Transnational terrorism data are difficult to forecast because they contain an unknown number of structural breaks of unknown functional form. The rise of religious fundamentalism, the demise of the Soviet Union, and the rise of al Qaeda have changed the nature of transnational terrorism. 'Old...

It is generally accepted that financial markets behave in a complex and unpredictable way, corroborating the Efficient Market Hypothesis. According to this hypothesis, investors cannot accurately predict future financial returns based on the information available. In this paper, we analyse the...

China has a population of 1.3 billion and grain accordingly plays a crucial role in the Chinese economy. In this paper we suggest predicting grain output mainly by factor inputs and asset holding, and present a Systematic Integrated Prediction Approach (SIPA). The key elements of SIPA are an...

In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model...

This paper considers the use of smooth transition autoregressive models for forecasting. First, the modelling of time series with these nonlinear models is discussed. Techniques for obtaining multiperiod forecasts are presented. The usefulness of forecast densities in the case of nonlinear...