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Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends

Published online by Cambridge University Press:  11 February 2009

Theodore Simos
Theodore Simos
Affiliation:
Université Catholique de Louvain
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Abstract

We derive the exact discrete model and the Gaussian likelihood function of a first-order system of linear stochastic differential equations driven by an observable vector of stochastic trends and a vector of stationary innovations.

Type
Research Article
Information
Econometric Theory , Volume 12 , Issue 2 , June 1996 , pp. 361 - 373
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Agbeyegbe, T.D. (1987) An exact discrete analogue to a closed linear first order continuous time system with mixed sample. Econometric Theory 3, 143–149.CrossRefGoogle Scholar
Agbeyegbe, T.D. (1988) An exact discrete analogue of an open linear non-stationary first order continuous time system with mixed sample. Journal of Econometrics 39, 237–250.Google Scholar
Bergstrom, A.R. (1983) Gaussian estimation of structural parameters in higher order continuous time dynamic models. Econometrica 51, 117–152.CrossRefGoogle Scholar
Bergstrom, A.R. (1984) Continuous time stochastic models and issues of aggregation over time. In Griliches, Z. & Intriligator, M.D. (eds.), Handbook of Econometrics, vol. 2, ch. 20, pp. 1145–1211. Amsterdam: North-Holland.CrossRefGoogle Scholar
Bergstrom, A.R. (1985) The estimation of parameters in non-stationary higher order continuous time dynamic models. Econometric Theory 1, 369–386.CrossRefGoogle Scholar
Bergstrom, A.R. (1986) The estimation of open higher order continuous time dynamic models with mixed stock and flow data. Econometric Theory 2, 350–373.CrossRefGoogle Scholar
Engle, R.F. & Granger, C.W.J. (1987) Cointegration and error correction: Representation, estimation and testing. Econometrica 55, 251–276.CrossRefGoogle Scholar
Harvey, A.C. & Stock, J.H. (1985) The estimation of higher order continuous time autoregres-sive models. Econometric Theory 1, 97–117.CrossRefGoogle Scholar
Harvey, A.C. & Stock, J.H. (1988) Continuous time autoregressive models with common stochastic trends. Journal of Economics Dynamics and Control 12, 365–384.CrossRefGoogle Scholar
Phillips, P.C.B. (1991a) Optimal inference in cointegrated systems. Econometrica 59, 283–306.CrossRefGoogle Scholar
Phillips, P.C.B. (1991b) Error correction and long run equilibrium in continuous time. Econometrica 59, 967–980.CrossRefGoogle Scholar
Robinson, P.M. (1977) The construction and estimation of continuous time models and discrete approximations in econometrics. Journal of Econometrics 6, 173–198.CrossRefGoogle Scholar
Robinson, P.M. (1993) Continuous time models in econometrics. In Phillips, P.C.B. (ed.), Models, Methods, and Applications of Econometrics, ch. 7, pp. 71–90. Oxford: Blackwell.Google Scholar
Rozanov, Y.A. (1967) Stationary Random Processes. San Francisco: Holden Day.Google Scholar
Yaglom, A.M. (1962) An Introduction to the Theory of Stationary Random Functions. Engle-wood Cliffs, NJ: Prentice-Hall.Google Scholar