9 papers
Curvature-Calibrated Quasi-Bayesian Updating for Moment-Restricted Models
Masahiro Tanaka
Moment restrictions provide a flexible basis for quasi-Bayesian inference when a full likelihood is unavailable, but the weighting matrix in a quadratic moment criterion determines…
Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks
Masahiro Tanaka
Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture o…
A Theory of Bootstrap Coverage Calibration for Generalized Posterior Credible Sets
Masahiro Tanaka
Generalized posteriors replace the likelihood by an exponentiated empirical criterion, but their credible sets generally lack asymptotic justification for frequentist coverage. Gen…
Modified Delayed Acceptance MCMC for Quasi-Bayesian Inference with Linear Moment Conditions
Masahiro Tanaka
We develop a computationally efficient framework for quasi-Bayesian inference based on linear moment conditions. The approach employs a delayed acceptance Markov chain Monte Carlo…
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning
Masahiro Tanaka
In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedastic…
Delayed Acceptance Markov Chain Monte Carlo for Robust Bayesian Analysis
Masahiro Tanaka
This study introduces a computationally efficient algorithm, delayed acceptance Markov chain Monte Carlo (DA-MCMC), designed to improve posterior simulation in quasi-Bayesian infer…
Quasi-Bayesian Local Projections: Simultaneous Inference and Extension to the Instrumental Variable Method
Masahiro Tanaka
Local projections (LPs) are widely used for impulse response analysis, but Bayesian methods face challenges due to the absence of a likelihood function. Existing approaches rely on…
Weighted Particle-Based Optimization for Efficient Generalized Posterior Calibration
Masahiro Tanaka
In the realm of statistical learning, the increasing volume of accessible data and increasing model complexity necessitate robust methodologies. This paper explores two branches of…
Generalized Posterior Calibration via Sequential Monte Carlo Sampler
Masahiro Tanaka
As the amount and complexity of available data increases, the need for robust statistical learning becomes more pressing. To enhance resilience against model misspecification, the…