A Flexible State Space Model and its Applications
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.
|Date of creation:||Apr 2012|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joshua C.C. Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2012.
"Time Varying Dimension Models,"
Journal of Business & Economic Statistics,
Taylor & Francis Journals, vol. 30(3), pages 358-367, January.
- Joshua C C Chan & Gary Koop & Roberto Leon-Gonzales & Rodney W Strachan, 2011. "Time Varying Dimension Models," CAMA Working Papers 2011-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- 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.
- Joshua Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2011. "Time Varying Dimension Models," Working Papers 1116, University of Strathclyde Business School, Department of Economics.
- Joshua C.C. Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2010. "Time Varying Dimension Models," Working Paper Series 44_10, The Rimini Centre for Economic Analysis.
- Chan, Joshua C C & Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W, 2010. "Time Varying Dimension Models," SIRE Discussion Papers 2012-33, Scottish Institute for Research in Economics (SIRE).
- 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.
- Schumacher, Christian, 2005. "Forecasting German GDP using alternative factor models based on large datasets," Discussion Paper Series 1: Economic Studies 2005,24, Deutsche Bundesbank, Research Centre.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003.
"Do financial variables help forecasting inflation and real activity in the euro area?,"
Journal of Monetary Economics,
Elsevier, vol. 50(6), pages 1243-1255, September.
- 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.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?," CEPR Discussion Papers 3146, C.E.P.R. Discussion Papers.
- 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.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
- 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.
- 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.
- 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., vol. 18(4), pages 427-443.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, December.
- Tom Doan, "undated". "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- 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.
- Eickmeier, Sandra, 2007. "Business cycle transmission from the US to Germany--A structural factor approach," European Economic Review, Elsevier, vol. 51(3), pages 521-551, April.
- 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.
- 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.
- 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, vol. 35(8), pages 1358-1368, August.
- B. Jungbacker & S.J. Koopman & M. Van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
- 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.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:38455. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.