Conditional independence models for seemingly unrelated regressions with incomplete data
We consider normal [reverse not equivalent] Gaussian seemingly unrelated regressions (SUR) with incomplete data (ID). Imposing a natural minimal set of conditional independence constraints, we find a restricted SUR/ID model whose likelihood function and parameter space factor into the product of the likelihood functions and the parameter spaces of standard complete data multivariate analysis of variance models. Hence, the restricted model has a unimodal likelihood and permits explicit likelihood inference. In the development of our methodology, we review and extend existing results for complete data SUR models and the multivariate ID problem.
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Volume (Year): 97 (2006)
Issue (Month): 2 (February)
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- Schmidt, Peter, 1977. "Estimation of seemingly unrelated regressions with unequal numbers of observations," Journal of Econometrics, Elsevier, vol. 5(3), pages 365-377, May.
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- Andersson, Steen A. & Perlman, Michael D., 1998. "Normal Linear Regression Models With Recursive Graphical Markov Structure," Journal of Multivariate Analysis, Elsevier, vol. 66(2), pages 133-187, August.
- Hwang, Hae-shin, 1990. "Estimation of a Linear SUR Model with Unequal Numbers of Observations," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 510-15, August.
- Steel, M. A. & Wood, G. R., 1993. "On a problem of Andersson and Perlman," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 381-382, December.
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