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UNEMPLOYMENT PERSISTENCE AND QUANTILE PARAMETER HETEROGENEITY

Published online by Cambridge University Press:  28 September 2016

Corrado Andini  and
Monica Andini
Corrado Andini*
Affiliation:
University of Madeira, CEEAplA, and IZA
Monica Andini
Affiliation:
Bank of Italy
*
Address correspondence to: Corrado Andini, University of Madeira, 9000-390 Funchal, Portugal; e-mail: andini@uma.pt.
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Abstract

We argue that a random-coefficients representation of the classical Barro's model of unemployment dynamics can be used as a theoretical basis for a panel quantile autoregressive model of the unemployment rate. Estimating the latter with State-level data for the United States (1980–2010), we find that (i) unemployment persistence increases along quantiles of the conditional unemployment distribution; (ii) disregarding State-fixed effects implies an overestimation of unemployment persistence along unemployment quantiles; (iii) a macroeconomic shock changes not only the location but also the dispersion of the distribution of the State unemployment rates; (iv) a federal policy equally applied in each State can reduce unemployment inequality among States; (v) “hysteresis” and “natural rate” hypotheses can co-exist along quantiles of the unemployment distribution, with the former being not rejected at upper quantiles. In sum, while the standard approach to the estimation of unemployment persistence implicitly assumes that quantile parameter heterogeneity does not matter, we suggest that it does.

Keywords

Quantile RegressionUnemploymentDynamic Models
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Issue 5 , July 2018 , pp. 1298 - 1320
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

We are grateful to an associate editor and two anonymous referees for valuable comments and suggestions, which have greatly improved the paper. We would like to thank Ivan Canay for extremely useful comments on an earlier version of this article. In addition, we are grateful to Gabriel Montes-Rojas for sharing a code in R used in his own work, which has helped us to understand some technical issues with the estimations performed in this paper. Finally, we are indebted to Steve Fleetwood, who has helped us to present our ideas more clearly. The views expressed in this paper do not necessarily reflect those of the institutions with which we are affiliated. The usual disclaimer applies.

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