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An Exact Discrete Analog to a Closed Linear First-Order Continuous-Time System with Mixed Sample

Published online by Cambridge University Press:  11 February 2009

Abstract

This article deals with the derivation of the exact discrete model that corresponds to a closed linear first-order continuous-time system with mixed stock and flow data. This exact discrete model is (under appropriate additional conditions) a stationary autoregressive moving average time series model and may allow one to obtain asymptotically efficient estimators of the parameters describing the continuous-time system.

Type
Brief Report
Copyright
Copyright © Cambridge University Press 1987

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References

REFERENCES

1. Bergstrom, A. R. Gaussian estimation of structural parameters in higher-order continuous time dynamic models. Econometrica 51 (1983): 117152.10.2307/1912251CrossRefGoogle Scholar
2. Bergstrom, A. R. Continuous time models and issues of aggregation over time. In Grilicbes, Z. and Intriligator, M. D. (eds.) Handbook of Econometrics, Chapter 20 and pp 1145–1212, Amsterdam: North-Holland, 1984.Google Scholar
3. Bergstrom, A. R. The estimation of parameters in non-stationary higher-order continuous time dynamic models. Econometric Theory 1 (1985): 370385.10.1017/S0266466600011269CrossRefGoogle Scholar
4. Bergstrom, A. R. The estimation of open higher-order continuous time dynamic models with mixed stock and flow data, Essex Economics Department Discussion Paper No. 277, 1986.10.1017/S026646660001166XCrossRefGoogle Scholar
5. Phillips, P.C.B. The treatment of flow data in the estimation of continuous time systems, in Bergstrom, A. R. et al. (eds.) Stability and Inflation, Wiley, 1978.Google Scholar
6. Rozanov, Y. A. Stationary Random Processes, Holden-Day, 1967.Google Scholar