TY - JOUR
T1 - The ACR Model
T2 - A Multivariate Dynamic Mixture Autoregression
AU - Bec, Frederique
AU - Rahbek, Anders Christian
AU - Shephard, Neil
N1 - JEL classification: C13, C32, F31
PY - 2008
Y1 - 2008
N2 - This paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov switching class of models, which are commonly used to describe nonlinear dynamics as implied by arbitrage in presence of transaction costs. Simple conditions on the parameters of the ACR process and its innovations are shown to imply geometric ergodicity, stationarity and existence of moments. Furthermore, consistency and asymptotic normality of the maximum likelihood estimators are established. An application to real exchange rate data illustrates the analysis.
AB - This paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov switching class of models, which are commonly used to describe nonlinear dynamics as implied by arbitrage in presence of transaction costs. Simple conditions on the parameters of the ACR process and its innovations are shown to imply geometric ergodicity, stationarity and existence of moments. Furthermore, consistency and asymptotic normality of the maximum likelihood estimators are established. An application to real exchange rate data illustrates the analysis.
U2 - 10.1111/j.1468-0084.2008.00512.x
DO - 10.1111/j.1468-0084.2008.00512.x
M3 - Journal article
SN - 0305-9049
VL - 70
SP - 583
EP - 618
JO - Oxford Bulletin of Economics and Statistics
JF - Oxford Bulletin of Economics and Statistics
IS - 5
ER -