INFERENCE IN GROUP FACTOR MODELS WITH AN APPLICATION TO MIXED-FREQUENCY DATA

We derive asymptotic properties of estimators and test statistics to determine—in a grouped data setting—common versus group-specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit canonical co...

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Veröffentlicht in:	Econometrica 2019-07, Vol.87 (4), p.1267-1305
Hauptverfasser:	Andreou, E., Gagliardini, P., Ghysels, E., Rubin, M.
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Sprache:	eng
Schlagworte:	canonical correlations Estimating techniques Industrial production Large panel mixed frequency output growth unobservable pervasive factors
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container_issue	4
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container_title	Econometrica
container_volume	87
creator	Andreou, E. Gagliardini, P. Ghysels, E. Rubin, M.
description	We derive asymptotic properties of estimators and test statistics to determine—in a grouped data setting—common versus group-specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit canonical correlations), we derive a parameterfree asymptotic Gaussian distribution. We show how the group factor setting applies to mixed-frequency data. As an empirical illustration, we address the question whether Industrial Production (IP) is still the dominant factor driving the U.S. economy using a mixed-frequency data panel of IP and non-IP sectors. We find that a single common factor explains 89% of IP output growth and 61% of total GDP growth despite the diminishing role of manufacturing.
doi_str_mv	10.3982/ECTA14690
format	Article
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subjects	canonical correlations Estimating techniques Industrial production Large panel mixed frequency output growth unobservable pervasive factors
title	INFERENCE IN GROUP FACTOR MODELS WITH AN APPLICATION TO MIXED-FREQUENCY DATA
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