Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models / Francis X. Diebold, Til Schuermann.

By:

Diebold, Francis X

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Material type: Text

TextSeries: Technical Working Paper Series (National Bureau of Economic Research) ; no. t0194.Publication details: Cambridge, Mass. National Bureau of Economic Research 1996.Description: 1 online resource: illustrations (black and white)Subject(s):

C1 - Econometric and Statistical Methods and Methodology: General

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Abstract: The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.

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April 1996.

The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.