We would like to thank the participants of the NBP Workshop on Forecasting (Warsaw, 2019), the European Seminar on Bayesian Econometrics (Madrid, 2021) and internal seminars at the University of Salzburg, the FAU Erlangen-Nuremberg and the ECB, four anonymous referees as well as Anna Stelzer, Michael Pfarrhofer and Paul Hofmarcher for helpful comments and suggestions.

In this paper, we write the time-varying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with T K explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the time-variation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the T K regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.

No abstract

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation.Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process.We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered.