Julie Lyng Forman Statistical Inference from Diffusion Driven Models
نویسنده
چکیده
Least squares estimators are developed for the parameters in the autocorrelation function of a stationary process. Regularity conditions for consistency and asymptotic normality are given, and optimal weights are derived. It is shown how goodness of fit and model selection can be based on the distance between empirical and fitted autocorrelations. Examples of sums of Ornstein-Uhlenbeck type processes and sums of linear drift diffusions are studied in greater detail. The performance of the estimators and the goodness of fit test is evaluated through Monte Carlo simulations.
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