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- W4294311565 abstract "We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this class, we propose the Generalized Covariance (GCov) estimator, which is obtained by minimizing a residual-based multivariate portmanteau statistic as an alternative to the Generalized Method of Moments. We derive the asymptotic properties of the GCov estimator and of the associated residual-based portmanteau statistic. Moreover, we show that the GCov estimators are semi-parametrically efficient and the residual-based portmanteau statistics are asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is also applied to a dynamic model of cryptocurrency prices." @default.
- W4294311565 created "2022-09-02" @default.
- W4294311565 creator A5039946949 @default.
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- W4294311565 date "2021-07-14" @default.
- W4294311565 modified "2023-10-18" @default.
- W4294311565 title "Generalized Covariance Estimator" @default.
- W4294311565 doi "https://doi.org/10.48550/arxiv.2107.06979" @default.
- W4294311565 hasPublicationYear "2021" @default.
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