Interpreting Tests of the Convergence Hypothesis

19 Pages Posted: 16 Sep 2000

See all articles by Andrew B. Bernard

Andrew B. Bernard

Dartmouth College - Tuck School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)

Steven N. Durlauf

University of Chicago; National Bureau of Economic Research (NBER)

Date Written: June 1994

Abstract

This paper provides a framework for understanding the cross- section and time series approaches which have been used to test the convergence hypothesis. First, we present two definitions of convergence which capture the implications of the neoclassical growth model for the relationship between current and future cross-country output differences. Second, we identify how the cross-section and time series approaches relate to these definitions. Cross-section tests are shown to be associated with a weaker notion of convergence than time series tests. Third, we show how these alternative approaches make different assumptions on whether the data are well characterized by a limiting distribution. As a result, the choice of an appropriate testing framework is shown to depend on both the specific null and alternative hypotheses under consideration as well as on the initial conditions characterizing the data being studied.

Suggested Citation

Bernard, Andrew B. and Durlauf, Steven N., Interpreting Tests of the Convergence Hypothesis (June 1994). NBER Working Paper No. t0159. Available at SSRN: https://ssrn.com/abstract=225118

Andrew B. Bernard (Contact Author)

Dartmouth College - Tuck School of Business ( email )

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HOME PAGE: http://mba.tuck.dartmouth.edu/pages/faculty/Andrew.Bernard/

National Bureau of Economic Research (NBER)

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Centre for Economic Policy Research (CEPR) ( email )

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Steven N. Durlauf

University of Chicago ( email )

1155 East 60th Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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