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The ACR Model: A Multivariate Dynamic Mixture Autoregression

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  • Frédérique Bec
  • Anders Rahbek
  • Neil Shephard

Abstract

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. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 70 (2008)
Issue (Month): 5 (October)
Pages: 583-618

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Handle: RePEc:bla:obuest:v:70:y:2008:i:5:p:583-618

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Cited by:
  1. Søren Johansen & Theis Lange, 2011. "Some econometric results for the Blanchard-Watson bubble model," CREATES Research Papers 2011-17, School of Economics and Management, University of Aarhus.
  2. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, Springer, vol. 45(1), pages 605-625, August.
  3. Koop, Gary & Potter, Simon, 2010. "A flexible approach to parametric inference in nonlinear and time varying time series models," Journal of Econometrics, Elsevier, Elsevier, vol. 159(1), pages 134-150, November.
  4. Anders Rahbek & Heino Bohn Nielsen, 2012. "Unit root vector autoregression with volatility induced stationarity," Discussion Papers 12-02, University of Copenhagen. Department of Economics.
  5. Michael Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2009. "Multivariate Contemporaneous Threshold Autoregressive Models," Department of Economics Working Papers, Universidad Torcuato Di Tella 2009-03, Universidad Torcuato Di Tella.
  6. Jiti Gao & Dag Tjøstheim & Jiying Yin, 2011. "Estimation in threshold autoregressive models with a stationary and a unit root regime," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 21/11, Monash University, Department of Econometrics and Business Statistics.
  7. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.

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