Arbeitspapier
On the Ambiguous Consequences of Omitting Variables
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 15-061/III
- Klassifikation
-
Wirtschaft
Estimation: General
Model Construction and Estimation
Model Evaluation, Validation, and Selection
- Thema
-
Omitted variables
Misspecification
Least-squares estimators
Bias
Mean squared error
- Ereignis
-
Geistige Schöpfung
- (wer)
-
De Luca, Giuseppe
Magnus, Jan
Peracchi, Franco
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2015
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- De Luca, Giuseppe
- Magnus, Jan
- Peracchi, Franco
- Tinbergen Institute
Entstanden
- 2015
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