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Specification versus data fitting: SEM prediction and the Q ‐class estimator
Author(s) -
Womer Norman Keith,
Cantrell R. Stephen,
Mayer Walter J.
Publication year - 1999
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199903)18:2<77::aid-for720>3.0.co;2-u
Subject(s) - estimator , specification , a priori and a posteriori , computer science , variance (accounting) , context (archaeology) , range (aeronautics) , class (philosophy) , extremum estimator , econometrics , statistics , mathematics , m estimator , artificial intelligence , business , composite material , biology , paleontology , philosophy , materials science , accounting , epistemology
We propose a new class of limited information estimators built upon an explicit trade‐off between data fitting and a priori model specification. The estimators offer the researcher a continuum of estimators that range from an extreme emphasis on data fitting and robust reduced‐form estimation to the other extreme of exact model specification and efficient estimation. The approach used to generate the estimators illustrates why ULS often outperforms 2SLS‐PRRF even in the context of a correctly specified model, provides a new interpretation of 2SLS, and integrates Wonnacott and Wonnacott's (1970) least weighted variance estimators with other techniques. We apply the new class of estimators to Klein's Model I and generate forecasts. We find for this example that an emphasis on specification (as opposed to data fitting) produces better out‐of‐sample predictions. Copyright © 1999 John Wiley & Sons, Ltd.