Multivariate Modelling of Non-Stationary Economic Time Series
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Focuses on the multivariate nature of the problem of modelling non-stationary economic time series Handles recent developments in Time Series Analysis Has relevance for aspects of regulation and competition policy
Autor*in
John Hunter
Themen in »Multivariate Modelling of Non-Stationary Economic Time Series«
econometrics economics modelling stationary data conventional time series impulse responses small sample correction volatililty integration Singular Spectrum Analysis (SSA) Kalman Filter Structural Time Series cointegration