Infinitesimal Robustness for Diffusions
AbstractWe develop infinitesimally robust statistical procedures for general diffusion processes. We first prove existence and uniqueness of the times series influence function of conditionally unbiased M–estimators for ergodic and stationary dffusions, under weak conditions on the (martingale) estimating function used. We then characterize the robustness of M–estimators for diffusions and derive a class of conditionally unbiased optimal robust estimators. To compute these estimators, we propose a general algorithm, which exploits approximation methods for dffusions in the computation of the robust estimating function. Monte Carlo simulation shows a good performance of our robust estimators and an application to the robust estimation of the exchange rate dynamics within a target zone illustrates the methodology in a real–data application.
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Bibliographic InfoPaper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2008 with number 2008-09.
Length: 53 pages
Date of creation: Apr 2008
Date of revision:
Dffusion processes; Eigenexpansion; Influence Function; Infinitesimal Generator; M–Estimators; Saddle– point Approximation.;
Other versions of this item:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-05-24 (All new papers)
- NEP-CBA-2008-05-24 (Central Banking)
- NEP-ECM-2008-05-24 (Econometrics)
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