Pasricha, Gurnain Kaur (2006): Kalman Filter and its Economic Applications.
Preview |
PDF
MPRA_paper_22734.pdf Download (166kB) | Preview |
Abstract
This paper is an eclectic study of the uses of the Kalman filter in existing econometric literature. An effort is made to introduce the various extensions to the linear filter first developed by Kalman(1960) through examples of their uses in economics. The basic filter is first derived and then some applications are reviewed.
Item Type: | MPRA Paper |
---|---|
Original Title: | Kalman Filter and its Economic Applications |
Language: | English |
Keywords: | Kalman Filter; Time-varying Parameters; Stochastic Volatility; Markov Switching |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General B - History of Economic Thought, Methodology, and Heterodox Approaches > B4 - Economic Methodology > B41 - Economic Methodology |
Item ID: | 22734 |
Depositing User: | Gurnain Pasricha |
Date Deposited: | 17 May 2010 13:44 |
Last Modified: | 26 Sep 2019 17:37 |
References: | Anderson, Brian D.O. and J.B. Moore (1979), Optimal Filtering, Prentice Hall, New Jersey. Bahmani, Osokee and Ford Brown (2004), Kalman Filter Approach to Estimate the Demand for International Reserves, Applied Economics, 36(15), 1655-1668 Broto, Carmen and Esther Ruiz (2004), Estimation Methods for Stochastic Volatility Models: A Survey, Journal of Economic Surveys, 18(5), 613-37 Cheung, Yin-Wong (1993), Exchange Rate Risk Premiums, Journal of International Money and Finance, 12, 182-194. Ghysels,E., Harvey, A.C. and Eric Renault (1996), Stochastic Volatility. in Maddala, G.S. and C.R. Rao, eds., Handbook of Statistics, Vol 14. Harrison, P.J. and C.F. Stevens (1976), Bayesian Forecasting Journal of the Royal Statistical Society, Series B, 38, 205-247. Harvey, A.C.(1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press. Harvey, A.C., Ruiz, E. and N.G. Shephard (1994), Multivariate Stochastic Variance Models. Review of Economic Studies, 61, 247-264. Kalman, R.E. (1960), A New Approach to Linear Filtering and Problems, Journal of Basic Engineering 82, 35-45. Kalman, R. E. (1963), New Methods in Wiener Filtering Theory, in John L. Bogdanoff and Frank Kozin Eds., Proceedings of the First Symposium On Engineering Applications of Random Function Theory and Probability, New York: John Wiley and Sons. Kim,C-J and Charles R. Nelson (1999), State-Space Models with Regime Switching: Classical and Gibbs Sampling Approaches with Applications, MIT Press. Maybeck, Peter S.(1979), Stochastic Models, Estimation and Control, Vol I, Academic Press. Meinhold, Richard J. and N.D. Singpurwalla(1983), Understanding the Kalman Filter, The American Statistician, 37(2), 123-127. Nelson, D.B.(1988), The Time Series Behaviour of Stock Market Volatility and Returns. (Unpublished PhD dissertaion, Massachusetts Institute of Technology). Ozbek, L. and Umit Ozale(2005), Employing the Extended Kalman Filter in Measuring the Output Gap , Journal of Economic Dynamics and Control, 29, 1611-22. Tanizaki, Hisashi (1993), Non-linear Filters: Estimation and Applications, Lecture Notes in Economics and Mathematical Systems, Springer Verlag. Taylor, S. (1986), Modeling Financial Time Series, Chichester:Wiley. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/22734 |