Decision Maps for Bivariate Time Series with Potential Thrshold Cointegration

Bivariate time series data often show strong relationships between the two components, while both individual variables can be approximated by random walks in the short run andare obviously bounded in the long run. Three model classes are considered for a time-series model selection problem: stable vector autoregressions, cointegrated models, and globally stable threshold models. It is demonstrated how simulated decision maps help in classifying observed time series. The maps process the joint evidence of two test statistics: a canonical root and an LR--type specification statistic for threshold effects.