Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect

Yukitoshi Matsushita, Taisuke Otsu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Over the past few decades, much progress has been made in semiparametric modelling and estimation methods for econometric analysis. This paper is concerned with inference (i.e. confidence intervals and hypothesis testing) in semiparametric models. In contrast to the conventional approach based on t-ratios, we advocate likelihood-based inference. In particular, we study two widely applied semiparametric problems, weighted average derivatives and treatment effects, and propose semiparametric empirical likelihood and jackknife empirical likelihood methods. We derive the limiting behaviour of these empirical likelihood statistics and investigate their finite sample performance through Monte Carlo simulation. Furthermore, we extend the (delete-1) jackknife empirical likelihood toward the delete-d version with growing d and establish general asymptotic theory. This extension is crucial to deal with non-smooth objects, such as quantiles and quantile average derivatives or treatment effects, due to the well-known inconsistency phenomena of the jackknife under non-smoothness.

Original languageEnglish
Pages (from-to)133-155
Number of pages23
JournalJapanese Economic Review
Issue number2
Publication statusPublished - 2018 Jun
Externally publishedYes

ASJC Scopus subject areas

  • Economics and Econometrics


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