Forecasting the world economy is a di¢ cult task given the complex interre-lationships within and across countries. This paper proposes a number ofapproaches to forecast short-term changes in selected world economic vari-ables and aims, …rst, at ranking various forecasting methods in terms...

This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance...

This study investigates whether the term structure of interest rates contains useful information about future real economic activity and inflation in Turkey during the 1991:7-2004:3 periods. In order to analyze these relationships, we have employed the Generalized Impulse Response (GIRF)...

This draft is a summary of the paper entitled: Forecasting Fuel Prices with the Chilean Exchange Rate. In that paper we show that the Chilean exchange rate has the ability to predict the returns of oil prices and of three additional oil-related products: gasoline, propane and heating oil. The...

I present evidence that higher frequency measures of inflation expectations outperform lower frequency measures of inflation expectations in tests of accuracy, predictive power, and rationality. For decades, the academic literature has focused on three survey measures of expected inflation: the...

This document compares the proprieties of different empirical methodologies to estimate the output gap and the potential output (non-observable variables of interest to the design of monetary policy and macroeconomic analysis) using Dominican Republic as a case of study. The output gap and...

We propose a prior for VAR models that exploits the panel structure of macroeconomic time series while also providing shrinkage towards zero to address overfitting concerns. The prior is flexible as it detects shared dynamics of individual variables across endogenously determined groups of...

A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper...

This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a...

It can be shown that inflation expectations and associated forecast errors are characterized by a high degree of persistence. One reason may be that forecasters cannot directly observe the inflation target pursued by the central bank and, hence, face a complicated forecasting problem. In...