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12 July 2017

SOEP Brown Bag Seminar Dynamic binary panel data models in presence of autocorrelation in the error term. A Monte-Carlo study

Dynamic panel data models are often applied to estimate the degree of state dependence. For instance, it has often been shown that there is state dependence in unemployment, i. e. unemployment in present causally increases the probability of unemployment in future. This paper uses Monte-
Carlo simulations to compare several dynamic linear and nonlinear panel data models for binary dependent variables. In particular, we focus on data generating processes where dynamics may stem from true state dependence or from autocorrelation in the error term (which may lead to the measurement of spurious state dependence). We employ estimators which explicitly model both sources of persistence and compare their performance with standard dynamic models. Furthermore, we test how these estimators perform in presence time-varying unobserved variables which follow time trends or which may be described by a moving-average model.

(joint with Thorsten Schank and Constantin Weiser)

  • Dr. Alexander Mosthaf (University of Mainz)

  • Time
    12:30 - 13:30
    DIW Berlin (Eleanor-Dulles-Raum) DIW Berlin im Quartier 110 Room 5.2.010 Mohrenstraße 58 10117 Berlin
    at DIW Berlin
    Tel.: +49 30 89789 336
    Tel.: +49 30 89789 272