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Data mining reconsidered: encompassing and the general-to-specific approach to specification search

Kevin Hoover () and Stephen Perez

Econometrics Journal, 1999, vol. 2, issue 2, 167-191

Abstract: This paper examines the efficacy of the general-to-specific modeling approach associated with the LSE school of econometrics using a simulation framework. A mechanical algorithm is developed which mimics some aspects of the search procedures used by LSE practitioners. The algorithm is tested using 1000 replications of each of nine regression models and a data set patterned after Lovell's (1983) study of data mining. The algorithm is assessed for its ability to recover the data-generating process. Monte Carlo estimates of the size and power of exclusion tests based on t -statistics for individual variables in the specification are also provided. The roles of alternative sizes for specification tests in the algorithm, the consequences of different signal-to-noise ratios, and strategies for reducing overparameterization are also investigated. The results are largely favorable to the general-to-specific approach. In particular, the size of exclusion tests remains close to the nominal size used in the algorithm despite extensive search.

Keywords: General-to-specific; Encompassing; Data mining; LSE econometrics. (search for similar items in EconPapers)
Date: 1999
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Working Paper: DATA MINING RECONSIDERED: ENCOMPASSING AND THE GENERAL-TO-SPECIFIC APPROACH TO SPECIFICATION SEARCH (2003) Downloads
Working Paper: DATA MINING RECONSIDERED: ENCOMPASSING AND THE GENERAL-TO-SPECIFIC APPROACH TO SPECIFICATION SEARCH Downloads
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