On the Ambiguous Consequences of Omitting Variables

Giuseppe De Luca (), Jan Magnus () and Franco Peracchi

No 15-061/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Keywords: Omitted variables; Misspecification; Least-squares estimators; Bias; Mean squared error (search for similar items in EconPapers)
JEL-codes: C13 C51 C52 (search for similar items in EconPapers)
Date: 2015-05-22
New Economics Papers: this item is included in nep-ecm
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https://papers.tinbergen.nl/15061.pdf (application/pdf)

Related works:
Working Paper: On the ambiguous consequences of omitting variables (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20150061

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