MODEL BUILDING AND DATA MINING
Econometric Reviews, 2001, vol. 20, issue 2, 159-170
This paper defines the phenomenon of data mining in econometrics and discusses various outcomes of and solutions to data mining. Both classical and Bayesian approaches are considered, each with notable advantages and disadvantages, and with the choice of loss function affecting critical values. Illustrative examples include variable addition and exclusion in a standard linear regression model, the choice of lag structure in a dynamic single equation, and specification in a simultaneous equations model.
Keywords: Bayes; Loss function; Pre-test estimation; Specification searches; Stein-James estimator; JEL Classification: C44; C51 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:20:y:2001:i:2:p:159-170
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