Determining air transportation demand is a very important input that impacts countries’ economies in terms of both micro and macroeconomics. Thus, almost all countries continuously conduct research studies on estimating and forecasting airline passenger demand. However, new estimating and forecasting methods have been improved since the past. The purpose of this study is to estimate and forecast airline passenger demand based on the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variables (ARIMAX) methods in light of updated time series data and to determine the best model between the two econometric methods. The data used in the study belong to quarterly data between 2010-2022, in which the total incoming and outgoing airline passengers and total incoming and outgoing aircraft are obtained from Türkiye’s General Directorate of State Airports Authority (DHMI), GDP values are obtained from the Turkish Statistical Institute (TurkSTAT), and crude oil import prices are obtained from the US Energy Information Administration (EIA). The collected data have been analyzed using the software program EViews 10.00. The study presents estimations and predictions for total number of airline passengers by first using ARIMA modeling based on the Box-Jenkins method for univariate time series analysis and then ARIMAX modeling by adding exogenous variables to the first model. The study first tested for effects from COVID-19 and found no significant structural break in the studied period. Thus, the seasonal ARIMA (1,1,0) x (1,1,2) effect model (SARIMA) was understood to have the best fit. The second part of the study added the exogenous variables to the model and found ARIMAX (1,1) x (0,0) to have the best fit. Therefore, while the number of aircraft movements and GDP variables are found to be significant and to support the model, imported crude oil price was found to not be significant and to not support the model. The forecasting analysis results found the Theil index; bias proportion; RMSE, MAE, and MAPE; variance, and covariance explanation ratios; and R square values to be at satisfactory levels for both models. When comparing the two models, the ARIMAX model is seen to perform better than the ARIMA model.


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    Title :

    Using the ARIMA and ARIMAX Methodologies to Estimate, Model, Forecast, and Compare Airline Passenger Transportation Demand in Türkiye


    Contributors:
    Vahap Önen (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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