Applied Dynamic Factor Analysis for Macroeconomic Forecasting

dc.contributor.advisor

West, Mike

dc.contributor.author

Eastman, William

dc.date.accessioned

2018-09-21T16:16:38Z

dc.date.available

2020-08-30T08:17:13Z

dc.date.issued

2018

dc.department

Statistical Science

dc.description.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.

dc.identifier.uri

https://hdl.handle.net/10161/17525

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Statistics

dc.subject

Finance

dc.title

Applied Dynamic Factor Analysis for Macroeconomic Forecasting

dc.type

Master's thesis

duke.embargo.months

23

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