Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data. In addition new multivariate specifications are outlined and...

Dramatic changes in macroeconomic time series volatility pose a challenge to contemporary vector autoregressive (VAR) forecasting models. Traditionally, the conditional volatility of such models had been assumed constant over time or allowed for breaks across long time periods. More recent work,...

Among the many demand specifications in the literature, the Rotterdam model and the Almost Ideal Demand System (AIDS) have particularly long histories, have been highly developed, and are often applied in consumer demand systems modeling. Using Monte Carlo techniques, we seek to determine which...

In this paper we use a global vector autoregression (GVAR) model to study the response of Russian macroeconomic indicators to external shocks. The model includes individual models for the world's largest economies and a model for the oil market. Our specification takes into account the...

The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global...

The present study extends an earlier elasticities-absorption approach to the balance of payments to the analysis of the consequences of tariff reforms on the Egyptian macro-economy, particularly as it relates to fiscal revenue implications. From an analytical perspective, we use the...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

Dramatic changes in macroeconomic time series volatility pose a challenge to contemporary vector autoregressive (VAR) forecasting models. Traditionally, the conditional volatility of such models had been assumed constant over time or allowed for breaks across long time periods. More recent work,...

The aim of this article is to analyse the out-of-sample behaviour of a bunch of statistical and economics-based models when forecasting exchange rates (FX) for the UK, Japan, and the Euro Zone in relation to the US. A special focus is given to the commodity prices boom of 2007-8 and the...