Model belirlenmesi amacında kullanılan bilgisayar yoğunluklu teknikler

ABSTRACT COMPUTER INTENSIVE TECHNIQUES FOR MODEL SELECTION SIDIKA BAŞÇI Ph.D Thesis in Economics Supervisor: Prof. Dr. Asad Zaman May 1998 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 crite ria for Autoregressive (AR) processes exists. Simulation results report the effects of being parsimonious while selecting the model on forecasting. Moreover, in the chap ter 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 esti mates 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. Key Words: Model selection, autoregressive processes, lag order determination, forecasting, cross-validation, structural change, unknown change point, Bayesian approach. m