Predicting the Total Construction Spending of Health Care by Using SARIMA Model: United States Case

Abstract

This study aims to determine the optimal model to predict the Total Construction Spending of Health Care by using the Seasonal Autoregressive Integrated Moving Average Model (SARIMA). SARIMA Model was performed during 22 years from January 2002 to December 2023 of Total Construction Spending of HealthCare (SHC), Millions of Dollars, from Federal Reserve Economic Data. The researcher concluded that the estimated model of the first-order difference for the logarithm of the SHC (DLSHC) series is SARIMA (1,1,2) (0,1,2)12. With coefficients: C = 0.003845, AR (1) = 0.970015, MA (1) = -1.147784, MA (2) = 0.219215, MA (12) = -0.89710 & MA (24) = -0.227258. This Model has more than 50% of the coefficients that are statistically significant at the 5% level. The jointly significant F-statistic value equals (3.893122) with a P-value (0.000981), S.E. of regression equals (0.019284). The ability to predict SARIMA (1, 1, 2) (0,1,2)12 Model is satisfactory, with a highly predictive power, with Theil Inequality Coefficient equals (0.000898) and Biaproportion equals (0.000087).