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基于折叠凹惩罚回归的高维宏观预测与变量选择AI 解读

Summary This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent…

共同断点下因子载荷结构变化的检验AI 解读

This paper proposes a new test for factor loading structural change in dynamic factor models. We first show that the leading test proposed by Breitung and Eickmeier (2011) exhibits nonmonotonic power, essentially because the breaks are considered as spurious factors with stable factor loadings. The proposed test eliminates the effects of the spurious factors by maximizing the test statistic over possible numbers of the original factors. Monte Carlo simulations and an…