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Linear restrictions and two step least squares with applications

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  • del Pino, Guido E.

Abstract

In this paper we consider the full rank regression model with arbitrary covariance matrix: Y = Xß + [var epsilon]. It is shown that the effect of restricting the information Y to T = A'Y may be analyzed through an associatedi regression problem which is amenable to solution by two step least squares. The results are applied to the important case of missing observations, where some classical results are rederived.

Suggested Citation

  • del Pino, Guido E., 1984. "Linear restrictions and two step least squares with applications," Statistics & Probability Letters, Elsevier, vol. 2(4), pages 245-248, August.
    • Handle: RePEc:eee:stapro:v:2:y:1984:i:4:p:245-248
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    Cited by:

    1. Gupta, A. K. & Kabe, D. G., 1997. "Linear restrictions and two step multivariate least squares with applications," Statistics & Probability Letters, Elsevier, vol. 32(4), pages 413-416, April.

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