Model Selection in Equations with Many ‘Small’ Effects*
研究了高维一般无约束模型中如何通过自动模型选择处理大量小效应和无关变量，利用主成分捕捉小影响，实现降维，适合处理变量数多于观测数的情形。
Abstract High dimensional general unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, nonlinear transformations, and multiple location shifts, together with all the principal components, possibly representing ‘factor’ structures, as perfect collinearity is also unproblematic. ‘Factors’ can capture small influences that selection may not retain individually. The final model can implicitly include more variables than observations, entering via ‘factors’. We simulate selection in several special cases to illustrate.