Parametric inference for discretely sampled stochastic differential equations
AbstractA review is given of parametric estimation methods for discretely sampled mul- tivariate diffusion processes. The main focus is on estimating functions and asymp- totic results. Maximum likelihood estimation is briefly considered, but the emphasis is on computationally less demanding martingale estimating functions. Particular attention is given to explicit estimating functions. Results on both fixed frequency and high frequency asymptotics are given. When choosing among the many estima- tors available, guidance is provided by simple criteria for high frequency efficiency and rate optimality that are presented in the framework of approximate martingale estimating functions.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-18.
Date of creation: 04 Apr 2008
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Asymptotic results; discrete time observation of a diffusion; efficiency; eigenfunctions; explicit inference; generalized method of moments; likelihood infer- ence; martingale estimating functions; high frequency asymptotics; Pearson diffu- sions.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-06-27 (All new papers)
- NEP-ECM-2008-06-27 (Econometrics)
- NEP-ETS-2008-06-27 (Econometric Time Series)
- NEP-MST-2008-06-27 (Market Microstructure)
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