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dc.contributor.author Wallace, Dudley en_US
dc.contributor.author Hussain, Ashiq en_US
dc.date.accessioned 2010-03-09T15:26:54Z
dc.date.available 2010-03-09T15:26:54Z
dc.date.issued 1969 en_US
dc.identifier.uri http://hdl.handle.net/10161/1865
dc.description.abstract A mixed model of regression with error components is proposed as one of possible interest for combining cross section and time series data. For known variances, it is shown that Aitken estimators and covariance estimators are in one sense asymptotically equivalent, even though the Aitken estimators are more efficient in small samples. Turning to unknown variance components, Zellner-type iterative estimators are compared with covariance estimators. Here, few small sample properties are obtained. However, it is shown that covariance and Zellner-type estimators have equivalent asymptotic distributions and equivalent limits of sequences of first and second order moments for weakly nonstochastic regressors. For the model analyzed, the theoretical results obtained, as well as ease of computation, tend to support traditional covariance estimators of the regression parameters. An additional interesting result presented in an appendix is that ordinary least squares estimates of the β's (ignoring the error components) have unbounded asymptotic variances. On efficiency grounds, this argues rather strongly for some care in combining data from alternative sources in regression analysis. en_US
dc.format.extent 329887 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Econometrica en_US
dc.subject Regression en_US
dc.subject error en_US
dc.subject estimators en_US
dc.title The Use of Error Components Models in Combining Cross Section with Time Series Data en_US
dc.type Journal Article en_US
dc.department Economics

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