Misspecification Analysis: Proceedings of a Workshop Held in Groningen, the Netherlands December 15-16, 1983

ISBN-13: 9783540138938 / Angielski / Miękka / 1984 / 134 str.

T. K. Dijkstra

Opis

This volume collects papers prepared for the workshop '~isspecification Ana- lysis" held in Groningen. The Netherlands. December 15th and 16th. 1983. The papers. which cover a wide range of problems. contain a number of interesting and fruitful ideas. A bird's-eye view of the contents of the papers is as follows: White describes in a very general setting properties of classical statistical procedures when models are misspecified. Verbeek provides a lucid analysis of the statistical complexity of the model selection process. Dijkstra indicates how to construct good estimators. taking the possibility of misspecification explicitly into account. Taking the view that measured variables are never continuous. De Leeuw dis- cusses in an illuminating way whether. and how. the discreteness of the data should and can be taken more seriously. Starting from the thesis that models are at best useful approximations of reality. Van ~ag and Koster derive properties of estimators determining best fitting hyperplanes. Within the context of the errors-in-variables problem. Bekker, Xapteyn and Wansbeek provide an elegant description of the variation in estimation re- sults due to part of the modelspecification varying, while estimators are .adjusted so as to keep them consistent. Bierens develops consistent tests of the hypothesis of parameter constancy against extremely diffuse alternatives.

Spis treści

Maximum likelihood estimation of misspecified dynamic models.- The geometry of model selection in regression.- Misspecification and the choice of estimators, a heuristic approach.- Discrete normal linear regression models.- Specification in simultaneous linear equations models: the relation between a priori specifications and resulting estimators.- Measurement error and endogeneity in regression: bounds for ML and 2SLS estimates.- Testing parameter constancy of linear regressions.- Prediction performance and the number of variables in multivariate linear regression.