Date of Award
2016
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Mathematical Sciences (MS)
Administrative Home Department
Department of Mathematical Sciences
Advisor 1
Yeonwoo Rho
Committee Member 1
Seokwoo Choi
Committee Member 2
Latika Lagalo
Committee Member 3
Qiuying Sha
Abstract
In econometrics and finance, variables are collected at different frequencies. If a higher frequency variable can help predict a lower frequency variable, it would be of interest to construct such regression models. One straightforward solution is to flat aggregate the higher frequency variable to match the lower frequency. However, flat aggregation may overlook useful information in the higher frequency variable. On the other hand, keeping all higher frequencies may result in overly complicated models. In literature, mixed data sampling (MIDAS) regression models have been proposed to balance between the two. In this thesis the mixed frequency models are addressed, and we propose a new model specification test that can help decide between the simple aggregation and the MIDAS model.
Recommended Citation
Groenvik, Henriette, "A SELF-NORMALIZING APPROACH TO THE SPECIFICATION TEST OF MIXED FREQUENCY MODELS", Open Access Master's Thesis, Michigan Technological University, 2016.