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Linear regression using both temporally aggregated and temporally disaggregated data: Revisited

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  • Qian, Hang

Abstract

This paper discusses regression models with aggregated covariate data. Reparameterized likelihood function is found to be separable when one endogenous variable corresponds to one instrument. In that case, the full-information maximum likelihood estimator has an analytic form, and thus outperforms the conventional imputed value two-step estimator in terms of both efficiency and computability. We also propose a competing Bayesian approach implemented by the Gibbs sampler, which is advantageous in more flexible settings where the likelihood does not have the separability property.

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File URL: http://mpra.ub.uni-muenchen.de/32686/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 32686.

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Date of creation: Jul 2010
Date of revision:
Handle: RePEc:pra:mprapa:32686

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Keywords: Aggregated covariate; Maximum likelihood; Bayesian inference;

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  1. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, Elsevier, vol. 158(2), pages 246-261, October.
  2. Palm, F.C. & Nijman, Th., 1981. "Linear regression using both temporally aggregated and temporally disaggregated data," Serie Research Memoranda, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics 0017, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  3. Hsiao, Cheng, 1979. "Linear regression using both temporally aggregated and temporally disaggregated data," Journal of Econometrics, Elsevier, Elsevier, vol. 10(2), pages 243-252, June.
  4. John F. Geweke, 1995. "Bayesian inference for linear models subject to linear inequality constraints," Working Papers, Federal Reserve Bank of Minneapolis 552, Federal Reserve Bank of Minneapolis.
  5. Dagenais, Marcel G., 1973. "The use of incomplete observations in multiple regression analysis : A generalized least squares approach," Journal of Econometrics, Elsevier, Elsevier, vol. 1(4), pages 317-328, December.
  6. Geweke, John F, 1978. "Temporal Aggregation in the Multiple Regression Model," Econometrica, Econometric Society, Econometric Society, vol. 46(3), pages 643-61, May.
  7. Gourieroux, Christian & Monfort, Alain, 1981. "On the Problem of Missing Data in Linear Models," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 48(4), pages 579-86, October.
  8. Koop, Gary M & Poirier, Dale J & Tobias, Justin, 2007. "Bayesian Econometric Methods," Staff General Research Papers, Iowa State University, Department of Economics 12722, Iowa State University, Department of Economics.
  9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, Elsevier, vol. 131(1-2), pages 59-95.
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