Estimating the wrapped stable distribution via indirect inference

Marco Bee

No 2018/11, DEM Working Papers from Department of Economics and Management

Abstract: The wrapped stable distribution is a model for non-symmetric circular data. Maximum likelihood estimation is feasible, but computationally expensive and not exact, because the density does not exist in closed form. In light of these difficulties, we develop a constrained indirect inference approach based on a skewed-t auxiliary model. To improve the finite-sample properties of the estimators, we devise a bootstrap-based estimate of the weighting matrix employed in the indirect inference program. The simulation study suggests that the indirect inference estimators are definitely preferable to the maximum likelihood estimators as concerns computing time, and approximately equivalent in terms of root-mean-squared-error.

Keywords: Indirect inference; directional statistics; stable distribution; weighting matrix (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm
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