A Comparative Study of ARIMA And ETS Models in Forecasting U.S. Nonfarm Data
DOI:
https://doi.org/10.54097/tvqce341Keywords:
Time series forecasting; ARIMA model; ETS model; Nonfarm payroll.Abstract
Time series forecasting is an essential reference for economic policies and their designs. Accurate predictions help to decrease uncertainty and guide the efficient allocation of resources. Nonfarm payroll in the United States is one of the most important economic indicators for evaluating the labor market and the overall expansion of the economy, while forecasting research that concentrates on this specific dataset remains scant. This paper constructs ARIMA and ETS models to forecast the U.S. nonfarm employment data and evaluates their performance. The optimal ARIMA and ETS specifications are selected based on the training dataset, and their forecasting accuracy is assessed using the RMSE metric. The results suggest that the ARIMA achieves higher accuracy of prediction compared to ETS. Through a systematic comparison of two forecasting approaches, this study provides evidence that ARIMA is more suitable for predicting U.S. nonfarm data, presenting useful information for economic forecasting research and the government and investors who favor labor market indicators.
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