Multivariate Modelling of Non-Stationary Economic Time Series

Inbunden, Engelska, 2017

AvJohn Hunter,Simon P. Burke,Alessandra Canepa

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This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models.

Produktinformation

Utgivningsdatum2017-05-17
Mått148 x 210 x 33 mm
Vikt773 g
FormatInbunden
SpråkEngelska
SeriePalgrave Texts in Econometrics
Antal sidor502
Upplaga2
FörlagPalgrave Macmillan
ISBN9780230243309

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Mikroekonomi inom Ekonomi och ledarskap

Simon P. Burke studied econometrics at the University of Reading, UK. He has published in the International Journal of Forecasting, Journal of Financial Econometrics and The Oxford Bulletin of Economics & Statistics. He has taught econometrics, mathematics and statistics at Reading and Surrey Universities. John Hunter studied econometrics at the London School of Economics, UK, under Denis Sargan. He published recently in the International Review of Financial Analysis, Economic Modelling and developed the notion of Cointegrating Exogeneity. He taught econometrics and financial modelling at Brunel, City, Queen Mary, Southampton and Surrey. He has consulted for HM Treasury, Oftel, OFT and KPN Mobile.Alessandra Canepa studied econometrics at Southampton University, UK. She has published in Statistics & Probability Letters, the European Journal of Operational Research and Oxford Economic Papers. She currently lectures in econometrics and Risk Management at Brunel University, UK, and is a member of CARISMA in the Department of Mathematics at Brunel.

Chapter 1. Introduction: Time Series, Common Trends and Equilibrium.- Chapter 2. Multivariate Time Series.- Chapter 3. Cointegration.- Chapter 4. Testing for Cointegration: Under Standard and Non-Standard Conditions.- Chapter 5. Structure and Evaluation.- Chapter 6. Testing in VECMs with Small Sample.- Chapter 7. Heteroscedasticity and Multivariate Volatility.- Chapter 8. Models with Alternative Orders of Integration.- Chapter 9. The Structural Analysis of Time Series.