Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/311894 
Autor:innen: 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 65 [Issue:] 5 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 3093-3109
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression estimates. The empirical part considers two illustrative examples: a univariate regression analysis, based on the rational expectation-augmented Phillips curve; and a VAR with output, inflation and interest rates where a "price puzzle" arises.
Schlagwörter: 
Kalman filter
Hausman test
Omitted variable bias
Phillips curve
Price puzzle
VAR
JEL: 
C12
C34
C52
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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