Forecasting and model averaging with structural breaks
This dissertation consists of three chapters. Collectively they attempt to investigate
on how to better forecast a time series variable when there is uncertainty on the stability
of model parameters.
The first chapter applies the newly developed theory of optimal and robust weights
to forecasting the U.S. market equity premium in the presence of structural breaks.
The empirical results suggest that parameter instability cannot fully explain the weak
forecasting performance of most predictors used in related empirical research.
The second chapter introduces a two-stage forecast combination method to forecasting
the U.S. market equity premium out-of-sample. In the first stage, for each predictive
model, we combine its stable and break cases by using several model averaging methods. Next, we pool all adjusted predictive models together by applying equal weights. The empirical results suggest that this new method can potentially offer substantial predictive gains relative to the simple one-stage overall equal weights method.
The third chapter extends model averaging theory under uncertainty regarding structural
breaks to the out-of-sample forecast setting, and proposes new predictive model
weights based on the leave-one-out cross-validation criterion (CV), as CV is robust to
heteroscedasticity and can be applied generally. It provides Monte Carlo and empirical
evidence showing that CV weights outperform several competing methods.