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

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  • SIMOS , Theodore

    (CORE, Université catholique de Louvain, Louvain-la-Neuve, B-1348 Belgium.)

Abstract

\Ve consider the estimation of a first order system of linear stochastic differential equations driven by an observable vector of stochastic trends and a vector of stationary innovations. vVe derive both the exact discrete model and the Gaussian likelihood function in the case the system comprises stock and flow variables and is observed at equispaced points in time.

Suggested Citation

  • SIMOS , Theodore, 1995. "Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends," LIDAM Discussion Papers CORE 1995012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1995012
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    Cited by:

    1. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    2. Chambers, Marcus J., 1999. "Discrete time representation of stationary and non-stationary continuous time systems," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 619-639, February.
    3. Michael A. Thornton & Marcus J. Chambers, 2013. "Continuous-time autoregressive moving average processes in discrete time: representation and embeddability," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 552-561, September.

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