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      Analysis of Deviance for Hypothesis Testing in Generalized Partially Linear Models

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          Abstract

          In this study, we develop nonparametric analysis of deviance tools for generalized partially linear models based on local polynomial fitting. Assuming a canonical link, we propose expressions for both local and global analysis of deviance, which admit an additivity property that reduces to analysis of variance decompositions in the Gaussian case. Chi-square tests based on integrated likelihood functions are proposed to formally test whether the nonparametric term is significant. Simulation results are shown to illustrate the proposed chi-square tests and to compare them with an existing procedure based on penalized splines. The methodology is applied to German Bundesbank Federal Reserve data.

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          Author and article information

          Journal
          09 September 2020
          Article
          10.1080/07350015.2017.1330693
          2009.04252

          http://creativecommons.org/licenses/by/4.0/

          Custom metadata
          Journal of Business & Economic Statistics, Volume 37, 2019 - Issue 2
          math.ST stat.TH

          Statistics theory

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