๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Modelling Non-Stationary Economic Time Series: A Multivariate Approach

โœ Scribed by Simon P. Burke, John Hunter


Publisher
Palgrave Macmillan
Year
2005
Tongue
English
Leaves
263
Series
Palgrave Texts in Econometrics
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.

โœฆ Table of Contents


Cover......Page 1
Contents......Page 6
Preface......Page 8
1 Introduction: Cointegration, Economic Equilibrium and the Long Run......Page 10
2 Properties of Univariate Time Series......Page 17
3 Relationships Between Non-Stationary Time Series......Page 47
4 Multivariate Time Series Approach to Cointegration......Page 78
5 Exogeneity and Identification......Page 137
6 Further Topics in the Analysis of Non-Stationary Time Series......Page 168
7 Conclusion: Limitations, Developments and Alternatives......Page 209
Notes......Page 212
Appendix A: Matrix Preliminaries......Page 224
Appendix B: Matrix Algebra for Engle and Granger (1987) Representation......Page 226
Appendix C: Johansenโ€™s Procedure as a Maximum Likelihood Procedure......Page 228
Appendix D: The Maximum Likelihood Procedure in Terms of Canonical Correlations......Page 232
Appendix E: Distribution Theory......Page 234
Appendix F: Estimation under General Restrictions......Page 244
Appendix G: Proof of Identification based on an Indirect Solution......Page 246
Appendix H: Generic Identification of Long-Run Parameters in Section 5.5......Page 248
References......Page 249
Index......Page 259

โœฆ Subjects


ะœะฐั‚ะตะผะฐั‚ะธะบะฐ;ะขะตะพั€ะธั ะฒะตั€ะพัั‚ะฝะพัั‚ะตะน ะธ ะผะฐั‚ะตะผะฐั‚ะธั‡ะตัะบะฐั ัั‚ะฐั‚ะธัั‚ะธะบะฐ;ะขะตะพั€ะธั ัะปัƒั‡ะฐะนะฝั‹ั… ะฟั€ะพั†ะตััะพะฒ;


๐Ÿ“œ SIMILAR VOLUMES


Modelling Non-Stationary Economic Time S
โœ Simon P. Burke, John Hunter ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Palgrave Macmillan ๐ŸŒ English

Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such

Modelling Non-Stationary Time Series: A
โœ Simon P. Burke, John Hunter (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Palgrave Macmillan UK ๐ŸŒ English

<p>Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues suc

Multivariate Modelling of Non-Stationary
โœ John Hunter, Simon P. Burke, Alessandra Canepa (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2017 ๐Ÿ› Palgrave Macmillan UK ๐ŸŒ English

<p><p>This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, consi

Forecasting Non-Stationary Economic Time
โœ Michael P. Clements, David F. Hendry ๐Ÿ“‚ Library ๐Ÿ“… 1999 ๐Ÿ› The MIT Press ๐ŸŒ English

Economies evolve and are subject to sudden shifts precipitated by legislative changes, economic policy, major discoveries, and political turmoil. Macroeconometric models are a very imperfect tool for forecasting this highly complicated and changing process. Ignoring these factors leads to a wide dis

Forecasting economic time series using l
โœ Tina Loll ๐Ÿ“‚ Library ๐Ÿ“… 2012 ๐Ÿ› Peter Lang ๐ŸŒ English

Stationarity has always played an important part in forecasting theory. However, some economic time series show time-varying autocovariances. The question arises whether forecasts can be improved using models that capture such a time-varying second-order structure. One possibility is given by autore