Much of the research on fiscal multipliers has used reduced form modelling approaches. While these models have been extended to include richer controls and identification approaches, it remains unclear whether shocks identified capture the true structural shocks. An alternative way to identify...

Much of the research on fiscal multipliers has used reduced form modelling approaches. While these models have been extended to include richer controls and identification approaches, it remains unclear whether shocks identified capture the true structural shocks. An alternative way to identify...

We develop a fiscal dynamic stochastic general equilibrium (DSGE) model for policy simulation and scenario analysis purposes tailored to Latvia, a small open economy in a monetary union. The fiscal sector elements comprise public investment, public consumption, government transfers that are...

We discuss Bayesian inferential procedures within the family of instrumental variables regression models and focus on two issues: existence conditions for posterior moments of the parameters of interest under a flat prior and the potential of Direct Monte Carlo (DMC) approaches for efficient...

We propose a new methodology for the Bayesian analysis of nonlinear non-Gaussian state space models with a Gaussian time-varying signal, where the signal is a function of a possibly high-dimensional state vector. The novelty of our approach is the development of proposal densities for the joint...

This paper presents the R package MitISEM, which provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in...

The paper considers a Bayesian approach to the cointegrated VAR model with a uniform prior on the cointegration space. Building on earlier work by Villani (2005b), where the posterior probability of the cointegration rank can be calculated conditional on the lag order, the current paper also...

A flexible forecast density combination approach is introduced that can deal with large data sets. It extends the mixture of experts approach by allowing for model set incompleteness and dynamic learning of combination weights. A dimension reduction step is introduced using a sequential...

The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...