Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-27T18:43:53.352Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction, Extraction, and Estimation in Unobserved Components Model

Published online by Cambridge University Press:  18 October 2010

Francis X. Diebold  and
Marc Nerlove
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Other
Information
Econometric Theory , Volume 4 , Issue 2 , August 1988 , pp. 356 - 359
Copyright
Copyright © Cambridge University Press 1988 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Anderson, B.D.O. & Moore, J.B.. Optimal Filtering. Englewood Cliffs, N.J.: Prentice Hall, 1979.Google Scholar
2. Ballakrishnan, A.V. Elements of State Space Theory of Systems. New York: Springer Verlag, 1983.Google Scholar
3. Diderrich, G.T. The Kalman filter from the perspective of Goldberger-Theil estimators. American Statistician, 39 (1985): 193–198.Google Scholar
4. Diebold, F.X. The exact initial covariance matrix of the state vector of a general MA(c) process. Forthcoming, Economics Letters (1986).10.1016/0165-1765(86)90137-0Google Scholar
5. Diebold, F.X. Exact maximum-likelihood estimation of autoregressive models via the Kalman filter. Forthcoming, Economics Letters (1986).10.1016/0165-1765(86)90231-4Google Scholar
6. Engle, R.F. & Watson, M.. The Kalman filter: applications to forecasting and rational-expectations models, in Advances in Econometrics-Fifth World Congress, Vol. I, edited by Truman, F., Bewley, Cambridge University Press.Google Scholar
7. Gardner, G., Harvey, A.C. & Phillips, G.D.A.. An algorithm for exact maximum-likelihood estimation of ARMA models by means of the Kalman filter. Applied Statistics 29 (1980): 311–322.Google Scholar
8. Granger, C.W.J. & Morris, M.J.. Time series modelling and interpretation. Journal of the Royal Statistical Society, Series A, 139 (1976): 246–257.10.2307/2345178Google Scholar
9. Harvey, A.C. Time Series Models. New York: Wiley, 1980.Google Scholar
10. Kalman, R.E. A new approach to linear filtering and prediction problems. Trans. American Society of Mechanical Engineering, Journal of Basic Engineering 82 (1960): 35–45.Google Scholar
11. Meinhold, R.J. & Singpurwalla, N.D.. Understanding the Kalman filter. American Statistician, 37 (1983): 123–127.Google Scholar
12. Nerlove, M., Grether, D.M., & Carvalo, J.L.. Analysis of Economic Time Series: A Synthesis. New York: Academic Press, 1979.Google Scholar
13. Schweppe, F. Evaluation of likelihood functions for Gaussian signals. IEEE Transactions on Information Theory, 11 (1965): 61–70.10.1109/TIT.1965.1053737Google Scholar
14. Watson, M. & Engle, R.F.. Alternative algorithms for the estimation of dynamic factor, MIMIC and varying coefficient regression models. Journal of Econometrics, 23 (1983): 385–400.Google Scholar
15. Whittle, P. (1963) Prediction and Regulation by Linear Least-Squared Methods, 2nd Ed., 1983. Minneapolis: University of Minnesota Press, 1963.Google Scholar