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...

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...

The paper discusses different methods to deal with unobservable variables: Kalman-Filtering, principal components, factor analysis, LISREL, MIMIC, DYMIMIC, PLS with respect to parameter estimation and forecasting. We got very good results by an extension of Kalman-Filtering called AS (general...

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The...

Forecasting real economic activity poses a considerable challenge not only due to hard-to-predict events like the current financial crisis but also due to the fact that targeted variables often undergo significant revisions after their first publication. In this paper we report the results of a...

This paper investigates the use of dynamic factor model for forecasting headline and core inflation as well as food price index in Poland. Method applied in the study extend conventional approaches by using bayesian techniques to dynamic factors' estimation, way of handling "ragged edge" data...

This study aims to make out-of-sample forecasts of recessions using the data of Turkey between 1986-2010. Recession forecast is important for decision makers in every level since it increases efficiency of decision making. Forecasting method used in this study is Qual-VAR method which includes...

Forecasting real economic activity poses a considerable challenge not only due to hard-to-predict events like the current financial crisis but also due to the fact that targeted variables often undergo significant revisions after their first publication. In this paper we report the results of a...

The papers in this special issue of Mathematics and Computers in Simulation cover the following topics: improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal:...

This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if...