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Bootstrapping the process of model selection: AN econometric example
Author(s) -
Veall Michael R.
Publication year - 1992
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.3950070109
Subject(s) - bootstrapping (finance) , econometrics , estimator , inference , computer science , model selection , data set , selection (genetic algorithm) , quality (philosophy) , process (computing) , set (abstract data type) , series (stratigraphy) , econometric model , statistical inference , statistics , economics , mathematics , machine learning , artificial intelligence , paleontology , philosophy , epistemology , biology , programming language , operating system
If a researcher has mined the data (i.e. selected an empirical model based on a series of trial estimates), inferences based on the final set of results are in general incorrect. This note treats the entire data mining process as an estimator and shows how a bootstrapping technique may improve the quality of inference. The method is applied to an empirical example on the deterrent effects of capital punishment.