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...

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

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

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

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,...

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...