Cao, Jianfei, Gu, Chris and Wang, Yike 
ORCID: 0009-0004-7201-2702
(2020)
Principal component and static factor analysis.
In: Fuleky, Peter, (ed.)
Macroeconomic Forecasting in the Era of Big Data: Theory and Practice.
Advanced Studies in Theoretical and Applied Econometrics.
Springer Berlin / Heidelberg, Cham, CH, 229 - 266.
ISBN 9783030311490
Abstract
Factor models are widely used in macroeconomic forecasting. With large datasets, factor models are particularly useful due to their intrinsic dimension reduction. In this chapter, we consider the forecasting problem using factor models, with special consideration to large datasets. In factor model estimation, we focus on principal component methods, and show how the estimated factors can be used to assist forecasting. Machine learning methods are discussed to encompass the high-dimensional features of large factor models. We consider policy evaluation as a nowcasting problem and show how factor analysis can be used to perform counter-factual outcome prediction in complicated models with observational data. The usage of all these techniques is illustrated by empirical examples.
| Item Type: | Book Section |
|---|---|
| Official URL: | https://link.springer.com/book/10.1007/978-3-030-3... |
| Additional Information: | © 2020 Springer Nature Switzerland AG |
| Divisions: | Economics |
| Subjects: | H Social Sciences > HB Economic Theory |
| Date Deposited: | 05 May 2021 16:39 |
| Last Modified: | 11 Dec 2024 18:02 |
| URI: | http://eprints.lse.ac.uk/id/eprint/110351 |
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