We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven...

We describe and assess the usefulness of a newly-constructed database of electronic payments, comprised of debit and credit card transactions as well cheques that clear through the banking system, as indicators of current GDP growth. Apart from capturing a broad range of spending activity, these...

I apply the model with unobserved components and stochastic volatility (UC-SV) to forecast the Russian consumer price index. I extend the model which was previously suggested as a model for inflation forecasting in the USA to take into account a possible difference in model parameters and...

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including bridge equations, Mixed Data Sampling (MIDAS) models, mixed frequency...

We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are...

Quantile forecasting has become an important research topic in econometrics as policy makers and investors are increasingly interested to focus more on downside (upside) risks rather than learning about the most likely outcome. Simultaneously, practitioners have largely used textual data to con-...

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...

We propose an easy-to-implement framework for combining quantile forecasts, applied to forecasting GDP growth. Using quantile regressions, our combination scheme assigns weights to individual forecasts from different indicators based on quantile scores. Previous studies suggest distributional...

This paper explores the potential of Business Survey data for the estimation and disaggregation of macroeconomic variables at higher frequency. We propose a multivariate approach which is an extension of the Stock and Watson (1991) dynamic factor model, considering more than one common factor...

Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of...