the estimated parameters but we cannot rank different aggregation schemes in terms of eciency, and ii) in the dynamic model, estimation by GMM does not necessarily entail a decrease in the efficiency of the estimated parameters under individual aggregation and no analytic comparison can be established for temporal aggregation, though simulations suggests that temporal aggregation deteriorates the accuracy of the estimates.

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File URL: http://www.ecares.org/index2.php?option=com_docman&task=doc_view&gid=68&Itemid=204
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Publisher Info
Paper provided by Université Libre de Bruxelles, Ecares in its series ECARES Working Papers with number 2009_012.

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Length: 30 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:eca:wpaper:2009_012

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Related research
Keywords: Panel data; temporal aggregation; temporal aggregation; model specification; efficiency.;

Find related papers by JEL classification:
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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References listed on IDEAS
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  1. Abeysinghe, Tilak, 1998. "Forecasting Singapore's quarterly GDP with monthly external trade," International Journal of Forecasting, Elsevier, vol. 14(4), pages 505-513, December. [Downloadable!] (restricted)
  2. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-27, July. [Downloadable!] (restricted)
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  3. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December. [Downloadable!] (restricted)
  4. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-35, November. [Downloadable!] (restricted)
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  5. Zellner, Arnold & Montmarquette, Claude, 1971. "A Study of Some Aspects of Temporal Aggregation Problems in Econometric Analyses," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 335-42, November. [Downloadable!] (restricted)
  6. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October. [Downloadable!] (restricted)
  7. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June. [Downloadable!] (restricted)
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This page was last updated on 2009-11-18.

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