High dimensional composite index makes experts' preferences in setting weights a hard task. In the literature, one of the approaches to derive weights from a data set is Principal Component or Factor Analysis that, although conceptually different, they are similar in results when FA is based on...

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a...

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...

This paper investigates the merger wave hypothesis for the US and the UK employing a Markov regime switching model. Using quarterly data covering the last thirty years, for the US, we identify the beginning of a merger wave in the mid 1990s but not the much-discussed 1980s merger wave. We argue...

In this paper, we estimate trend inflation in Sweden using an unobserved components stochastic volatility model. Using data from 1995Q4 to 2021Q4 and Bayesian estimation methods, we find that trend inflation has been well-anchored during the period - although in general at a level below the...

In this paper, we analyse Okun's law - a relation between the change in the unemployment rate and GDP growth - using data from Australia, the euro area, the United Kingdom and the United States. More specifically, we assess the relevance of non-Gaussianity when modelling the relation. This is...

In 2016 the Central Bank of Argentina began to announce inflation targets. In this context, providing authorities with good estimates of relevant macroeconomic variables is crucial for making pertinent corrections in order to reach the desired policy goals. This paper develops a group of models...

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...

We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find...