Stochastic Parameter Regression Models: Quantitative Applications in the Social Sciences, cartea 51
Autor Paul Newbold, Theodore Bosen Limba Engleză Paperback – 29 aug 1985
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Specificații
ISBN-13: 9780803924253
ISBN-10: 0803924259
Pagini: 80
Dimensiuni: 140 x 216 x 5 mm
Greutate: 0.1 kg
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 0803924259
Pagini: 80
Dimensiuni: 140 x 216 x 5 mm
Greutate: 0.1 kg
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
Cuprins
Introduction and Preliminaries
Estimation and Prediction
Some Tests of Hypotheses
Testing for Efficient Capital Markets
Estimation and Prediction
Some Tests of Hypotheses
Testing for Efficient Capital Markets
Notă biografică
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 PhD in Statistics at the University of Wisconsin. He worked under the supervision of George
Box, and was awarded his PhD 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.
Descriere
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.