Fully Nonparametric Estimation of Scalar Diffusion Models

Federico M. Bandi and Peter Phillips
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Federico M. Bandi: GSB, University of Chicago

No 1332, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens (that is, we implement both infill and long span asymptotics). We prove consistency and convergence to mixtures of normal laws, where the mixing variates depend on the chronological local time of the underlying process, that is the time spent by the process in the vicinity of a spatial point. The estimation method and asymptotic results apply to both stationary and nonstationary processes.

Keywords: Diffusion; Drift; Infill asymptotics; Kernel density; Local time; Long span asymptotics; Martingale; Nonparametric estimation; Semimartingale; Stochastic differential equation (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2001-09
New Economics Papers: this item is included in nep-ecm, nep-ent, nep-ets and nep-net
Note: CFP 1050.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published in Econometrica (2003), 71(1): 241-283

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