Averaging estimators for regressions with a possible structural break created by Bruce E. Hansen

By:

Hansen, Bruce E [author]

Material type: Text

TextSeries: Econometric theory ; Volume 25, number 6Cambridge: Cambridge University Press, 2009Content type:

text

Media type:

unmediated

Carrier type:

volume

ISSN:

02664666

Subject(s):

Estimation theory -- Regression analysis -- Structural break

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: This paper investigates selection and averaging of linear regressions with a possible structural break. Our main contribution is the construction of a Mallows criterion for the structural break model. We show that the correct penalty term is nonstandard and depends on unknown parameters, but it can be approximated by an average of limiting cases to yield a feasible penalty with good performance. Following Hansen (2007, Econometrica 75, 1175–1189) we recommend averaging the structural break estimates with the no-break estimates where the weight is selected to minimize the Mallows criterion. This estimator is simple to compute, as the weights are a simple function of the ratio of the penalty to the Andrews SupF test statistic.

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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	Vol. 25, no.6 (pages 1498-1514)	SP3261	Not for loan	For In House Use Only

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This paper investigates selection and averaging of linear regressions with a possible structural break. Our main contribution is the construction of a Mallows criterion for the structural break model. We show that the correct penalty term is nonstandard and depends on unknown parameters, but it can be approximated by an average of limiting cases to yield a feasible penalty with good performance. Following Hansen (2007, Econometrica 75, 1175–1189) we recommend averaging the structural break estimates with the no-break estimates where the weight is selected to minimize the Mallows criterion. This estimator is simple to compute, as the weights are a simple function of the ratio of the penalty to the Andrews SupF test statistic.

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