Yamagata Takashi

Affiliation and department：

Research field (1)： Economic statistics

Research theme for competitive and other funds (5)：

2021 - 2025 Behavioral Macroeconomics under Imperfect Information
2020 - 2025 Economic stagnation and widening wealth inequality: Crises of the world economy and a construction of a unified macroeconomic theory
2021 - 2024 反実仮想実験による炭素価格付加政策の排出削減効果と世界経済への影響の分析
2020 - 2023 ファクターモデルと罰則化法を用いた時系列・パネルデータの計量分析:理論と応用
2018 - 2022 Estimation and inferential methods for cross-sectionally dependent dynamic panel data models and their applications

Papers (23)：

M Hashem Pesaran, Takashi Yamagata. Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities. Journal of Financial Econometrics. 2023. Early View
Yoshimasa Uematsu, Takashi Yamagata. Inference in Sparsity-Induced Weak Factor Models. Journal of Business & Economic Statistics. 2023. 41. 1. 126-139
Yoshimasa Uematsu, Takashi Yamagata. Estimation of Sparsity-Induced Weak Factor Models. Journal of Business & Economic Statistics. 2023. 41. 1. 213-227
Guowei Cui, Vasilis Sarafidis, Takashi Yamagata. IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude Toward Risk. Econometrics Journal. 2022. forthcoming
Guowei Cui, Kazuhiko Hayakawa, Shuichi Nagata, Takashi Yamagata. A Robust Approach to Heteroscedasticity, Error Serial Correlation and Slope Heterogeneity in Linear Models with Interactive Effects for Large Panel Data. Journal of Business & Economic Statistics. 2022. Published online. 1-14

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Lectures and oral presentations (7)：

Discovering the network Granger causality in large vector autoregressive models
(Maastricht-York econometrics workshop 2022)
Discovering the network Granger causality in large VAR models
(Recent Developments in Spatial Econometrics 2022)
Discovering the Network Granger Causality in Large Vector Autoregressive Models
(2022)
Linear Panel Regression Models with Non-Classical Measurement Errors: An Application to Investment Equations
(Data Science Workshop 2022)
A robust approach to slope heterogeneity in linear models with interactive effects for large panel data
(International Conference on Econometrics and Statistics 2022)

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