International Statistical Review · 2004 · 176 citations · 104 references
BusinessBiostatisticsStatistical ModelingStochastic ModelingStochastic ProcessesBayesian InferenceBayesian StatisticsDiffusion ProcessesMaximum Likelihood EstimationMonte Carlo MethodsStatistical InferenceBayesian AnalysisDiffusion ModelsDiffusionsErgodic ProcessesIndirect InferenceInference For Diffusion ProcessesLikelihood MaximizationStationary Processes Estimation
Summary This paper is a survey of estimation techniques for stationary and ergodic diffusion processes observed at discrete points in time. The reader is introduced to the following techniques: (i) estimating functions with special emphasis on martingale estimating functions and so‐called simple estimating functions; (ii) analytical and numerical approximations of the likelihood function which can in principle be made arbitrarily accurate; (iii) Bayesian analysis and MCMC methods; and (iv) indirect inference and EMM which both introduce auxiliary (but wrong) models and correct for the implied bias by simulation.
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