Chapter 23 - Alternative Models of Adaptive and Near-Rational Expectations — Classification by Bootstrap Filter

Abstract

This chapter examines the alternative models of adaptive and near-rational expectations-classification by Bootstrap filter. The problem of deciding between alternative hypotheses is common. There may be two or more theories on a model background or parameter values and the data should determine which alternative is the most plausible. In case one of these models is nonlinear, the Monte Carlo approach should be used instead of linear methods. Such an approach is employed for classifying expectation formation within augmented Phillips curve. It is found that before the prediction phase the importance resampling is performed. It makes it no longer necessary to calculate with sample weights, because they are all equal. The other advantage is that there are no samples with small weights. Some samples with greater weight are selected two or more times while some samples are not selected at all. The prediction step is realized according to the state space model of the system. The probability of each model is computed based on the data. Such an approach is used for the classification of alternative economic models of expectation formation.