Artificial neural network modeling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very complex problems, and that they are free from...

We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance. We find that in...

It is now an accepted fact that the majority of financial markets worldwide are neither normal nor constant, and South Africa is no exception. One idea that can be used to understand such markets and has been gaining popularity recently is that of regimes and regime-switching models. In this...

It is commonly known that various econometric techniques fail to consistently outperform a simple random walk model in forecasting exchange rates. The aim of this study is to analyse whether this also holds for selected currencies of the CEE region as the literature relating to the ability of...

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...

In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on...

This paper surveys the literature on multi-step forecasting when the model or the estimation method focuses directly on the link between the forecast origin and the horizon of interest. Among diverse contributions, we show how the current consensual concepts have emerged. We present an...

We study the out-of-sample predictability of the real price of crude oil using forecast combinations constructed from several individual predictors. We find that forecasts of themonthly average price of oil are more accurate than the no-change forecast at horizons ranging from 1 to 24 months...

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...