Main Article Content

Abstract

Fluctuations in world crude oil prices significantly impact oil-importing countries like Indonesia. These fluctuations affect multiple sectors, including the import of consumer goods, which is vital for the domestic economy. In Indonesia, consumer goods are crucial for economic stability. Thus, understanding how oil price fluctuations influence imports is essential for policymaking. This study investigates the relationship between world crude oil price fluctuations and the import value of consumer goods in Indonesia over an extended period. A quantitative approach was used, treating world crude oil prices as the independent variable and the import value of consumer goods as the dependent variable. Linear regression analysis, supported by descriptive statistical methods, was employed to assess this relationship. The results indicate that world crude oil prices have a significant effect on the import value of consumer goods, with data showing near-normal distribution and low skewness and kurtosis, validating the model. However, positive autocorrelation in the residuals suggests the need for further analysis to ensure the reliability of the model. These findings offer important insights for policymakers on managing the economic impact of oil price volatility on Indonesia’s domestic market. Future research could explore additional factors, such as inflation or trade policy, that may influence this relationship, contributing to more effective economic strategies.

Keywords

Crude oil prices consumer goods linear regression Indonesian imports time series analysis

Article Details

How to Cite
Handayani, U., Ruslan, D., & Paidi, P. (2025). Pengaruh Harga Rata-Rata Minyak Mentah Dunia terhadap Penawaran Barang Konsumsi di Indonesia. Ecoplan, 8(1), 16-26. https://doi.org/10.20527/ecoplan.v8i1.1098

