DATA MINING RECONSIDERED: ENCOMPASSING AND THE GENERAL-TO-SPECIFIC APPROACH TO SPECIFICATION SEARCH

Kevin Hoover () and Stephen J. Perez
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Stephen J. Perez: Department of Economics, University of California Davis

No 200, Working Papers from University of California, Davis, Department of Economics

Abstract: The effectiveness of one aspect of the London School of Economics (LSE) approach to econometrics is assessed in a simulation study. The paper uses a data set and nine models analogous to those in Lovell''s (1983) study of data mining. A simplified general-to-specific algorithm is tested in a simulation framework. While the study documents some of the pitfalls of the general-to-specific approach, it is, on the whole, supportive of the effectiveness of the LSE methodology as applied to stationary data with relatively simple dynamics. The general-to-specific methodology clearly dominates the alternative search methodologies investigated by Lovell.

Pages: 57
Date: 2003-01-08
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https://repec.dss.ucdavis.edu/files/gDNtG2kqWXifNmfSnw2r4P7N/97-27.pdf (application/pdf)

Related works:
Journal Article: Data mining reconsidered: encompassing and the general-to-specific approach to specification search (1999)
Working Paper: DATA MINING RECONSIDERED: ENCOMPASSING AND THE GENERAL-TO-SPECIFIC APPROACH TO SPECIFICATION SEARCH
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