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广义动态因子模型的推断理论AI 解读

We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. By exploiting the duality between common shocks and dynamic loadings, we derive the asymptotic distribution and associated standard errors for a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. We…

利用大型数据集测量产出缺口AI 解读

Abstract We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (comovements, nonstationarity, and the slow drift in long-run output growth over time). We find that (1) from the mid-1990s to 2008, the U.S. economy operated above its potential and (2) in 2018:Q4, the labor market was tighter than the market for goods…

大维动态因子模型：含一阶单整协整因子的脉冲响应估计AI 解读

We study a large-dimensional Dynamic Factor Model where: (i) the vector of factors Ft is I(1) and driven by a number of shocks that is smaller than the dimension of Ft; and, (ii) the idiosyncratic components are either I(1) or I(0). Under (i), the factors Ft are cointegrated and can be modeled as a Vector Error Correction Model (VECM). Under (i) and (ii), we provide consistent estimators, as both the cross-sectional size n and the time dimension T go to infinity, for the…