arXiv subjects

Makoto Nakakita

Publications and source records attributed to Makoto Nakakita.

4 recordsLinked to original sources

arXiv2026

Shared-Donor Inference for Fixed-Set Heterogeneity in Synthetic Difference-in-Differences

Empirical studies often estimate several synthetic-control or synthetic Difference-in-Differences effects from a common donor pool and then summarize their heterogeneity. Because the same donors enter several comparisons, the first-stage errors are jointly distributed and affect different targets differently. We characterize three regimes for linear fixed-set summaries: nonconcentration, root-rate donor exposure, and donornegligibility. We also show that removing shared-donor uncertainty from a quadratic summary requires the shared-donor covariance to vanish in every direction entering that summary. We take a valid joint first-order experiment for the effect vector as the input to the second stage. It yields simultaneous intervals for a prespecified linear family, a fixedgrid projection band, a centered effect-vector confidence region, image sets for total and explained heterogeneity and their ratio, and a sampling-center homogeneity test. For the hard-simplex SDID estimator, an operator-normalized numerical directional bootstrap re-estimates the donor and time weights in every draw. We use an exact critical-cone derivative to check the numerical approximation. In the Medicaid expansion application, omitting off-diagonal covariance understates uncertainty for the equal-jurisdiction mean but overstates it for a centered projection on the baseline uninsured rate. Persistent counterfactual mismatch is reported separately through deterministic sensitivity regions.

econ.EM↗

arXiv2026

Calibrated Horizon-Weighted Local Projection Designs for Markov Switchbacks

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 the feasible persistence minimizing the covariance, HAC, residual-bootstrap, or realized-schedule risk of the estimator and reporting object specified before the experiment. A balanced homoskedastic Markov benchmark yields a closed form because the lagged-assignment information matrix is AR(1)-Toeplitz with a tridiagonal inverse. The benchmark maps local-projection reporting weights into persistence recommendations within a prespecified first-order Markov class. Field recommendations replace the benchmark covariance with residualized, serially dependent, pilot-calibrated, or randomization-based risk. A semi-synthetic Low Carbon London evaluation uses observed half-hourly baseline dynamics and known injected responses to assess design risk. It evaluates the covariance calculations under realistic load autocovariance and identifies when calibrated covariance selection should replace the homoskedastic Markov formula. Near-boundary designs use randomization-first inference when many-spell normal approximations are unsupported.

stat.ME↗

arXiv2026

Bayesian Robustness Values for Modern Causal Panel Estimators via Riesz Representations

We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial $R^2$ values. Route B treats observed-covariate benchmarks as auxiliary data only when benchmark-count, alpha-side alignment, model-check, dependence, and dominance diagnostics are credible; otherwise its main role is demotion. We derive estimator-specific Riesz diagnostics and clarify which are fixed-weight, target-level, or first-stage-conditional rather than full derivatives of regularized training maps. Monte Carlo stress tests distinguish calibrated benchmark settings from dominance failure, coarse alpha-side benchmarks, benchmark dependence, noisy covariates, and concentrated SDID weights. In the California tobacco-control panel, the SDID estimate is $-15.60$ packs per capita; corrected finite-donor placebo inference gives standard error 9.49 and add-one $p=0.051$. A refit-weight finite-difference audit changes the Route A nullification robustness value from 0.054 to 0.045, leaving the low-single-digit conclusion unchanged. A county-level minimum-wage application applies the same profile to a multi-cohort staggered panel.

stat.ME↗

arXiv2025

Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models

Improving Markov chain Monte Carlo algorithm efficiency is essential for enhancing computational speed and inferential accuracy in Bayesian analysis. These improvements can be effectively achieved using the ancillarity-sufficiency interweaving strategy (ASIS), an effective means of achieving such gains. Herein, we provide the first rigorous theoretical justification for applying ASIS in Bayesian hierarchical panel data models. Asymptotic analysis demonstrated that when the product of prior variance of unobserved heterogeneity and cross-sectional sample size N is sufficiently large, the latent individual effects can be sampled almost independently of their global mean. This near-independence accounts for ASIS's rapid mixing behavior and highlights its suitability for modern "tall" panel datasets. We derived simple inequalities to predict which conventional data augmentation scheme-sufficient augmentation (SA) or ancillary augmentation (AA)-yields faster convergence. By interweaving SA and AA, ASIS achieves optimal geometric rate of convergence and renders the Markov chain for the global mean parameter asymptotically independent and identically distributed. Monte Carlo experiment confirm that this theoretical efficiency ordering holds even for small panels (e.g., N=10). These findings confirm the empirical success of ASIS application across finance, marketing, and sports, laying the groundwork for its extension to models with more complex covariate structures and nonGaussian specifications.

stat.ME↗