Economic theories are often encoded in equilibrium models that cannot be directly estimated because they lack features that, while inessential to the theoretical mechanism that is central to the specific theory, would be essential to fit the data well. We propose an econometric approach that...

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...

This paper proposes a novel way of formulating priors for estimating economic models. System priors are priors about the model's features and behavior as a system, such as the sacrifice ratio or the maximum duration of response of inflation to a particular shock, for instance. System priors...

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...

There are many indications that formal methods are not used to their full potential by central banks today. In this paper we demonstrate how BVAR and DSGE models can be used to shed light on questions that policy makers deal with in practice using data from Sweden. We compare the forecast...

This paper uses an estimated open economy DSGE model to examine if constant interest forecasts one and two years ahead can be regarded as modest policy interventions during the period 1993Q4-2002Q4. An intervention is here defined to be modest if it does not trigger the agents to revise their...

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...

This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables...