Revisiting money - output casuality from a Bayesian logistic smooth transition VECM perspective

This paper proposes a Bayesian approach to explore money-output causality within a logistic smooth transition VECM framework. Our empirical results provide substantial evidence that the postwar US money-output relationship is nonlinear, with regime changes mainly governed by the lagged inflation rates. More importantly, we obtain strong support for long-run non-causality and nonlinear Grangercausality from money to output. Furthermore, our impulse response analysis reveals that a shock to money appears to have negative accumulative impact on real output over the next fifty years, which calls for more caution when using money as a policy instrument

Nonlinear impacts of international business cycles on the UK - A Bayesian smooth transition VAR approach

Employing a Bayesian approach, we investigate the impact of international business cycles on the UK economy in the context of a smooth transition VAR. We find that British business cycle is asymmetrically influenced by the US, France and Germany. Overall, positive and negative shocks generating in the US or France affect the UK in the same directions of the shock. Yet, a shock emanating from Germany always exerts negative accumulative effects on the UK. More strikingly, a positive shock arising from Germany negatively affects UK output growth more than a negative shock from Germany of the same size. These results suggest that the appropriate UK economic policy depends upon the origin, size and direction of the external shocks

A Test to Select between Spatial Weighting Matrices

There exist a number of ways of selecting the best spatial weighting matrix in a spatial regression framework. But these methods all work under the assumption that there is only one matrix in the final model and they simply aim to pick the best one. We propose an encompassing tests which allows for the possibility that the final preferred model may have two or more spatial weighting matrices. We validate the proposed test through a Monte Carlo study. We then illustrate the test by applying it to a two-equation simultaneous system determining sovereign bond ratings and spreads for two groups comprising northern and Southern Euro-area countries.</p

Computationally efficient inference in large Bayesian mixed frequency VARs

Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome over-parameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet–Laplace global–local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.</ul

Quantifying Spillovers Among Regions

The standard procedure for quantifying spillover effects of changes in economic fundamentals among separate regions (or countries) is to link the regions through predetermined weights – for example through fixed weighted trade indices or fixed spatial weights based on geographical distance. We provide a method for quantifying spillover effects among the U.S., the euro area, and the U.K. using spatial weights that are determined endogenously. We specify a new spatially augmented VAR model and we introduce a Bayesian estimation technique to freely estimate and quantify spatial interactions. We are able to quantify the effects of shocks to economic fundamentals in the three regions considered without imposing a priori restrictions on the size and directions of the spillovers. To illustrate our technique, we quantify the spillover effects of a series of shocks, including the recent rises in inflation and money supply shocks, in each of the three regions under consideration on the other regions.</p

Inflation forecasting with rolling windows: An appraisal

We examine the performance of rolling windows procedures in forecasting inflation. We implement rolling windows augmented Dickey–Fuller (ADF) tests and then conduct a set of Monte Carlo experiments under stylized forms of structural breaks. We find that as long as the nature of inflation is either stationary or non‐stationary, popular varying‐length window techniques provide little advantage in forecasting over a conventional fixed‐length window approach. However, we also find that varying‐length window techniques tend to outperform the fixed‐length window method under conditions involving a change in the inflation process from stationary to non‐stationary, and vice versa. Finally, we investigate methods that can provide early warnings of structural breaks, a situation for which the available rolling windows procedures are not well suited.</p