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Forecasting USA Unemployment Rate Base on ARIMA Model
- 1 Poole Management, North Carolina State University, Raleigh, United States
* Author to whom correspondence should be addressed.
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
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Cite this article
Zhang,D. (2023). Forecasting USA Unemployment Rate Base on ARIMA Model. Advances in Economics, Management and Political Sciences,49,67-76.
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
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