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Prediction, Extraction, and Estimation in Unobserved Components Model

Published online by Cambridge University Press:  18 October 2010

Francis X. Diebold  and
Marc Nerlove
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Econometric Theory , Volume 4 , Issue 2 , August 1988 , pp. 356 - 359
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Copyright © Cambridge University Press 1988 

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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