Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect

Yukitoshi Matsushita, Taisuke Otsu

研究成果: Article

抄録

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.

元の言語English
ページ(範囲)133-155
ページ数23
ジャーナルJapanese Economic Review
69
発行部数2
DOI
出版物ステータスPublished - 2018 6 1
外部発表Yes

Fingerprint

Derivatives
Treatment effects
Semiparametric model
Empirical likelihood
Inference
Jackknife
Quantile
Hypothesis testing
Statistics
Confidence interval
Finite sample
Inconsistency
Monte Carlo simulation
Modeling
Asymptotic theory
Econometric analysis

ASJC Scopus subject areas

  • Economics and Econometrics

これを引用

Likelihood Inference on Semiparametric Models : Average Derivative and Treatment Effect. / Matsushita, Yukitoshi; Otsu, Taisuke.

:: Japanese Economic Review, 巻 69, 番号 2, 01.06.2018, p. 133-155.

研究成果: Article

@article{616853e4f2fc4111ac2f5b20bcd9b3b3,
title = "Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect",
abstract = "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.",
author = "Yukitoshi Matsushita and Taisuke Otsu",
year = "2018",
month = "6",
day = "1",
doi = "10.1111/jere.12167",
language = "English",
volume = "69",
pages = "133--155",
journal = "Japanese Economic Review",
issn = "1352-4739",
publisher = "Wiley-Blackwell",
number = "2",

}

TY - JOUR

T1 - Likelihood Inference on Semiparametric Models

T2 - Average Derivative and Treatment Effect

AU - Matsushita, Yukitoshi

AU - Otsu, Taisuke

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85036529480&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85036529480&partnerID=8YFLogxK

U2 - 10.1111/jere.12167

DO - 10.1111/jere.12167

M3 - Article

AN - SCOPUS:85036529480

VL - 69

SP - 133

EP - 155

JO - Japanese Economic Review

JF - Japanese Economic Review

SN - 1352-4739

IS - 2

ER -