4 papers
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…
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…
Shared-Donor Inference for Fixed-Set Heterogeneity in Synthetic Difference-in-Differences
Takahiro Hoshino, Makoto Nakakita
Empirical studies often estimate several synthetic-control or synthetic Difference-in-Differences effects from a common donor pool and then summarize their heterogeneity. Because t…
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…