Opening the black box: structural factor models with large cross-sections created by Mario Forni, Domenico Giannone, Marco Lippi and Lucrezia Reichlin

By:

Forni, Mario [author]

Contributor(s):

Material type: Text

TextSeries: Econometric theory ; Volume 25, number 5Cambridge: Cambridge University Press, 2009Content type:

text

Media type:

unmediated

Carrier type:

volume

ISSN:

02664666

Subject(s):

Dynamic Factor Models -- Fundamentalness -- Structural VARs

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse response functions. In particular, we argue that, if the data follow an approximate factor structure, the “problem of fundamentalness”, which is intractable in structural VARs, can be solved provided that the impulse responses are sufficiently heterogeneous. Finally, we propose a consistent method (and n, T rates of convergence) to estimate the impulse-response functions, as well as a bootstrapping procedure for statistical inference. JEL Classification: E0, C1

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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library Journal Article	HB139.T52 ECO (Browse shelf(Opens below))	Vol. 25, no.5 (pages 1319-1348)	SP3260	Not for loan	For In House Use Only

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This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse response functions. In particular, we argue that, if the data follow an approximate factor structure, the “problem of fundamentalness”, which is intractable in structural VARs, can be solved provided that the impulse responses are sufficiently heterogeneous. Finally, we propose a consistent method (and n, T rates of convergence) to estimate the impulse-response functions, as well as a bootstrapping procedure for statistical inference. JEL Classification: E0, C1

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