Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 49 , 01 December 2023


Open Access | Article

Forecasting USA Unemployment Rate Base on ARIMA Model

Dihan Zhang * 1
1 Poole Management, North Carolina State University, Raleigh, United States

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 49, 67-76
Published 01 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Dihan Zhang. Forecasting USA Unemployment Rate Base on ARIMA Model. AEMPS (2023) Vol. 49: 67-76. DOI: 10.54254/2754-1169/49/20230486.

Abstract

This paper presents a detailed analysis of unemployment rate forecasting, a critical subject for various stakeholders including policymakers, businesses, and individuals. Amid significant economic events such as the global financial crisis and COVID-19 pandemic, the need for precise unemployment forecasts has become crucial. The research utilizes an Autoregressive Integrated Moving Average (ARIMA) model to analyze US unemployment rate data from 2000 to 2023, sourced from the Federal Reserve Economic Data (FRED). The paper identifies seasonality patterns, executes appropriate data transformations, and incorporates the Box-Jenkins methodology for ARIMA model identification. The findings reveal the model's resilience, demonstrating accurate forecasts despite significant disruptions. These insights offer valuable contributions in understanding labor market dynamics, facilitating informed decision-making and strategic planning. The paper highlights the robustness of ARIMA models, and their potential to adapt to rapid changes in the economic landscape, thereby proving invaluable in forecasting unemployment rates.

Keywords

unemployment rate, forecasting, ARIMA model, time-series analysis, labor market dynamics

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-145-2
ISBN (Online)
978-1-83558-146-9
Published Date
01 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/49/20230486
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated