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Multivariate Modelling of Non-Stationary Economic Time Series

:	John Hunter, Simon P. Burke, Alessandra Canepa
:	Multivariate Modelling of Non-Stationary Economic Time Series
:	Palgrave Macmillan
:	9781137313034
:	2
:	CHF 57.00
:

:	Allgemeines, Lexika
:	English

:	508
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.

Simon P. Burke studied econometrics at the University of Reading, UK. He has published in the International Journal of Forecasting,Journal of Financial EconometricsandThe 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 . 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.

	Preface	6
	Contents	9
	1 Introduction	14
	References	29
	2 Multivariate Time Series	33
	2.1 Introduction	33
	2.2 Stationarity	34
	2.2.1 Strict Stationarity	35
	2.2.2 Strict (Joint Distribution) Stationarity	36
	2.2.3 Describing Covariance Non-Stationarity: Parametric Models	36
	2.2.4 The White Noise Process	37
	2.2.4.1 White Noise	37
	2.2.5 The Moving Average Process	38
	2.2.6 Wold's Representation Theorem	40
	2.2.7 The Autoregressive Process	40
	2.2.8 Lag Polynomials and Their Roots	41
	2.2.8.1 The Lag Operator and Lag Polynomials	41
	2.2.9 Non-Stationarity and the Autoregressive Process	43
	2.2.9.1 Stationarity of an Autoregressive Process	43
	2.2.10 The Random Walk and the Unit Root	43
	2.2.10.1 The Random Walk Process	43
	2.2.10.2 Differencing and Stationarity	44
	2.2.10.3 The Random Walk as a Stochastic Trend	45
	2.2.10.4 The Random Walk with Drift	47
	2.2.11 The Autoregressive Moving Average Process and Operator Inversion	47
	2.2.11.1 Illustration of Operator Inversion	49
	2.2.12 Testing Stationarity in Single Series	50
	2.2.12.1 Reparameterizing the Autoregressive Model	50
	2.2.12.2 Semi-parametric Methods	52
	2.3 Multivariate Time Series Models	54
	2.3.1 The VAR and VECM Models	54
	2.3.2 The VMA Model	56
	2.3.3 Estimation	57
	2.3.4 The Procedure	59