The choice of appropriate forecasting technique for air transport business is quite challenging and requires comprehensive analysis of empirical results. This paper compares the forecasting accuracy of Vector Autoregressive (VAR) Model and Dynamic Factor Model(DFM)in the determination of air passenger traffic. The aim is to establish through statistical principles, which of the two models outperforms the other by: fitting multivariate time series models of VAR and DFM; comparing the forecasting accuracy of the models; and determining the appropriate model for forecasting monthly passenger flow. Monthly data from January 2015 to December 2019 were utilized for the comparison. The series are trend stationary at all levels based on standard Augmented Dickey Fuller (ADF) unit root tests. Each of these series is found to be integrated of order one [I (1)] on the bases of Akaike Information Criterion (AIC) and the Hannan-Quin information criterion (HQIC). The models were further diagnosed for residual autocorrelation and normality tests followed by models fitness. The results of MAPE, RMSE, and MAD show that values of these three statistics are lower for DFM than the corresponding values for VAR. This study therefore recommends the DFM as a better forecast model for air passenger traffic.
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