The Perils of Aggregating Foreign Variables in Panel Data Models

31 Pages Posted: 10 Jul 2012

See all articles by Michele Ca' Zorzi

Michele Ca' Zorzi

European Central Bank (ECB)

Alexander Chudik

Federal Reserve Banks - Federal Reserve Bank of Dallas

Alistair Dieppe

World Bank

Date Written: June 11, 2012

Abstract

The curse of dimensionality refers to the difficulty of including all relevant variables in empirical applications due to the lack of sufficient degrees of freedom. A common solution to alleviate the problem in the context of open economy models is to aggregate foreign variables by constructing trade-weighted cross-sectional averages. This paper provides two key contributions in the context of static panel data models. The first is to show under what conditions the aggregation of foreign variables (AFV) leads to consistent estimates (as the time dimension T is fixed and the cross section dimension N -> infinite). The second is to design a formal test to assess the admissibility of the AFV restriction and to evaluate the small sample properties of the test by undertaking Monte Carlo experiments. Finally, we illustrate an application in the context of the current account empirical literature where the AFV restriction is rejected.

Keywords: Curse of dimensionality, panel data models, current account

JEL Classification: C12, C31, C33, F41

Suggested Citation

Ca' Zorzi, Michele and Chudik, Alexander and Dieppe, Alistair, The Perils of Aggregating Foreign Variables in Panel Data Models (June 11, 2012). ECB Working Paper No. 1444. Available at SSRN: https://ssrn.com/abstract=2081661

Michele Ca' Zorzi (Contact Author)

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Alexander Chudik

Federal Reserve Banks - Federal Reserve Bank of Dallas ( email )

2200 North Pearl Street
PO Box 655906
Dallas, TX 75265-5906
United States

Alistair Dieppe

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

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