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The Estimation of Open Higher-Order Continuous Time Dynamic Models with Mixed Stock and Flow Data

Published online by Cambridge University Press:  18 October 2010

A. R. Bergstrom
A. R. Bergstrom
Affiliation:
University of Essex
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Abstract

This article extends recent work on the Gaussian or quasi-maximum likelihood estimation of the parameters of a closed higher-order continuous time dynamic model by introducing exogenous variables into the model The method presented yields exact maximum likelihood estimates when the innovations are Gaussian and the exogenous variables are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under more general conditions. It is applicable, in principle, to a system of any order with mixed stock and iow data. The precise formulas for its implementation are derived, in this article, for a second-order system in which both the endog-enous and exogenous variables are a mixture of stock and flow variables.

Type
Articles
Information
Econometric Theory , Volume 2 , Issue 3 , December 1986 , pp. 350 - 373
Copyright
Copyright © Cambridge University Press 1986

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References

REFERENCES

1.Agbeyegbe, T. D. The exact discrete analogue to a closed linear first order continuous time system with mixed sample. (1985).Google Scholar
2.Bergstrom, A. R.Non-recursive models as discrete approximations to systems of stochastic differential equations. Econometrica 34 (1966): 173–182.CrossRefGoogle Scholar
3.Bergstrom, A. R.Gaussian estimation of structural parameters in higher-order continuous time dynamic models. Econometrica 51 (1983): 117–152.CrossRefGoogle Scholar
4.Bergstrom, A. R. Continuous time stochastic models and issues of aggregation over time. In Griliches, Z. and Intriligator, M. D. (eds.), Handbook of Econometrics, Chapter 20 and pp. 1145–1212. Amsterdam: North Holland, 1984.CrossRefGoogle Scholar
5.Bergstrom, A. R.The estimation of parameters in nonstationary higher-order continuous-time dynamic models. Econometric Theory 1 (1985): 369–385.CrossRefGoogle Scholar
6.R., Bergstrom. A. and Wymer., C. R. A model of disequilibrium neoclassical growth and its application to the United Kingdom. In Bergstrom, A. R. (ed.), Statistical Inference in Continuous Time Economic Models, Chapter 10 and pp. 267–327. Amsterdam: North Holland, 1976.Google Scholar
7.Golub, G. H. and Loan., C. F. vanMatrix Computations Baltimore:John Hopkins University Press, 1983.Google Scholar
8.Harvey, A. C. and Stock., J. H.Th e estimation of higher-order continuous time autoregres-sive models. Econometric Theory 1 (1985): 97–117.CrossRefGoogle Scholar
9.Phillips, P.C.B.The structural estimation of a stochastic differential equation system.Econometrica 40 (1972): 1021–1041.CrossRefGoogle Scholar
10.Phillips, P.C.B.The estimation of some continuous time models. Econometrica 42 (1974): 803–824.CrossRefGoogle Scholar
11.Phillips, P.C.B. The estimation of linear stochastic differential equations with exogenous variables. In Bergstrom, A. R. (ed.), Statistical Inference in Continuous Time Economic Models, Chapter 7 and pp. 135–173. Amsterdam: Nort h Holland 1976.Google Scholar
12.Phillips, P.C.B. Some computations based on observed data series of the exogenous variable component of continuous systems. In Bergstrom, A. R. (ed.) Statistical Inference in Continuous Time Economic Models, Chapter 8 and pp. 174–214. Amsterdam: North Holland, 1976.Google Scholar
13.Robinson, P. M. Fourier estimation of continuous time models. In Bergstrom, A. R. (ed.)Statistical Inference in Continuous Time Economic Models, Chapter 9 and pp. 215–266. Amsterdam: North Holland, 1976.Google Scholar
14.Robinson, P. M.The estimation of linear differential equations with constant coefficients.Econometrica 44 (1976): 751–764.CrossRefGoogle Scholar
15.Robinson, P. M.Instrumenta l variables estimation of differential equations. Econometrica 44 (1976): 765–776.CrossRefGoogle Scholar
16.Robinson, P. M.The construction and estimation of continuous time models and discrete approximations in econometrics. Journal of Econometrics 6 (1977): 173–198.CrossRefGoogle Scholar
17.Sargan, J. D. Some discrete approximations to continuous time stochastic models. In Bergstrom, A. R. (ed.), Statistical Inference in Continuous Time Economic Models, Chapter 3 and pp. 27–80. Amsterdam: North Holland, 1976.Google Scholar
18.Wymer, C. R.Econometric estimation of stochastic differential equatio n systems. Econometrica 40 (1972): 565–577.CrossRefGoogle Scholar