Titlebook: Statistical Inference in Random Coefficient Regression Models; P. A. V. B. Swamy Book 1971 Springer-Verlag Berlin · Heidelberg 1971 Invest

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Efficient Methods of Estimating a Regression Equation with Equicorrelated Disturbances,city. One of the transformed observations will have a variance different from those of the remaining transformed observations, which will all have the same variance. When these variances are unknown, the choice is open to drop the observation with the higher variance, and that if the variance is enough higher it should be dropped.

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A Random Coefficient Investment Model,e U.S. corporations during the years 1935–54 supported his theory. We, in this chapter, assume that the regression coefficient vector appearing in Grunfeld’s micro investment function is random across firms following a multivariate distribution, and use the panel data compiled by Grunfeld to illustrate the method developed in Chapter IV.

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Introduction,ata. The study in this thesis can be divided into two parts. In the first part following Kuh (1959, 1963) we consider a regression equation with additional effects associated with both the time direction and cross-sectional units and develop efficient methods of estimating the parameters of that mod

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Efficient Methods of Estimating the Error Components Regression Models,es, a constant term, an additive disturbance term varying both in the time and crosssectional dimensions, and two additive components--one component associated with time, another with the cross-sectional units. In this chapter we present an analysis of this model as an approach to combining cross-se

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A Random Coefficient Investment Model,placed upon the firm by the securities markets, when taken in conjunction with an estimate of the replacement value of the physical assets of the firm, is a sensitive indicator of the expectations upon which corporate investment decisions are based. His analysis of the investment behavior of 11 larg

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