Strategic Management & Leadership
Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis
External / Open Access
Abstract
To establish an updated understanding of the U.S. textile and apparel (TAP) industrys competitive position within the global textile environment, trade data from UN-COMTRADE (1996-2016) was used to calculate the Normalized Revealed Comparative Advantage (NRCA) index for 169 TAP categories at the four-digit Harmonized Schedule (HS) code level. Univariate time series using Autoregressive Integrated Moving Average (ARIMA) models forecast short-term future performance of Revealed categories with export advantage. Accompanying outlier analysis examined permanent level shifts that might convey important information about policy changes, influential drivers and random events.
Full Title
Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis
Primary Author
Zahra Saki
Co-Authors
Lori Rothenberg, Marguerite Moor, Ivan Kandilov, A. Blanton Godfrey
Publication Type
Preprint
Year
2019
Journal
arXiv Preprint
Category
Strategic Management & Leadership
Institution
External / Open Access
Access
Open Access
Added to Library
March 24, 2026
Cite This Publication
APA
Zahra Saki, Lori Rothenberg, Marguerite Moor, Ivan Kandilov, A. Blanton Godfrey (2019). *Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis*. External / Open Access.
MLA
Zahra Saki. *Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis*. External / Open Access, 2019.