Template-Type: ReDIF-Paper 1.0
Author-Name: Kevin Hoover
Author-Name-First: Kevin
Author-Name-Last: Hoover
Author-Name: Stephen J. Perez
Author-Name-First: Stephen J.
Author-Name-Last: Perez
Author-Workplace-Name: Department of Economics, University of California Davis
Title: DATA MINING RECONSIDERED: ENCOMPASSING AND THE GENERAL-TO-SPECIFIC APPROACH TO SPECIFICATION SEARCH
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.
Length: 57
File-URL: https://repec.dss.ucdavis.edu/files/gDNtG2kqWXifNmfSnw2r4P7N/97-27.pdf
File-Format: application/pdf
Number: 200
Classification-JEL: 
KeyWords: 
Creation-Date: 20030108
Handle: RePEc:cda:wpaper:200