References

  1. Adhikari, G. (2022). Interpreting the basic results of multiple linear regression. Scholars Journal, 22–37.
  2. Arintoko, A. (2023). Asymmetric effects of world energy prices on inflation in Indonesia. International Journal of Energy Economics and Policy, 13(6), 185–193. https://doi.org/10.32479/ijeep.14731
  3. Ayadi, O. (2005). Oil price fluctuations and the Nigerian economy. Opec Review, 29(3), 199–217. https://doi.org/10.1111/j.0277-0180.2005.00151.x
  4. Berentzen, T., Ängquist, L., Kotronen, A., Borra, R., Yki-Järvinen, H., Iozzo, P., & Jakobsen, M. (2012). Waist circumference adjusted for body mass index and intra-abdominal fat mass. Plos One, 7(2), e32213. https://doi.org/10.1371/journal.pone.0032213
  5. Bistacchi, A., Mittempergher, S., Martinelli, M., & Storti, F. (2020). On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity). Solid Earth, 11(6), 2535–2547. https://doi.org/10.5194/se-11-2535-2020
  6. Charlot, F., & Rachdi, M. (2008). On the statistical properties of a stationary process sampled by a stationary point process. Statistics & Probability Letters, 78(4), 456–462. https://doi.org/10.1016/j.spl.2007.07.019
  7. Chelli, A., & Patzold, M. (2011). A non-stationary mimo vehicle-to-vehicle channel model derived from the geometrical t-junction model.
  8. Dahlhaus, R., Richter, S., & Wu, W. (2019). Towards a general theory for nonlinear locally stationary processes. Bernoulli, 25(2). https://doi.org/10.3150/17-bej1011
  9. Darmawan, I., Siregar, H., Hakim, D., & Manurung, A. (2020). The effect of crude oil price shocks on Indonesia stock market performance. Jurnal Organisasi Dan Manajemen, 16(1), 11–23. https://doi.org/10.33830/jom.v16i1.785.2020
  10. Destiarni, R., & Jamil, A. (2021). Price integration analysis of crude oil and vegetable oils. Habitat, 32(2), 82–92. https://doi.org/10.21776/ub.habitat.2021.032.2.10
  11. Dette, H., Preuß, P., & Vetter, M. (2011). A measure of stationarity in locally stationary processes with applications to testing. Journal of the American Statistical Association, 106(495), 1113–1124. https://doi.org/10.1198/jasa.2011.tm10811
  12. Firmansyah, M., Boedirochminarni, A., Riyanto, W., & Tsalasa, A. (2024). Do cashless transactions and credit distribution affect inflation in Indonesia? Ecoplan, 7(2), 144–153. https://doi.org/10.20527/ecoplan.v7i2.728
  13. Gao, X., & Fang, Y. (2016). Penalized weighted least squares for outlier detection and robust regression.
  14. Ghosh, H., & Paul, R. (2010). Wavelet frequency domain approach for statistical modeling of rainfall time-series data. Journal of Statistical Theory and Practice, 4(4), 813–825. https://doi.org/10.1080/15598608.2010.10412020
  15. Guo, H., & Kliesen, K. (2005). Oil price volatility and U.S. macroeconomic activity. Review of Economic Dynamics, 87(6). https://doi.org/10.20955/r.87.669-84
  16. Hayashi, Y., & Zhang, N. (2015). Evaluation of measurement precision from stationary baseline noise in instrumental analyses. Analytical Sciences, 31(12), 1219–1224. https://doi.org/10.2116/analsci.31.1219
  17. Hesikumalasari, H., Budiantara, I., Ratnasari, V., & Nisa, K. (2022). Estimation of semiparametric regression curve with mixed estimator of multivariable linear truncated spline and multivariable kernel. Media Statistika, 15(1), 12–23. https://doi.org/10.14710/medstat.15.1.12-23
  18. Hu, S., Xi, Z., McGowin, G., Sukthankar, G., Fiore, S., & Oden, K. (2021). Representing time series data in intelligent training systems. The International Flairs Conference Proceedings, 34(1). https://doi.org/10.32473/flairs.v34i1.128508
  19. Hu, Z. (2021). Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index. Oil & Gas Science and Technology – Revue D’IFP Energies Nouvelles, 76, 28. https://doi.org/10.2516/ogst/2021010
  20. Ibrahim, M. (2008). Growth prospects of oil and gas abundant economies: the Nigerian experience (1970–2000). Journal of Economic Studies, 35(2), 170–190. https://doi.org/10.1108/01443580810870155
  21. Imam, A. (2021). Untitled. Asian Journal of Mathematical Sciences, 4(4). https://doi.org/10.22377/ajms.v4i4.295
  22. Iwueze, I., Nwogu, E., Ohakwe, J., & Ajaraogu, J. (2011). Uses of the Buys-Ballot table in time series analysis. Applied Mathematics, 2(5), 633–645. https://doi.org/10.4236/am.2011.25084
  23. Kim, S., Park, H., Jung, H., Lee, J., & Lim, K. (2021). Estimation of health-related physical fitness using multiple linear regression in Korean adults: National Fitness Award 2015–2019. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.668055
  24. Koo, B., & Linton, O. (2012). Estimation of semiparametric locally stationary diffusion models. Journal of Econometrics, 170(1), 210–233. https://doi.org/10.1016/j.jeconom.2012.05.003
  25. Kuntz, J., Ottobre, M., Stan, G., & Barahona, M. (2016). Bounding stationary averages of polynomial diffusions via semidefinite programming. SIAM Journal on Scientific Computing, 38(6), A3891–A3920. https://doi.org/10.1137/16m107801x
