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Consistent estimation of regression models with incompletely observed exogenous variables

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Author Info

  • Nijman, T.E.
  • Palm, F.C.

    (Tilburg University, Faculty of Economics and Business Administration)

Abstract

We consider consistent estimation of regression models in which the exogenous variables are incompletely observed assuming that the response mechanism is random. In the literature on imputed data, several estimators have been proposed which are based on approximations substituted for the missing data. We discuss conditions under which these proxy variables estimators are asymptotically more efficient than the estimator based on complete observations and we show how an optimal proxy variables estimator can be obtained. For simple models, some proxy variables estimators are almost as efficient as the Gaussian maximum likelihood (ML) estimator and sometimes more efficient than the pseudo ML estimator.

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Bibliographic Info

Paper provided by Tilburg University, Faculty of Economics and Business Administration in its series Research Memorandum with number 272.

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Date of creation: 1987
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Handle: RePEc:dgr:kubrem:1987272

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Web page: http://www.tilburguniversity.edu/nl/over-tilburg-university/schools/economics-and-management/

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  1. Palm, F.C. & Nijman, T.E., 1982. "Linear regression using both temporally aggregated and temporally disaggregated data," Open Access publications from Tilburg University urn:nbn:nl:ui:12-153298, Tilburg University.
  2. Conniffe, Denis, 1983. "Small-Sample Properties of Estimators of Regression Coefficients Given a Common Pattern of Missing Data," Review of Economic Studies, Wiley Blackwell, vol. 50(1), pages 111-20, January.
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Cited by:
  1. Feijoo, Santiago Rodriguez & Caro, Alejandro Rodriguez & Quintana, Delia Davila, 2003. "Methods for quarterly disaggregation without indicators; a comparative study using simulation," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 63-78, May.

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