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

DOI

10.37099/mtu.dc.etdr/127

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.

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