Improved methods of inference in econometrics
Author(s)
Bibliographic Information
Improved methods of inference in econometrics
（Studies in mathematical and managerial economics, v. 34）
North-Holland, 1986
Available at 56 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book has as its general objective the improvement of estimation rules for linear statistical models and the development of estimating procedures, to be used with a single data set, that are appropriate to economic decision problems. Advances in the estimating procedure are brought about by changing: (i) the statistical model, (ii) the amount of information used, and (iii) the measure of performance. Within this context the book considers estimation and hypothesis testing when sample information and non-sample information of an inequality form are combined. Also evaluated are: the statistical consequences of using traditional and non-traditional estimators when the error assumptions are weakened; and the precision and statistical implications of new Stein estimators.
Table of Contents
Introduction. The Inferential and Decision Framework. The Measure of Performance. Some Alternative Statistical Models, Estimators and Tests. Inequality Estimation and Hypothesis Testing. Inequality Estimation and Hypothesis Testing of the Location Parameter of a Normal Random Variable. General Linear Statistical Model and a Single Linear Inequality Constraint. Inequality Restricted Estimation of Two or More Location Parameters: The Orthonormal Case. An Inequality Restricted Stein Rule Estimator. Hypothesis Testing for Two or More Inequalities: The Orthogonal Case. Inequality Restricted Estimation: General Design and Restriction Matrices. Inequality Hypothesis Testing: General Case. Some Sampling Results for the Stein Family of Estimators. Assessing the Precision of Stein's Estimator. Some Evaluations of the Sampling Performance of the Limited Translation and New Stein Estimators. Estimation Under Non-Normal Errors and Quadratic Loss. Estimation and Hypothesis Testing in the Case of Possible Heteroskedasticity. Subject Index.
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