Public debt and economic growth conundrum: Nonlinearity and inter-temporal relationship

Vladimir Arčabić, Josip Tica, Junsoo Lee, Robert J. Sonora

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26 Scopus citations

Abstract

The influential paper by Reinhart and Rogoff (Reinhart, C. M., and K. S. Rogoff. 2010. "Growth in a Time of Debt." The American Economic Review 100: 573-578.) has triggered a debate about the effects of the public debt on GDP growth. They argue that a debt-to-GDP ratio of over 90 percent has a deleterious effect on long-run economic growth. In this paper, we examine the inter-temporal relationship between public debt and GDP growth rates. We examine debt-to-GDP thresholds in nonlinear panel models, using various econometric strategies, methodologies, and data samples. We also evaluate confidence intervals around the estimated thresholds to determine the accuracy of estimated thresholds. Our results demonstrate that in the majority of estimated models, threshold values are not uniquely defined and the estimated coefficients are insignificant in most model specifications, as in Enders, Falk, and Siklos (Enders, W., B. L. Falk, and P. Siklos. 2007. "A Threshold Model of Real US GDP and the Problem of Constructing Confidence Intervals in TAR Models." Studies in Nonlinear Dynamics & Econometrics 11: 1322.). Next, we examine the inter-temporal relationship between the public debt and economic growth using structural panel data models as well as reduced form panel VAR models. In contrast to the standard presumption in the literature, we find that the inter-temporal effect of economic growth on the public debt is strong, but the effect of the public debt on economic growth is weak. We find similar results in sub-samples that include countries where the public debt is over 90 percent of GDP.

Original languageEnglish
Article number20160086
JournalStudies in Nonlinear Dynamics and Econometrics
Volume22
Issue number1
DOIs
StatePublished - Feb 23 2018

Keywords

  • economic growth
  • feedback effect
  • panel data model
  • public debt
  • threshold

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