Efficient Importance Sampling Maximum Likelihood Estimation of Stochastic Differential Equations

Computational Statistics & Data Analysis, Vol. 54, No. 11, pp. 2753-2762, November 2010

Posted: 14 Apr 2011

See all articles by Eduardo Rossi

Eduardo Rossi

Department of Economics and Management

Sergio Pastorello

University of Bologna - Department of Economics

Date Written: November 1, 2010

Abstract

Maximum likelihood estimation (MLE) of stochastic differential equations (SDEs) is difficult because in general the transition density function of these processes is not known in closed form, and has to be approximated somehow. An approximation based on efficient importance sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diffusion processes, evaluate its performance against an alternative importance sampling (IS) strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater flexibility needed when examining more complicated models.

Keywords: Diffusion process, Stochastic differential equation, Transition density, Importance sampling, Simulated maximum likelihood

JEL Classification: C13, C15, C22

Suggested Citation

Rossi, Eduardo and Pastorello, Sergio, Efficient Importance Sampling Maximum Likelihood Estimation of Stochastic Differential Equations (November 1, 2010). Computational Statistics & Data Analysis, Vol. 54, No. 11, pp. 2753-2762, November 2010 , Available at SSRN: https://ssrn.com/abstract=1809016

Eduardo Rossi (Contact Author)

Department of Economics and Management ( email )

Via San Felice 5
27100 Pavia
Italy
++ (Phone)

Sergio Pastorello

University of Bologna - Department of Economics ( email )

Via Saragozza, 8
Bologna, 40125
Italy
+39 051 2098144 (Phone)
+39 051 2098040 (Fax)

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