Applied Dynamic Factor Analysis for Macroeconomic Forecasting

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Date

2018

Authors

Eastman, William

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Abstract

The use of dynamic factor analysis in statistical modeling has broad utility across an array of applications. This paper presents a novel hierachical structure suited to a particular class of predictive problems - those which necessitate the aggregation of numerous forecasts in the presence of substantial data missingness and a need for systematic dimensionality reduction. The model hierarchy is presented in the context of the prediction of U.S. Nonfarm Payrolls, a well-known economic statistic, though can be generally applied for any context exhibiting an analagous data structure.

Type

Master's thesis

Department

Statistical Science

Description

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Subjects

Statistics, Finance

Citation

Citation

Eastman, William (2018). Applied Dynamic Factor Analysis for Macroeconomic Forecasting. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/17525.

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