Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2014

Abstract

This paper develops a maximum likelihood (ML) method to estimate partially observed diffusion models based on data sampled at discrete times. The method combines two techniques recently proposed in the literature in two separate steps. In the first step, the closed form approach of Aït-Sahalia (2008) is used to obtain a highly accurate approximation to the joint transition probability density of the latent and the observed states. In the second step, the efficient importance sampling technique of Richard and Zhang (2007) is used to integrate out the latent states, thereby yielding the likelihood function. Using both simulated and real data, we show that the proposed ML method works better than alternative methods. The new method does not require the underlying diffusion to have an affine structure and does not involve infill simulations. Therefore, the method has a wide range of applicability and its computational cost is moderate. © 2013 Elsevier B.V. All rights reserved.

Keywords

Closed-form approximation, Diffusion model, Efficient importance sampler

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

180

Issue

1

First Page

73

Last Page

80

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2014.02.002

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.jeconom.2014.02.002

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