Model Selection Criteria for Factor‐Augmented Regressions
研究了在因子增强回归中如何选择相关因子数量的准则，提出了理论条件并修改了标准模型选择准则，模拟和实证表明这些准则有效。
Abstract Existing dynamic factor selection criteria determine the appropriate number of factors in a large‐dimensional panel of explanatory variables, but not all of these have to be relevant for modeling a specific dependent variable within a factor‐augmented regression. We develop theoretical conditions that selection criteria have to meet in order to get consistent estimates of the relevant factor dimension for such a regression. These incorporate factor estimation error and do not depend on specific factor estimation methodologies. Using this framework, we modify standard model selection criteria, and simulation and empirical applications indicate that these are useful in determining appropriate factor‐augmented regressions.