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...

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...

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

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...

In this paper we investigate the behavior of inflation persistence in the United States. To model inflation we estimate an autoregressive GARCH-in-mean model with variable coefficients and we propose a new measure of second-order time varying persistence, which not only distinguishes between...

Tourism is increasingly becoming an extremely important sector with its rapidly increasing contribution to GDP of any state or country as a whole. Analyzing and predicting tourist inflow not only enables us to make an accurate estimate of the number of tourists that is likely to visit a...

This paper investigates the global macroeconomic consequences of falling oil prices due to the oil revolution in the United States, using a Global VAR model estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Set-identification of the U.S. oil supply shock is achieved through...

For the large family of ARMA models with variable coefficients we provide an explicit and computationally tractable solution representation, which yields the fundamental properties of such processes, including the Wold-Cramer decomposition and the covariance structure. These results are founded...

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

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...