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Optimal model averaged prediction of counter-factual outcomes for causal inference

Research Project

Project/Area Number 16K17102
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Economic statistics
Research InstitutionKyoto Sangyo University

Principal Investigator

YOSHIMURA Arihiro  京都産業大学, 経済学部, 講師 (40773982)

Project Period (FY) 2016-04-01 – 2018-03-31
Project Status Completed (Fiscal Year 2017)
Budget Amount *help
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Keywordsモデル選択 / モデル平均 / 政策評価法 / クロスセクション相関 / モデル選択法 / モデル平均法 / 経済統計学 / 計量経済学
Outline of Final Research Achievements

This study developed an optimal model averaged prediction method of individual counter-factual outcomes for causal inference. The main result of this study is to construct a model averaging method in the presence of cross-sectional dependence in the data and to derive the theoretical properties of the method. Moreover, the finite sample property of the method was investigated by large scale simulation experiments and the better performance of the method relative to the existing methods was found.

Report

(3 results)
  • 2017 Annual Research Report   Final Research Report ( PDF )
  • 2016 Research-status Report
  • Research Products

    (1 results)

All 2017

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Acknowledgement Compliant: 1 results)

  • [Journal Article] Focused Information Criterion for Series Estimation in Partially Linear Models2017

    • Author(s)
      Naoya Sueishi and Arihiro Yoshimura
    • Journal Title

      The Japanese Economic Review

      Volume: 印刷中

    • NAID

      120006398788

    • Related Report
      2016 Research-status Report
    • Peer Reviewed / Acknowledgement Compliant

URL: 

Published: 2016-04-21   Modified: 2019-03-29  

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