The smooth colonel and the reverend find common ground

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A semiparametric regression estimator that exploits categorical (i.e., discrete-support) kernel functions is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effects estimator familiar from panel data, and the varying coefficient estimator, among others. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The estimator is motivated as an intuitive and appealing generalization of existing methods. It is then supported by demonstrating that it can be realized as a posterior mean in the Lindley and Smith (1972) framework. As a demonstration of the flexibility of the proposed approach, the model is extended to nonparametric hierarchical regression based on B-splines.

Original languageEnglish
JournalEconometric Reviews
Pages (from-to)241-256
Number of pages16
Publication statusPublished - Mar 2017

    Research areas

  • Bayesian methods, econometrics, hierarchical models, kernel estimation, nonparametrics, panel data

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