Tests for overidentifying restrictions in Factor-Augmented VAR models

Xu Han

Journal of Econometrics, 2015, vol. 184, issue 2, 394-419

Abstract: This paper develops tests for overidentifying restrictions in Factor-Augmented Vector Autoregressive (FAVAR) models. The identification of structural shocks in FAVAR can involve infinitely many restrictions as the number of cross sections goes to infinity. Our focus is to test the joint null hypothesis that all the restrictions are satisfied. Conventional tests cannot be used due to the large dimension. We transform the infinite-dimensional problem into a finite-dimensional one by combining the individual statistics across the cross section dimension. We find the limit distribution of our joint test statistic under the null hypothesis and prove that it is consistent against the alternative that a fraction of or all identifying restrictions are violated. The Monte Carlo results show that the joint test statistic has good finite-sample size and power. We implement our tests to an updated version of Stock and Watson’s (2005) data set. Our test result is further confirmed by the impulse responses of major macroeconomic variables to the monetary policy shock: the impulse responses based on the specification selected by our test are generally more plausible than those based on the specifications rejected by our test.

Keywords: Factor model; Identification; Structural impulse responses; Monetary policy shock (search for similar items in EconPapers)
JEL-codes: C33 C52 E37 E52 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407614002425
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:184:y:2015:i:2:p:394-419

DOI: 10.1016/j.jeconom.2014.04.024

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().