Regularized Bayesian estimation in generalized threshold regression models

Friederike Greb, Tatyana Krivobokova, Axel Munk and Stephan von Cramon-Taubadel
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Friederike Greb: Georg-August-University Göttingen
Tatyana Krivobokova: Georg-August-University Göttingen
Axel Munk: Georg-August-University Göttingen

No 99, Courant Research Centre: Poverty, Equity and Growth - Discussion Papers from Courant Research Centre PEG

Abstract: Estimation of threshold parameters in (generalized) threshold regression models is typically performed by maximizing the corresponding pro file likelihood function. Also, certain Bayesian techniques based on non-informative priors are developed and widely used. This article draws attention to settings (not rare in practice) in which these standard estimators either perform poorly or even fail. In particular, if estimation of the regression coeffcients is associated with high uncertainty, the pro file likelihood for the threshold parameters and thus the corresponding estimator can be highly aff ected. We suggest an alternative estimation method employing the empirical Bayes paradigm, which allows to circumvent defi ciencies of standard estimators. The new estimator is completely data-driven and induces little additional numerical e ffort compared with the old one. Simulation results show that our estimator outperforms commonly used estimators and produces excellent results even if the latter show poor performance. The practical relevance of our approach is illustrated by a real-data example; we follow up the anlysis of cross-country growth behavior detailed in Hansen (2000).

Keywords: threshold estimation; nuisance parameters; empirical Bayes (search for similar items in EconPapers)
Date: 2011-10-07, Revised 2012-10-18
New Economics Papers: this item is included in nep-ecm and nep-ets
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