Nowcasting GDP using dynamic factor model: A Bayesian approach

dc.contributor.advisor Cindy Yu
dc.contributor.author Zhang, Yixiao
dc.contributor.department Statistics
dc.date 2020-06-26T19:48:32.000
dc.date.accessioned 2020-06-30T03:21:24Z
dc.date.available 2020-06-30T03:21:24Z
dc.date.copyright Fri May 01 00:00:00 UTC 2020
dc.date.embargo 2020-06-23
dc.date.issued 2020-01-01
dc.description.abstract <p>Real-time nowcasting is an assessment of current economic conditions from timely released economic series (such as monthly macroeconomic data) before the direct measure (such as quarterly GDP figure) is disseminated. Dynamic factor models (DFMs) are widely used in econometrics to bridge series with different frequencies and achieve a reduction in dimensionality. However, most of the research using DFMs often assumes the number of factors is known. In this dissertation, we first develop a Bayesian approach to provide a way to deal with unbalanced feature of the data set and to estimate latent common factors when the number of factors is assumed to be fixed and known. Then we extend our method such that it can identify the unknown number of factors and estimate the latent dynamic factors of DFMs accurately in a real-time nowcasting framework. The proposed method can deal with the unbalanced data, which is typical of a real-time nowcasting analysis. We demonstrate the validity of our approach through simulation studies and explore the applicability of our approach through empirical studies in nowcasting China's GDP or US GDP using monthly data series of several categories in each country's market respectively. The simulation studies and empirical studies indicate that our Bayesian approach is a viable option to conduct real-time nowcasting for China's and US's quarterly GDP.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/17858/
dc.identifier.articleid 8865
dc.identifier.contextkey 18242379
dc.identifier.doi https://doi.org/10.31274/etd-20200624-37
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/17858
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/32041
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/17858/Zhang_iastate_0097E_18614.pdf|||Fri Jan 14 21:29:54 UTC 2022
dc.subject.keywords Bayesian Analysis
dc.subject.keywords Dynamic Factor Models
dc.subject.keywords Nowcasting
dc.subject.keywords Number of Factors
dc.subject.keywords Stochastic Volatility
dc.title Nowcasting GDP using dynamic factor model: A Bayesian approach
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.discipline Statistics
thesis.degree.level thesis
thesis.degree.name Doctor of Philosophy
File
Original bundle
Now showing 1 - 1 of 1
Name:
Zhang_iastate_0097E_18614.pdf
Size:
1.29 MB
Format:
Adobe Portable Document Format
Description: