This chapter is concerned with methods for analyzing spatial data. After initial discussion of the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of...

Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory variables can be grouped into 21078 forecast combinations, and the number of possibilities increases further to 21078+21078 if we...

This paper systematically studies the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing...

This paper offers an econometric methodology for the detection of self-organisational change (defined in terms of the presence of time irreversibility, structural change and fundamental uncertainty) in economic processes that follow logistic diffusion growth paths in historical time. The...

The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. A normalization does not just imply a rule for selecting which point, among equivalent ones, to call the maximum likelihood estimator (MLE). It also governs the...