论文标题
因子模型中结构变化的似然比测试
Likelihood ratio test for structural changes in factor models
论文作者
论文摘要
在其因子负荷上有破损的因子模型在观察上等同于模型，而没有变化，而是其因子差异的变化。这有效地将高维的结构变化问题转化为低维问题。本文考虑了估计因素的差异变化的可能性比（LR）测试。 LR测试隐式探讨了估计因素的特殊特征：在替代假设下，突破和突破后的差异可以是一个单数矩阵，从而使LR测试的差异更快，因此比Wald-type测试更强大。 LR测试的功率效率更好，也通过模拟确认。我们还考虑平均变化和多次休息。我们将程序应用于使用每月行业级别数据的美国就业的因素建模和结构变化。
A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but a change in the variance of its factors. This effectively transforms a structural change problem of high dimension into a problem of low dimension. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply the procedure to the factor modelling and structural change of the US employment using monthly industry-level-data.
