Macroeconomic Forecasting: Statistically Adequate, Temporal Principal Components

The main goal of this dissertation is to expand upon the use of Principal Component Analysis (PCA) in macroeconomic forecasting, particularly in cases where traditional principal components fail to account for all of the systematic information making up common macroeconomic and financial indicators. At the outset, PCA is viewed as a statistical model derived from the reparameterization of the Multivariate Normal model in Spanos (1986). To motivate a PCA forecasting framework prioritizing sound model assumptions, it is demonstrated, through simulation experiments, that model mis-specification erodes reliability of inferences. The Vector Autoregressive (VAR) model at the center of these simulations allows for the Markov (temporal) dependence inherent in macroeconomic data and serves as the basis for extending conventional PCA. Stemming from the relationship between PCA and the VAR model, an operational out-of-sample forecasting methodology is prescribed incorporating statistically adequate, temporal principal components, i.e. principal components which capture not only Markov dependence, but all of the other, relevant information in the original series. The macroeconomic forecasts produced from applying this framework to several, common macroeconomic indicators are shown to outperform standard benchmarks in terms of predictive accuracy over longer forecasting horizons.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Department dc:contributor.department

Not found

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Dorazio, Brian Arthur

Chair dc:contributor.committeechair

• Spanos, Aris

Committee members dc:contributor.committeemember

• Bose, Niloy
• Ashley, Richard A.
• Habibnia, Ali

• Principal Component Analysis
• Forecasting
• Vector Autoregressive Models
• Statistical Adequacy

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37157

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115345