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Forecasting Time Series with a Mixture of Stationary and Nonstationary Factors

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thesis
posted on 27.02.2022, 02:25 by SIUM SHASHIKALA BODHA HANNADIGE
Accurate forecasting of macroeconomic variables, such as economic growth and inflation, is central to making economic policy decisions. This thesis contributes to the literature by developing improved methods for forecasting such variables. The central theme of this thesis is to extend the method based on the Factor Augmented Regression model. The new methods developed in the thesis may use a mixture of stationary and nonstationary unobserved factors and observed variables as predictors, time-varying parameters, and two-level factors. These advances constitute a significant contribution to improve econometric methods in this area.

History

Campus location

Australia

Principal supervisor

Jiti Gao

Additional supervisor 1

Mervyn J. Silvapulle

Year of Award

2022

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics