Parametric estimation of long memory in factor models
提出一个动态因子模型，其中因子由具有任意持久性水平的随机时间趋势驱动，使用主成分分析和条件平方和估计，并应用于美国行业已实现波动率面板数据。
A dynamic factor model is proposed in that factor dynamics are driven by stochastic time trends describing arbitrary persistence levels. The proposed model is essentially a long memory factor model, which nests standard I(0) and I(1) behavior smoothly in common factors. In the estimation, principal components analysis (PCA) and conditional sum of squares (CSS) estimations are employed. For the dynamic model parameters, centered normal asymptotics are established at the usual parametric rates, and their small-sample properties are explored via Monte-Carlo experiments. The method is then applied to a panel of U.S. industry realized volatilities.