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Modeling heterogeneity: a praise for varying-coefficient models in causal analysis

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Published in Computational Statistics. 2015, vol. 30, p. 693-718
Abstract This article considers the question of how to cope with heterogeneity when studying causal effects. The standard approach in empirical economics is still to use a linear model and interpret the coefficients as the average returns or effects. Nowadays, instrumental variables (IV) are quite popular to account for (unobserved) heterogeneity when estimating these parameters. First the inadequacy of these standard methods is illustrated. Then it is shown why varying-coefficient models have a strong natural potential to model heterogeneity in many interesting regression problems. Moreover, it is straight forward to develop alternative IV specifications in the varying-coefficient models framework. The corresponding modeling and implementation facilities that are nowadays available in R are studied.
Keywords Varying-coefficient modelsCausal inferenceEconometricsSemiparametric modeling
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SPERLICH, Stefan Andréas, THELER, Raoul. Modeling heterogeneity: a praise for varying-coefficient models in causal analysis. In: Computational Statistics, 2015, vol. 30, p. 693-718. https://archive-ouverte.unige.ch/unige:73273

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Deposited on : 2015-06-22

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