Qian, Hang (2010): Linear regression using both temporally aggregated and temporally disaggregated data: Revisited.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Linear regression using both temporally aggregated and temporally disaggregated data: Revisited|
|Keywords:||Aggregated covariate; Maximum likelihood; Bayesian inference|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Hang Qian|
|Date Deposited:||08. Aug 2011 23:57|
|Last Modified:||30. Dec 2015 21:56|
Allison, P. D., 2000. Multiple imputation for missing data: a cautionary tale. Sociological methods and Research 28, 301-309.
Anderson, T. W., 1957. Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. Journal of the American Statistical Association 52 (278), 200-203.
Andreou, E., Ghysels, E., Kourtellos, A., 2010. Regression models with mixed sampling frequencies. Journal of Econometrics.
Bureau of Labor Statistics, U.S. Department of Labor, 2010. Occupational Outlook Handbook. Washington: U.S. Government Printing Office.
Dagenais, M. G., 1973. The use of incomplete observations in multiple regression analysis: A generalized least squares approach. Journal of Econometrics 1 (4), 317-328.
Fraser, D. A. S., 1951. Normal samples with linear constraints and given variances. Canadian Journal of Mathematics 3, 363-366.
Geweke, J. F., 1978. Temporal aggregation in the multiple regression model. Econometrica 46 (3), 643-61.
Geweke, J. F., 1995. Bayesian inference for linear models subject to linear inequality constraints. Working Papers 552, Federal Reserve Bank of Minneapolis.
Ghysels, E., Santa-Clara, P., Valkanov, R., 2006. Predicting volatility: getting the most out of return data sampled at di_erent frequencies. Journal of Econometrics 131 (1-2), 59-95.
Gourieroux, C., Monfort, A., 1981. On the problem of missing data in linear models. Review of Economic Studies 48 (4), 579-86.
Hsiao, C., 1979. Linear regression using both temporally aggregated and temporally disaggregated data. Journal of Econometrics 10 (2), 243-252.
Koop, G., Poirier, D. J., Tobias, J. L., 2007. Bayesian Econometric Methods. Cambridge Books. Cambridge University Press.
Palm, F. C., Nijman, T. E., 1982. Linear regression using both temporally aggregated and temporally disaggregated data. Journal of Econometrics 19 (2-3), 333-343.
Rubin, D. B., 1987. Multiple imputation for nonresponse in surveys. New York: Wiley.
Schafer, J. L., 1997. Analysis of incomplete multivariate data. London: Chapman and Hall.
Van Buuren, S., Boshuizen, H. C., Knook, D. L., 1999. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 18, 681-694.