An algorithm to generate samples with approximate first-, second-, and third-order moments is presented extending the Cholesky matrix decomposition to a Cholesky tensor decomposition of an arbitrary order. The tensor decomposition of the first-, second-, and third-order objective moments...

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the...

Atheoretical regression trees (ART) are applied to detect changes in the mean of a stationarylong memory time series when location and number are unknown. It is shownthat the BIC, which is almost always used as a pruning method, does not operate well inthe long memory framework.[...]

This Monte-Carlo study investigates sensitivity of the Wilcoxon signed rank test to certainassumption violations in small samples. Emphasis is put on within-sample-dependence,between-sample dependence, and the presence of ties. Our results show that both assumptionviolations induce severe size...