High Dimensional Threshold Factor Models with Common Stochastic Trends
研究了在包含共同随机趋势的大因子模型中如何进行阈值回归的统计推断，提出了阈值的最小二乘估计量、置信区间构建方法以及各机制下共同趋势数量的估计程序，并通过蒙特卡洛实验和股票价格与债券收益率的应用验证了理论结果。
We study inference for threshold regression in the context of a large factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel way of constructing confidence intervals based on a randomised test. Our confidence intervals are constructed using the rates of convergence of the estimated threshold level, and no limiting distribution is required. We also develop a procedure to estimate the number of common trends in each regime, and investigate the properties of the Principal Component estimator for the loadings and common factors in both regimes. Our theoretical findings are corroborated through a comprehensive set of Monte Carlo experiments, and an application to equity prices and bond yields.