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Generalized moment estimation of stochastic differential equations

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Abstract

We study the semiparametric estimation of stochastic differential equations employing methods based on moment conditions, comparing the finite sample and robustness properties of generalized method of moments, empirical likelihood and minimum contrast methods using unconditional and conditional formulations of moment conditions. The results obtained indicate that the estimators proposed, particularly, the estimators based on exponential tilting, obtain better results than those of the generalized methods of moments normally used to estimate stochastic differential equations. This conclusion is mainly derived from the robustness properties of this method in the presence of problems of incorrect specification.

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Acknowledgments

We greatly appreciate the comments of the associate editor and the two anonymous referees. This research was partially supported by FAPESP and CNPq.

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Authors and Affiliations

  1. FEA-RP USP, Av. dos Bandeirantes 3900, Ribeirão Preto, São Paulo, Brazil

    Márcio Poletti Laurini

  2. Imecc-Unicamp, Campinas, Brazil

    Luiz Koodi Hotta

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  1. Márcio Poletti Laurini
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  2. Luiz Koodi Hotta
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Correspondence to Márcio Poletti Laurini.

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Laurini, M.P., Hotta, L.K. Generalized moment estimation of stochastic differential equations. Comput Stat 31, 1169–1202 (2016). https://doi.org/10.1007/s00180-015-0598-2

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