Estimation of Models with Variable Coefficients

Datos Bibliográficos

ID7971016
AutoresJ E Jackson (0000-0001-6629-4660, autor de correspondencia)
Año1991
Volumen3
Páginas27-49
Fecha de publicación1991-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaPolitical Analysis (JOURNAL)
Identificadores de la revistaISSN: 1047-1987 • E-ISSN: 1476-4989
EditorialCambridge University Press (CUP) (PUBLISHER)
DOI10.1093/pan/3.1.27
OpenAlexW2063319381
IdiomaEN
Citas recibidas14
Referencias citadas6

The ordinary least squares (OLS) estimator gives biased coefficient estimates if coefficients are not constant for all cases but vary systematically with the explanatory variables. This article discusses several different ways to estimate models with systematically and randomly varying coefficients using estimated generalized least squares and maximum likelihood procedures. A Monte Carlo simulation of the different methods is presented to illustrate their use and to contrast their results to the biased results obtained with ordinary least squares. Several applications of the methods are discussed and one is presented in detail. The conclusion is that, in situations with variables coefficients, these methods offer relatively easy means for overcoming the problems

Contrast (vision · Econometrics · Estimation · Estimator · Generalized least squares · Least-squares function approximation · Monte Carlo method · Ordinary least squares · Statistics · Variable (mathematics · Variables · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Water Quality and Resources Studies · Applied Mathematics