An Analysis of Passenger Flight Distance as an Indicator of Economic Activity

Ihsan Fathoni Amri; Suci Izzati; Rendi Andika Putra; Iva Aurellia Khalif; Febryana Dilla Setyaningrum; Isnaini Maulida; M. Al Haris

doi:10.30598/parameterv5i1pp111-124

Ihsan Fathoni Amri universitas muhammadiyah semarang
Suci Izzati Universitas Muhammadiyah Semarang
Rendi Andika Putra Universitas Muhammadiyah Semarang
Iva Aurellia Khalif Universitas Muhammadiyah Semarang
Febryana Dilla Setyaningrum Universitas Muhammadiyah Semarang
Isnaini Maulida Universitas Muhammadiyah Semarang
M. Al Haris Universitas Muhammadiyah Semarang

DOI: https://doi.org/10.30598/parameterv5i1pp111-124

Keywords: SARIMAX, RPMs, Unemployment Rate, Time Series Forecasting, Macroeconomic Indicators

Abstract

Understanding macroeconomic dynamics in the United States requires advanced forecasting techniques capable of capturing both seasonal structures and external shocks. This study investigates the relationship between passenger flight distance and the unemployment rate through the implementation of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model—an enhancement of the SARIMA framework. While SARIMA accounts for autoregressive, differencing, and moving average components with seasonal integration, SARIMAX further augments this structure by incorporating exogenous predictors, enhancing explanatory and predictive power.

Monthly time series data from 2015 to 2024 were utilized, with flight distance as the endogenous variable and the unemployment rate as the exogenous regressor. The modeling procedure involved rigorous stationarity testing via the Augmented Dickey-Fuller (ADF) test, model selection using the Akaike Information Criterion (AIC), and residual diagnostics employing the Box–Ljung and Shapiro–Wilk tests. SARIMAX(0,1,0)(0,1,1)[12] + X emerged as the optimal specification, with all parameters statistically significant and a MAPE of 3.68%, denoting excellent forecast accuracy.

Empirical findings reveal a significant and negative association between unemployment and air travel activity, emphasizing the role of labor market dynamics in shaping mobility trends. These results reinforce the utility of SARIMAX as a robust tool in macroeconomic forecasting and evidence-based policy formulation.

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References

[1] S. Mitra and C. Rada, "The Dynamics of Inflation and Unemployment in the U.S.: A Structuralist Approach," Rev. Polit. Econ., vol. 32, no. 2, pp. 248–270, 2020, doi: 10.1080/09538259.2019.1664290.
[2] B. Bok, D. Caratelli, D. Giannone, A. M. Sbordone, and A. Tambalotti, "Macroeconomic Nowcasting and Forecasting with Big Data," Annu. Rev. Econ., vol. 14, pp. 615–643, 2022, doi: 10.1146/annurev-economics-090821-032253.
[3] L. T. Hansen and T. J. Sargent, “Seasonal adjustment and economic time series,” J. Bus. Econ. Stat., vol. 5, no. 4, pp. 473–481, 1987.
[4] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ, USA: Wiley, 2015.
[5] A. I. Hidayat, “Forecasting of the Rupiah Exchange Rate Against the US Dollar Using the SARIMA Method,” Square: Journal of Mathematics and Applications, vol. 5, no. 2, pp. 65–76, 2023. [Online]. Available: https://doi.org/10.21580/square.2023.5.2.17059
[6] R. F. Siregar and I. S. Susanti, “Forecasting Indonesia’s Inflation Rate Using ARIMA and SARIMAX Models,” Parameter: Jurnal Statistika dan Sains Data, vol. 3, no. 2, pp. 93–106, 2023. [Online]. Available: https://doi.org/10.30598/parameterv3i02pp93-106
[7] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. OTexts, 2021. [Online]. Available: https://otexts.com/fpp3/
[8] B. D. Nugroho and A. Gunardi, "Model SARIMAX untuk Prediksi Inflasi di Indonesia," J. Stat. Terapan Komput. Stat., vol. 8, no. 1, pp. 45–54, 2023, doi: 10.14710/jstks.v8i1.32189.
[9] W. Carlin and D. Soskice, Macroeconomics: Institutions, Instability and the Financial System, 2nd ed. Oxford: Oxford University Press, 2021.
[12] D. Kurnia, A. Ahmadi, and A. Karsa, "Strategi Prediksi Data Deret Waktu Menggunakan Model ARIMA dan SARIMAX," J. Inform. Komput., vol. 11, no. 1, pp. 27–35, 2020.
[13] R. River, M. Sanchez, and L. Ornelas, “Time Series Forecasting with Exogenous Variables: A Comparative Study,” Journal of Statistical Modeling, vol. 19, no. 3, pp. 112–130, 2021.
[14] M. Qadrini, A. Pratama, and R. Sholeh, “Hybrid Forecasting Model Using ARIMAX and TSR for Monetary Flow Prediction in Indonesia,” Jurnal Manajemen dan Sistem Informasi, vol. 17, no. 2, pp. 87–97, 2021. doi: 10.20956/jmsk.v17i2.11803
[15] A. Amri, M. N. Syahputra, and A. A. Ridwan, “Model SARIMAX untuk Prediksi Okupansi Hotel di Bali Berdasarkan Jumlah Wisatawan,” Edukasi Matematika: Jurnal Penelitian Pendidikan Matematika, vol. 7, no. 1, pp. 21–31, 2024. doi: 10.29303/emj.v7i1.174
[16] M. F. A. Ramadhan and L. M. Utami, “Perbandingan Model ARIMA dan ARIMA–GARCH dalam Peramalan Indeks Harga Saham Harian PT. Jasa Marga,” Eksakta: Jurnal Ilmu-Ilmu MIPA, vol. 25, no. 2, pp. 89–96, 2024. [Online]. Available: https://journal.uii.ac.id/Eksakta/article/view/35335
[17] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, dan G. M. Ljung, Time Series Analysis: Forecasting and Control, edisi ke-5, Hoboken, NJ: Wiley, 2015.
[18] R. J. Hyndman dan G. Athanasopoulos, Forecasting: Principles and Practice, edisi ke-3, OTexts, 2021. Tersedia secara daring: https://otexts.com/fpp3/
[19] A. R. Wibowo dan D. R. Widodo, “Peramalan Inflasi di Indonesia Menggunakan Model SARIMAX,” Jurnal Statistika dan Aplikasinya, vol. 9, no. 2, pp. 85–95, 2021.
[20] M. Y. Fadli dan L. Nurhadi, “Penerapan Model SARIMAX pada Peramalan Jumlah Penumpang Kereta Api di Jawa,” Jurnal Sains dan Informatika, vol. 6, no. 3, pp. 112–118, 2022.