Stochastic Parameter Regression Models

by Paul Newbold, Theodore Bos

Overview

This excellent introduction to stochastic parameter regression models is more advanced and technically difficult than other papers in this series. These models allow relationships to vary through time, rather than requiring them to be fixed, without forcing the analyst to specify and analyze the causes of the time-varying relationships. This volume will be most useful to those with a good working knowledge of standard regression models and who wish to understand methods which deal with relationships that vary slowly over time, but for which the exact causes of variation cannot be identified.

Product Details

ISBN-13:	9780803924253
Publisher:	SAGE Publications
Publication date:	05/01/1985
Series:	Quantitative Applications in the Social Sciences , #51
Edition description:	New Edition
Pages:	80
Product dimensions:	5.50(w) x 8.50(h) x 0.20(d)

About the Author

Paul Newbold was born in England in 1945. In 1966 he obtained a BSc in Economics at the London School of Economics, before continuing to study for a Ph D in Statistics at the University of Wisconsin. He worked under the supervision of George
Box, and was awarded his Ph D in 1970. His first academic posts were at the University of Nottingham, where he spent time in both the Department of Economics and the Department of Mathematics. From 1979-1994 he was Professor at the University of Illinois, before returning to the University of Nottingham in 1994 as Professor of Econometrics. Paul Newbold has had a large influence on the discipline of time series econometrics, particularly in the areas of non-stationary time series, forecasting, and univariate time series analysis. He has published extensively in journals such as Journal of Econometrics, Journal of Business and Economic Statistics, Journal of the American Statistical Association, Biometrika, and Econometric
Theory. He retired in 2006 and is now Emeritus Professor of Econometrics.

Table of Contents

Introduction and Preliminaries
Estimation and Prediction
Some Tests of Hypotheses
Testing for Efficient Capital Markets