Degree Type
Dissertation
Date of Award
2020
Degree Name
Doctor of Philosophy
Department
Statistics
Major
Statistics
First Advisor
Zhengyuan Zhu
Abstract
This dissertation is focused on time series analysis, particularly the high-dimensional time series and its application in traffic network data and economic forecasting. In the first project, we propose a weighted l1 regularized vector auto-regressive (VAR) model for spatio-temporal data. We compared the proposed method with commonly used LASSO estimation by extensive simulation studies and an application on a traffic network dataset. Both the simulation and real data application demonstrate the advantages of our method. In addition, we explored the theoretical properties of the proposed method under both exactly sparse and weakly sparse scenarios. In my second project, we provide a thorough analysis of the dynamic structures and forecasting of the Chinese consumer price index (CPI), with a comparison to those of the US CPI. We studied the similarities and differences between the Chinese CPI and US CPI in terms of their decompositions, dynamic structures and predictabilities. We found that both series can be well modeled by a class of seasonal auto-regressive integrated moving average model with covariates (S-ARIMAX), which precisely captures the dynamic structures of these two series, including trend, seasonality, outliers and lunar holiday effects. In addition, the Chinese CPI series possesses stable annual cycles and strong Spring Festival effects, with fitting and forecasting errors largely comparable to their US counterparts. Finally, for Chinese CPI, the factor augment (FA) approach can improve prediction over S-ARIMAX models for short-term forecasting. In the third project, we survey several cutting-edge variable selection methods and apply them to the field of economic forecasting with many predictors. Four groups of variable selection methods are reviewed and applied in real data to forecast three important economic indices. Their performances are compared with the popular FA approach. We also conduct several simulation studies to compare the four groups of variable selection methods in terms of their variable selection accuracy and out-of-sample prediction.
DOI
https://doi.org/10.31274/etd-20200902-161
Copyright Owner
Zhenzhong Wang
Copyright Date
2020-08
Language
en
File Format
application/pdf
File Size
139 pages
Recommended Citation
Wang, Zhenzhong, "High-dimensional time series analysis and its application in economic forecasting" (2020). Graduate Theses and Dissertations. 18242.
https://lib.dr.iastate.edu/etd/18242
