Forecasting and model averaging with structural breaks
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The Department of Economic Science was founded in 1898 to teach economic theory as a truth of industrial life, and was very much concerned with applying economics to business and industry, particularly agriculture. Between 1910 and 1967 it showed the growing influence of other social studies, such as sociology, history, and political science. Today it encompasses the majors of Agricultural Business (preparing for agricultural finance and management), Business Economics, and Economics (for advanced studies in business or economics or for careers in financing, management, insurance, etc).
History
The Department of Economic Science was founded in 1898 under the Division of Industrial Science (later College of Liberal Arts and Sciences); it became co-directed by the Division of Agriculture in 1919. In 1910 it became the Department of Economics and Political Science. In 1913 it became the Department of Applied Economics and Social Science; in 1924 it became the Department of Economics, History, and Sociology; in 1931 it became the Department of Economics and Sociology. In 1967 it became the Department of Economics, and in 2007 it became co-directed by the Colleges of Agriculture and Life Sciences, Liberal Arts and Sciences, and Business.
Dates of Existence
1898–present
Historical Names
- Department of Economic Science (1898–1910)
- Department of Economics and Political Science (1910-1913)
- Department of Applied Economics and Social Science (1913–1924)
- Department of Economics, History and Sociology (1924–1931)
- Department of Economics and Sociology (1931–1967)
Related Units
- College of Agricultural and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
- College of Business (parent college)
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Abstract
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.