Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model with the Ordering of Variables for the Japanese Economy and Monetary Policy

This paper applies the time-varying parameter vector autoregressive model to the Japanese economy. The both parameters and volatilities, which are assumed to follow a random-walk process, are estimated using a Bayesian method with MCMC. The recursive structure is assumed for identification and the reversible jump MCMC is used for the ordering of variables. The empirical result reveals the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008 and provides evidence that the order of variables may change by the introduction of zero interest rate policy.Bayesian inference, Monetary policy, Reversible jump Markov chain Monte Carlo, Stochastic volatility, Time-varying parameter VAR

Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model for the Japanese Economy and Monetary Policy

This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR model and other VAR models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR model best fits the Japanese economic data.Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregressive model, Stochastic volatility, Time-varying parameter

Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model for the Japanese Economy and Monetary Policy

This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR model and other VAR models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR model best fits the Japanese economic data.Bayesian inference, Markov chain Monte Carlo, Monetary policy, State space model, Structural vector autoregressive model, Stochastic volatility, Time-varying parameter

Quantum Feature Extraction for THz Multi-Layer Imaging

A learning-based THz multi-layer imaging has been recently used for contactless three-dimensional (3D) positioning and encoding. We show a proof-of-concept demonstration of an emerging quantum machine learning (QML) framework to deal with depth variation, shadow effect, and double-sided content recognition, through an experimental validation.Comment: 2 pages, 5 figures, IRMMW-THz202