Pemodelan ARDL Pengaruh Ekspor- Impor terhadap Inflasi di Jawa Timur

Reta Wanda Mardaningrum, Sri Harini, Erna Herawati

Abstract


The Autoregressive Distributed Lag (ARDL) approach is a time series analysis method that enables the examination of short- and long-term relationships between variables, even when the variables have different levels of stationarity. This study aims to analyze the influence of export and import values on inflation in East Java using standardized monthly data from the Central Bureau of Statistics (BPS) East Java, covering the 2018–2023 period. The selected ARDL model revealed cointegration, indicating significant long-term relationships among the variables. The best model was identified with an F-statistic value of 6.4036, an R-squared value of 0.8163, and the lowest Akaike Information Criterion (AIC). The results demonstrate that export activities, particularly non-oil and gas exports, tend to suppress inflation in the long term, while oil and gas imports exhibit a strong positive influence on inflation. Standardized data were used in this analysis to ensure consistent representation of relationships among.


Keywords


Autoregressive Distributed Lag; Inflation; Oil and Gas Exports; Non-oil and Gas Exports; Oil and Gas Imports; Non-oil and Gas Imports; Cointegration; Standardized Data

Full Text:

PDF

References


S. Serdawati, “Penggunaan Metode Autoregressive Distributed Lag (ARDL) untuk Analisis Faktor-Faktor yang Mempengaruhi Harga Emas di Indonesia Tahun 2007-2017,” Universitas Islam Indonesia, 2018.

C. Brooks, Introdustory Econometrics for Finance Second Edition, no. 112. United States of America by Cambridge University Press, New York, 2008.

M. H. Pesaran, Y. Shin, dan R. J. Smith, “Bounds testing approaches to the analysis of level relationships,” J. Appl. Econom., vol. 16, no. 3, hal. 289–326, 2001, doi: 10.1002/jae.616.

S. S. Astiyah, “Inflasi,” in Pusat Pendidikan dan Studi Kebanksentralan (PPSK), 22 ed., vol. 22, no. 22, Pusat Pendidikan dan Studi Kebanksentralan (PPSK) BI, 2009, hal. 1–68.

D. Kertayuga, E. Santoso, dan N. Hidayat, “Prediksi Nilai Ekspor Impor Migas Dan Non-Migas Indonesia Menggunakan Extreme Learning Machine (ELM),” 2021. [Daring]. Tersedia pada: http://j-ptiik.ub.ac.id

P. Rahayu dkk., Buku Ajar Data Mining, vol. 1, no. January 2024. 2018.

Rusdi, “Uji Akar-Akar Unit dalam Model Runtun Waktu Autoregresif,” Satistika, vol. 11, no. 2, hal. 67–78, 2011.

R. D. Anggraeni dan N. A. K. Rifai, “Penerapan Metode Autoregressive Distributed Lag terhadap Faktor yang Mempengaruhi Harga Minyak Goreng Kemasan di Indonesia,” Bandung Conf. Ser. Stat., vol. 3, no. 1, hal. 113–121, 2023, [Daring]. Tersedia pada: https://doi.org/10.29313/bcss.v3i1.6711

S. Johansen, “Statistical Analysis of Cointegration Vectors,” INSTITUTE OF MATHEMATICAL STATISTICS UNIVERSITY OF COPENHAGEN, 1987.

F. K. Dewi dan H. Sudarsono, “Analisis Profitabilitas Bank Syariah di Indonesia: Pendekatan Autoregressive Distributed Lag (ARDL),” Al-Mashrafiyah J. Ekon. Keuangan, dan Perbank. Syariah, vol. 5, no. 1, hal. 59–74, 2021, doi: 10.24252/al-mashrafiyah.v5i1.20281.

M. D. Ariefianto dan I. Trinugroho, Statistik dan Ekonometrika Terapan Aplikasi dengan STATA. 2021.

D. C. Montgomery, C. L. Jennings, dan M. Kulahci, “Introduction to Time Series Analysis and Forecasting,” Second edi., J. T. Editors: David J. Balding, Noel A. C. Cressie, Garrett M. Fitzmaurice, GeofH. Givens, Harvey Goldstein, Geert Molenberghs, David W. Scott, Adrian F. M. Smith, Ruey S. Tsay, Sanford Weisberg Editors Emeriti: J. Stuart Hunter, Iain M. Johnstone, Joseph B. K, Ed., Canada: y John Wiley & Sons, Inc., Hoboken, New Jersey, 2015, hal. 671.

A. Ridha, Nurjannah, dan R. Mutia, “Analisis Permintaan Uang di Indonesia: Pendekatan Autoregressive Distributed Lag (Ardl),” J. Samudra Ekon., vol. 5, no. 2, hal. 152–160, 2021, doi: 10.33059/jse.v5i2.4273.

A. T. Basuki, Pengantar Ekonometrika (Dilengkapi Penggunaan Eviews). 2017.

D. N. Gujarati dan D. C. Porter, Basic Econometrics, 5 ed. 2009.




DOI: https://doi.org/10.18860/jrmm.v4i3.31209

Refbacks

  • There are currently no refbacks.