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A Flexible State Space Model and its Applications

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  • Qian, Hang
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    Abstract

    The standard state space model (SSM) treats observations as imprecise measures of the Markov latent states. Our flexible SSM treats the states and observables symmetrically, which are simultaneously determined by historical observations and up to first-lagged states. The only distinction between the states and observables is that the former are latent while the latter have data. Despite the conceptual difference, the two SSMs share the same Kalman filter. However, when the flexible SSM is applied to the ARMA model, mixed frequency regression and the dynamic factor model with missing data, the state vector is not only parsimonious but also intuitive in that low-dimension states are constructed simply by stacking all the relevant but unobserved variables in the structural model.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 38455.

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    Date of creation: Apr 2012
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    Handle: RePEc:pra:mprapa:38455

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    Keywords: State Space Model; Kalman Filter; ARMA; Mixed Frequency; Factor Model;

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    1. Jungbacker, B. & Koopman, S.J. & van der Wel, M., 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(8), pages 1358-1368, August.
    2. Marc Hallin & Mario Forni & Marco Lippi & Lucrezia Reichlin, 2003. "Do financial variables help forecasting inflation and real activity in the Euro area ?," ULB Institutional Repository 2013/2123, ULB -- Universite Libre de Bruxelles.
    3. Namwon Hyung & Clive W.J. Granger, 2008. "Linking series generated at different frequencies This work is part of a PhD dissertation presented at the University of California, San Diego (1999)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 95-108.
    4. Joshua C.C. Chan & Garry Koop & Roberto Leon Gonzales & Rodney W. Strachan, 2010. "Time Varying Dimension Models," ANU Working Papers in Economics and Econometrics 2010-523, Australian National University, College of Business and Economics, School of Economics.
    5. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
    6. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
    7. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank, Research Centre.
    8. Roberto S. Mariano & Yasutomo Murasawa, 2010. "A Coincident Index, Common Factors, and Monthly Real GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 27-46, 02.
    9. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    10. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
    11. Olivier Basdevant, 2003. "On applications of state-space modelling in macroeconomics," Reserve Bank of New Zealand Discussion Paper Series DP2003/02, Reserve Bank of New Zealand.
    12. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
    13. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    14. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
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    Cited by:
    1. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.

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