  26. Li, J., Wang, X., & Wang, Z. (2023). Whether the air transport is going through the difficulties: evidence from dynamic changes in brent oil prices. BCP Business & Management, 38, 1386–1395. https://doi.org/10.54691/bcpbm.v38i.3901
  27. Lieshout, v. (2011). A J–function for inhomogeneous point processes. Statistica Neerlandica, 65(2), 183–201. https://doi.org/10.1111/j.1467-9574.2011.00482.x
  28. Liu, Z., Ren, L., Xiao, C., Zhang, K., & Demian, P. (2022). Virtual reality aided therapy towards health 4.0: a two-decade bibliometric analysis. International Journal of Environmental Research and Public Health, 19(3), 1525. https://doi.org/10.3390/ijerph19031525
  29. Makala, D., & Li, Z. (2019). Economic forecasting with deep learning: crude oil. Matter International Journal of Science and Technology, 5(2), 213–228. https://doi.org/10.20319/mijst.2019.52.213228
  30. Mishra, P., Pandey, C., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103/aca.aca_157_18
  31. Nandha, M., & Faff, R. (2008). Does oil move equity prices? a global view. Energy Economics, 30(3), 986–997. https://doi.org/10.1016/j.eneco.2007.09.003
  32. Nasir, J., Aamir, M., Haq, Z., Khan, S., Amin, M., & Naeem, M. (2023). A new approach for forecasting crude oil prices based on stochastic and deterministic influences of LMD using ARIMA and LSTM models. IEEE Access, 11, 14322–14339. https://doi.org/10.1109/access.2023.3243232
  33. Novković, N., Vukelić, N., Šarac, V., & Nikolić, S. (2022). State and tendencies of production characteristics of wheat and maize in Serbia. Journal on Processing and Energy in Agriculture, 26(2), 68–70. https://doi.org/10.5937/jpea26-37904
  34. Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2011). Combining qualitative and quantitative research within mixed method research designs: a methodological review. International Journal of Nursing Studies, 48(3), 369–383. https://doi.org/10.1016/j.ijnurstu.2010.10.005
  35. Paparoditis, E. (2009). Testing temporal constancy of the spectral structure of a time series. Bernoulli, 15(4). https://doi.org/10.3150/08-bej179
  36. Prabheesh, K., & Laila, N. (2020). Asymmetric effect of crude oil and palm oil prices on economic growth: evidence from Indonesia. Buletin Ekonomi Moneter Dan Perbankan, 23(2), 253–268. https://doi.org/10.21098/bemp.v23i1.1304
  37. Priwiningsih, V., & Abidin, A. (2022). Literature study of cooking oil scarcity and the increase of cooking oil prices in Indonesia. Proceedings of International Conference on Economics Business and Government Challenges, 5(1), 87–92. https://doi.org/10.33005/ic-ebgc.v1i1.14
  38. Pusparum, M., Kurnia, A., & Alamudi, A. (2017). Winsor approach in regression analysis with outlier. Applied Mathematical Sciences, 11, 2031–2046. https://doi.org/10.12988/ams.2017.76214
  39. Richardson, D., MacLehose, R., Langholz, B., & Cole, S. (2011). Hierarchical latency models for dose-time-response associations. American Journal of Epidemiology, 173(6), 695–702. https://doi.org/10.1093/aje/kwq387
  40. Steinitz, G., Piatibratova, O., & Barbosa, S. (2007). Radon daily signals in the Elat granite, southern Arava, Israel. Journal of Geophysical Research: Atmospheres, 112(B10). https://doi.org/10.1029/2006jb004817
  41. Suherman, S., Syaifuddin, S., & Faris, S. (2022). The effect of leadership style and career development on employee performance at CV Setia Kawan Medan. International Journal of Science Technology & Management, 3(5), 1460–1464. https://doi.org/10.46729/ijstm.v3i5.588
  42. Suryani, D., Fadhilla, M., & Labellapansa, A. (2022). Indonesian crude oil price (ICP) prediction using multiple linear regression algorithm. Jurnal Resti (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 1057–1063. https://doi.org/10.29207/resti.v6i6.4590
  43. Syah, H., Irmansyah, J., Hulfian, L., & Lubis, M. (2022). Hybrid learning space as an alternative for physical education learning post COVID-19 pandemic. International Journal of Human Movement and Sports Sciences, 10(5), 1047–1059. https://doi.org/10.13189/saj.2022.100523
  44. Vandyck, T., Kitous, A., Saveyn, B., Keramidas, K., Santos, L., & Wójtowicz, K. (2018). Economic exposure to oil price shocks and the fragility of oil-exporting countries. Energies, 11(4), 827. https://doi.org/10.3390/en11040827
  45. Velicer, W., & Fava, J. (2003). Time series analysis. In Encyclopedia of Statistical Sciences (pp. 581–606). https://doi.org/10.1002/0471264385.wei0223
  46. Wen, F., Ming, F., Zhang, Y., & Yang, C. (2018). Crude oil price shocks, monetary policy, and China’s economy. International Journal of Finance & Economics, 24(2), 812–827. https://doi.org/10.1002/ijfe.1692
  47. Williamson, J., Lin, H., & Lyles, R. (2023). A censored quantile regression approach for relative survival analysis: relative survival quantile regression. Biometrical Journal, 65(5). https://doi.org/10.1002/bimj.202200127