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Tanaka Shinya  田中 晋矢

ORCIDConnect your ORCID iD *help
Researcher Number 80727149
Other IDs
Affiliation (Current) 2025: 小樽商科大学, 商学部, 教授
Affiliation (based on the past Project Information) *help 2024: 小樽商科大学, 商学部, 教授
2018 – 2019: 青山学院大学, 経済学部, 准教授
2016 – 2017: 小樽商科大学, 商学部, 准教授
Review Section/Research Field
Principal Investigator
Basic Section 07030:Economic statistics-related / Economic statistics
Keywords
Principal Investigator
統計的推測 / 高次元データ / ポートフォリオ選択 / 経済統計学 / 罰則付き回帰 / 超高次元経済データ / 超高次元パネルVARモデル / マクロ経済予測 / パネルベクトル自己回帰モデル / 経済予測 / Lasso / 罰則付回帰 / 超高次元経済データ分析
  • Research Projects

    (2 results)
  • Research Products

    (8 results)
  • Co-Researchers

    (1 People)
  •  Portfolio Selection for High-Dimensional DataPrincipal Investigator

    • Principal Investigator
      田中 晋矢
    • Project Period (FY)
      2024 – 2026
    • Research Category
      Grant-in-Aid for Scientific Research (C)
    • Review Section
      Basic Section 07030:Economic statistics-related
    • Research Institution
      Otaru University of Commerce
  •  Econometric Analysis on Ultra-high dimensional Factor models and DI forecastsPrincipal Investigator

    • Principal Investigator
      Tanaka Shinya
    • Project Period (FY)
      2016 – 2019
    • Research Category
      Grant-in-Aid for Young Scientists (B)
    • Research Field
      Economic statistics
    • Research Institution
      Aoyama Gakuin University
      Otaru University of Commerce

All 2020 2019 2018 2017 2016

All Journal Article Presentation

  • [Journal Article] 経済時系列データにおける非スパース化Lassoにもとづく検定統計量の有限標本特性 : シミュレーション分析による接近2020

    • Author(s)
      田中晋矢
    • Journal Title

      青山経済論集

      Volume: 71 Pages: 41-56

    • NAID

      120006845604

    • Open Access
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Journal Article] High‐dimensional macroeconomic forecasting and variable selection via penalized regression2019

    • Author(s)
      Uematsu Yoshimasa and Tanaka Shinya
    • Journal Title

      The Econometrics Journal

      Volume: 22 Issue: 1 Pages: 34-56

    • DOI

      10.1111/ectj.12117

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PROJECT-16K17100, KAKENHI-PROJECT-19K13665
  • [Journal Article] High-dimensional Macroeconomic Forecasting and Variable Selection via Penalized Regression2018

    • Author(s)
      Yoshimasa Uematsu and Shinya Tanaka
    • Journal Title

      青山学院大学経済学部経済研究所 ワーキングペーパー

      Volume: 2018-4

    • Open Access
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Journal Article] Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach2017

    • Author(s)
      Yoshimasa Uematsu and Shinya Tanaka
    • Journal Title

      SSRN (Social Science Research Network)

      Volume: id:2927876

    • Acknowledgement Compliant / Open Access
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Presentation] Large-Scale Panel Vector Autoregressive Models2019

    • Author(s)
      田中晋矢,植松良公,山形孝志,新谷元嗣
    • Organizer
      Summer Workshop on Economic Theory 2019
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Presentation] Statistical Inference on high-dimensional economic data through the desparsified Lasso: Monte Carlo evidence and its possible directions2019

    • Author(s)
      田中晋矢
    • Organizer
      Data Science Workshop (東北大学大学院経済学研究科サービス・データ科学研究センター)
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Presentation] High-dimensional Macroeconomic Forecasting and Variable Selection via Penalized Regression2018

    • Author(s)
      田中晋矢
    • Organizer
      ICS FSファカルティーセミナー(一橋大学大学院国際企業戦略研究科)
    • Data Source
      KAKENHI-PROJECT-16K17100
  • [Presentation] Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach2016

    • Author(s)
      田中晋矢(報告者),植松良公
    • Organizer
      計量経済学セミナー
    • Place of Presentation
      京都大学経済研究所(京都府京都市左京区)
    • Year and Date
      2016-11-16
    • Data Source
      KAKENHI-PROJECT-16K17100
  • 1.  植松 良公 (40835279)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results

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