MODEL SELECTION AND INFERENCE: FACTS AND FICTION
讨论了数据驱动模型选择对后续推断的影响，驳斥了关于一致模型选择渐近无影响的迷思，并指出估计有限样本分布的不可能性。
Model selection has an important impact on subsequent inference. Ignoring the model selection step leads to invalid inference. We discuss some intricate aspects of data-driven model selection that do not seem to have been widely appreciated in the literature. We debunk some myths about model selection, in particular the myth that consistent model selection has no effect on subsequent inference asymptotically. We also discuss an “impossibility” result regarding the estimation of the finite-sample distribution of post-model-selection estimators.