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Model-based asymptotic inference on the effect of infrequent large shocks on cointegrated variables

机译:基于模型的渐近推断,对罕见的大冲击对协整变量的影响

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摘要

Quasi-maximum-likelihood (QML) estimation of a model combining cointegration in the conditional mean and rare large shocks (outliers) with a factor structure in the innovations is studied. The goal is not only to robustify inference on the conditional-mean parameters, but also to find regularities and conduct inference on the instantaneous and long-run effect of the large shocks. Given the cointegration rank and the factor order, asymptotic inference is obtained for the cointegration vectors, the short-run parameters, and the direction of each column of both the factor loading matrix and the matrix of long-run impacts of the large shocks. Large shocks, whose location is assumed unknown a priori, can be detected and classified consistently into the factor components.
机译:研究了在创新中结合了条件均值和罕见大冲击(离群值)的协整与因子结构的模型的准最大似然(QML)估计。目标不仅是要加强对条件均值参数的推论,而且要找到规律性并就大冲击的瞬时和长期影响进行推论。给定协整等级和因子阶数,就可以得到协整矢量,短期参数以及因子加载矩阵和大冲击的长期冲击矩阵的每一列的方向的渐近推断。可以确定大震荡,其位置被认为是先验未知的,并且可以始终如一地分类为因素分量。

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