Computer intensive techniques for model selection

Abstract

There are three essays in this dissertation. In the first one, which appears in Chapter 2, a comparison of finite sample performances of six model selection criteria for Autoregressive (AR) processes exists. Simulation results report the effects of being parsimonious while selecting the model on forecasting. Moreover, in the chapter the assumption of normality, which can be seen in all of the previous theoretical and emprical studies, is relaxed and performances of the criteria under non-normal distributions are investigated. The second essay is presented in Chapter 3. In this essay three new model selection criteria are suggested where cross-validated estimates of variances are used. In the chapter, a comparison of the finite sample performances of these new criteria with the already existing ones is presented. The main concern of the third essay, that appears in Chapter 4, is detecting structural change when the change point is unknown. In the chapter, we derive some Bayesian tests to detect structural change with unknown change point under the assumptions of different prior distributions.