The identification of the effects of climate shocks on economic growth is central to design effective policies aiming at managing the future global climate change challenge. In this study, we investigate the effects of temperature and precipitation shocks on economic growth across different...

By means of a very simple example, this note illustrates the appeal of using Bayesian rather than classical methods to produce inference on hidden states in models of Markovian regime switching. -- Bayesian analysis ; switching regression ; regime changes ; nonlinear filtering

This paper examines the evolution of the cyclicality of real wages and employment in four Latin American economies (Brazil, Chile, Colombia and Mexico) during the period 1980-2010. Wages are highly pro-cyclical during the 1980s and early 1990s, a period characterized by high inflation. As...

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

Following Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time...

DSGE models have recently received considerable attention in macroeconomic analysis and forecasting. They are usually estimated using Bayesian methods, which require the computation of the likelihood function under the assumption that the parameters of the model remain fixed throughout the...

In this paper we extend the Bayesian Proxy VAR to incorporate time variation in the parameters. A Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in...