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 | ||
| dc.subject | 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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