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.

翻译：我们考虑的是具有强烈白色噪声误差的半参数动态模型类别。这一流程类别包括标准矢量自动递减模型、非基本结构VAR、混合因果非焦量模型以及非线性动态模型,如(多变)ARCH-M模型。关于这一类别进程的估计,我们建议采用通用变量(GCov)估计器,通过尽量减少残留的多变量多端端端口服动物统计作为通用模型的替代。我们从GCov估计器和相关的剩余端口服动物统计中提取出无症状特性。此外,我们表明GCov估计器半偏差有效,剩余端端口服动物统计分布在静默性上。模拟研究中演示了GCov估计器的有限样品性能。测量器还应用到一个静态货币价格动态模型。