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:||16. Feb 2013 05:10|
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