Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/87103 
Erscheinungsjahr: 
2004
Schriftenreihe/Nr.: 
Quaderni di Dipartimento - EPMQ No. 171
Verlag: 
Università degli Studi di Pavia, Dipartimento di Economia Politica e Metodi Quantitativi (EPMQ), Pavia
Zusammenfassung: 
This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most effcient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogeneous processes. Some Monte Carlo experiments evaluate its performance against an alternative IS strategy (Durham and Gallant, 2002), showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.
Schlagwörter: 
Diffusion process
Stochastic differential equation
Transition density
Importance sampling
Simulated maximum likelihood
JEL: 
C13
C15
C22
Dokumentart: 
Working Paper

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