Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM,...

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model, the switching regression model whose univariate counterpart is called threshold...

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment might be less efficient than the whole market and hence easier to forecast. In this paper we extend the...

In this paper we apply cointegration and Granger-causality analyses to construct linear and neural network error-correction models for an Austrian Initial Public Offerings IndeX (IPOXATX). We use the significant relationship between the IPOXATX and the Austrian Stock Market Index ATX to forecast...

Many macroeconometric models depict situations where the shares of the major demand aggregates in output are stable over time. The joint dynamic behavior of the considered demand aggregate and output may thus be approximated by a cointegrated vector autoregression. However, the shares of many...