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stat.ME2026
Calibrated Horizon-Weighted Local Projection Designs for Markov Switchbacks
Makoto Nakakita, Teruo Nakatsuma
We study temporal assignment design for Markov switchback experiments when the reported object is a dynamic local-projection target. We develop a calibrated selector that chooses t…
stat.ME2026
Bayesian Robustness Values for Modern Causal Panel Estimators via Riesz Representations
Makoto Nakakita, Takahiro Hoshino
We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time ave…
stat.ME2025
Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models
Makoto Nakakita, Tomoki Toyabe, Teruo Nakatsuma +1
Improving Markov chain Monte Carlo algorithm efficiency is essential for enhancing computational speed and inferential accuracy in Bayesian analysis. These improvements can be effe…