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

Theodore Simos ()

No 1995012, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Date: 1995-02-01
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Related works:
Journal Article: Gaussian Estimation of a Continuous Time Dynamic Model with Common Stochastic Trends (1996)
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