Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

We examine a trivariate time series model that is subject to a regime switch, where the shifts are governed by an unobserved, two-state variable that follows a Markov process. The analysis is performed in a Bayesian framework developed by Albert and Chib (1993), where the unobserved states are...

This paper constructs a DSGE model for an economy with commodity exports. We estimate the model on Russian data, making a special focus on quantitative effects of commodity price dynamics. There is a widespread belief that economic activity in Russia crucially depends on oil prices, but...

This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as...

DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter...

The paper provides estimates of the euro-area output gap, based on a relatively standard medium scale DSGE model estimated recursively with Bayesian techniques over the period 1985-2016. The main findings can be summarized as follows. First, our measure of output gap identifies episodes of...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for “online” estimation, and...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for "online" estimation, and...

This paper presents Aino 2.0 – the dynamic stochastic general equilibrium (DSGE) model currently used at the Bank of Finland for forecasting and policy analysis. The paper provides a detailed theoretical description of the model, its estimation and how it can be used to interpret the evolution...

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-step ahead forecasts. In the empirical...