Empirical Bayesian inference in a nonparametric regression model

G.M. Koop, D. Poirier, Andrew Harvey (Editor), Siem Jan Koopman (Editor), Neil Shephard (Editor)

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Describes procedures for Bayesian estimation and testing in cross-sectional, panel data and nonparametric regression models. Non-parametric regression is a type of regression analysis in which the functional form of the relationship between the response variable and the associated predictor variables does not to be specified in order to fit a model to a set of data.
Original languageEnglish
Title of host publicationState Space Models and Unobserved Components
PublisherCambridge University Press
Pages152-171
Number of pages19
ISBN (Print)052183595X
DOIs
Publication statusPublished - 2005

Keywords

  • bayesian econometrics
  • semiparametric regression
  • non-parametric regression models

Cite this

Koop, G. M., Poirier, D., Harvey, A. (Ed.), Jan Koopman, S. (Ed.), & Shephard, N. (Ed.) (2005). Empirical Bayesian inference in a nonparametric regression model. In State Space Models and Unobserved Components (pp. 152-171). Cambridge University Press. https://doi.org/10.2277/052183595X