Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends
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.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1995012.
Date of creation: 01 Feb 1995
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Other versions of this item:
- Simos, Theodore, 1996. "Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends," Econometric Theory, Cambridge University Press, vol. 12(02), pages 361-373, June.
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- 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.
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