Mixing Forecasts in Linear Simultaneous Equations Under Quadratic Loss

Esfandiar Maasoumi

Chapter 10 in Contributions to Consumer Demand and Econometrics, 1992, pp 176-188 from Palgrave Macmillan

Abstract: Abstract In the econometric literature a great variety of ‘improved’ estimators have been proposed which are commonly expressible as mixtures of traditional estimators. Minimum risk (MELO) Bayesian estimators, Steinlike and pre-test estimators, mixed regression and Minimum Mean Squared Error (MMSE) are examples of such estimators. See Zellner and Vandaele (1975) and Maasoumi (1978, 1984). Zellner and Vandaele (1975) consider the Bayesian interpretations of such estimators, Sawa (1973) considers a MMSE combination of the OLS and 2SLS structural estimators, and Newbold and Granger (1974) is an example where informal mixtures of predictors have been investigated and observed to perform well.

Keywords: Minimum Mean Square Error; Simultaneous Equation; Quadratic Loss; Simultaneous Equation Model; Finite Moment (search for similar items in EconPapers)
Date: 1992
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DOI: 10.1007/978-1-349-12221-9_10

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