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Estimation of SUR Model with Non-nested Missing Observations

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  • Hae-Shin Hwang
  • Craig Schulman

Abstract

This paper considers alternative two-step estimators and their small sample properties for the seemingly unrelated regression (SUR) model with non-nested missing observations. A Monte Carlo experiment indicates that alternative estimators have more profound differences in their efficiency, compared to the case of nested missing observations. In particular, the two-step application of the Hartley-Hocking maximum likelihood estimator can realize a significant gain in efficiency. There are substantial losses in efficiency when only the subset of data that has complete observations is used in estimation.

Suggested Citation

  • Hae-Shin Hwang & Craig Schulman, 1996. "Estimation of SUR Model with Non-nested Missing Observations," Annals of Economics and Statistics, GENES, issue 44, pages 219-240.
    • Handle: RePEc:adr:anecst:y:1996:i:44:p:219-240
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    File URL: http://www.jstor.org/stable/20076045
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    Cited by:

    1. Olivier Donni & Eleonora Matteazzi, 2012. "On the Importance of Household Production in Collective Models: Evidence from U.S. Data," Annals of Economics and Statistics, GENES, issue 105-106, pages 99-125.
    2. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.

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