Elsevier

Journal of Econometrics

Volume 66, Issues 1–2, March–April 1995, Pages 251-287
Journal of Econometrics

Nonparametric estimation of structural models for high-frequency currency market data☆

https://doi.org/10.1016/0304-4076(94)01618-AGet rights and content
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Abstract

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975–90.

Keywords

Monetary model
Calibration
Simulation estimator
Exchange rates
Nonparametric

JEL classification

C51

Cited by (0)

☆

This material is based upon work supported by the National Science Foundation under Grants No. SES-9111867 and SES-9023083. We thank Geert Bekaert, Lars Hansen, David Hsieh, Ellen McGrattan, Tom Sargent, and many seminar and conference participants for helpful comments at various stages of this research.

Copyright © 1995 Published by Elsevier B.V.