DOI: https://doi.org/10.14710/jfma.v7i1.20502

ROBUST PREDICTION INTERVALS FOR INDONESIAN INFLATION: A BIAS-CORRECTED BOOTSTRAP APPROACH

*Umi Mahmudah orcid  -  Universitas Islam Negeri K.H. Abdurrahman Wahid, Pekalongan, Indonesia
Siti Fatimah  -  Institut Agama Islam Nahdlatul Ulama, Kebumen, Indonesia

Citation Format:
Abstract

Inflation is important to be analyzed due to its impact is felt across various aspects of the economy and individuals' lives. This research aimed to develop robust and reliable predictions concerning Indonesian inflation using the bias-corrected bootstrap method for an AR model. The data utilized spanned from January 2020 to September 2023 and was obtained from Bank Indonesia's website. The analysis provided the optimal order in the AR model, which resulted in p=2 as the best order (AIC=-1.858, BIC=-1.698, and HQ=-1.798). The number of bootstrap replications used was B=100, 250, 500, and 1000. The analysis was conducted using R Studio. The analysis results indicated that the model employed for prediction analysis was highly stable, with all point forecasts indicating result consistency. The prediction results suggested that inflation in Indonesia was expected to decrease in the upcoming 5 months. The results also revealed that the bias-corrected bootstrap approach could provide forecasting results with a higher level of accuracy. This research contributed to the understanding and forecasting of Indonesian inflation, emphasizing model stability and consistent results.

Fulltext View|Download
Keywords: inflation; AR model; bias-corrected bootstrap; prediction

Article Metrics:

Article Info
Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS
Language : EN
Recent articles
PRIME LABELING OF SOME WEB GRAPHS WITHOUT CENTER ANALYSIS OF THE EFFECT OF STUART NUMBER AND RADIATION ON VISCOUS FLUID FLOW CONSTRUCTION OF FUNDAMENTAL THEOREMS OF FRACTIONAL CALCULUS More recent articles
  1. I. Rahman, R. T. Ratnasari, and A. K. Wardhana, “Effect of certificate of Bank Indonesia Sharia and Indonesian bank seven days repository rate to inflation ratio in Indonesia during COVID-19 Pandemic,” Econ. Educ. Entrep. J., vol. 5, no. 1, pp. 157–174, 2022
  2. H. Aginta and M. Someya, “Regional economic structure and heterogeneous effects of monetary policy: evidence from Indonesian provinces,” J. Econ. Struct., vol. 11, no. 1, p. 1, 2022
  3. S. Devi, N. M. S. Warasniasih, P. R. Masdiantini, and L. S. Musmini, “The impact of COVID-19 pandemic on the financial performance of firms on the Indonesia stock exchange,” J. Econ. Business, Account. Ventur., vol. 23, no. 2, pp. 226–242, 2020
  4. S. Olivia, J. Gibson, and R. an Nasrudin, “Indonesia in the Time of Covid-19,” Bull. Indones. Econ. Stud., vol. 56, no. 2, pp. 143–174, 2020
  5. S. E. Obi, T. Yunusa, A. N. Еzеoguеri-Oyеwolе, S. S. Sekpe, E. Egwemi, and A. S. Isiaka, “The socio-economic impact of covid-19 on the economic activities of selected states in Nigeria,” Indones. J. Soc. Environ. Issues, vol. 1, no. 2, pp. 39–47, 2020
  6. X. Jaravel and M. O’Connell, “Real-time price indices: Inflation spike and falling product variety during the Great Lockdown,” J. Public Econ., vol. 191, p. 104270, 2020
  7. H. Tiirinki, M. Viita-Aho, L.-K. Tynkkynen, M. Sovala, V. Jormanainen, and I. Keskimäki, “COVID-19 in Finland: Vaccination strategy as part of the wider governing of the pandemic,” Heal. Policy Technol., vol. 11, no. 2, p. 100631, 2022
  8. A. Munifatussa’idah and K. Saleh, “Indonesia Monetary Policy During COVID-19 Outbreak: In Islamic Economic Perspective,” Am. J. Humanit. Soc. Sci. Res., vol. 4, no. 10, pp. 56–63, 2020
  9. M. P. Clements and N. Taylor, “Bootstrapping prediction intervals for autoregressive models,” Int. J. Forecast., vol. 17, no. 2, pp. 247–267, 2001
  10. G. Masarotto, “Bootstrap prediction intervals for autoregressions,” Int. J. Forecast., vol. 6, no. 2, pp. 229–239, 1990
  11. R. Errouissi, J. Cardenas-Barrera, J. Meng, E. Castillo-Guerra, X. Gong, and L. Chang, “Bootstrap prediction interval estimation for wind speed forecasting,” in 2015 IEEE Energy Conversion Congress and Exposition (ECCE), 2015, pp. 1919–1924
  12. J. H. Kim, H. Song, and K. K. F. Wong, “Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting,” J. Forecast., vol. 29, no. 7, pp. 655–672, 2010
  13. M. R. M. R. Chernick and R. A. R. A. R. A. LaBudde, An introduction to bootstrap methods with applications to R. John Wiley & Sons, 2014
  14. G. Dikta and M. Scheer, Bootstrap Methods: with Application in R. Springer, 2021
  15. L. A. Thombs and W. R. Schucany, “Bootstrap prediction intervals for autoregression,” J. Am. Stat. Assoc., vol. 85, no. 410, pp. 486–492, 1990
  16. E. Paparoditis, “Sieve bootstrap for functional time series,” Ann. Stat., vol. 46, no. 6B, pp. 3510–3538, 2018
  17. M. P. Clements and J. H. Kim, “Bootstrap prediction intervals for autoregressive time series,” Comput. Stat. Data Anal., vol. 51, no. 7, pp. 3580–3594, 2007
  18. D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to time series analysis and forecasting. John Wiley & Sons, 2015
  19. J. Brownlee, Introduction to time series forecasting with python: how to prepare data and develop models to predict the future. Machine Learning Mastery, 2017
  20. K. Neusser, Time series econometrics. Springer, 2016
  21. J. Zhang, Z. Zhao, Y. Xue, Z. Chen, X. Ma, and Q. Zhou, “Time series analysis,” Handb. Med. Stat., vol. 269, 2017
  22. J. H. Kim, “Bootstrap-after-bootstrap prediction intervals for autoregressive models,” J. Bus. Econ. Stat., vol. 19, no. 1, pp. 117–128, 2001
  23. Y. S. Lee and S. Scholtes, “Empirical prediction intervals revisited,” Int. J. Forecast., vol. 30, no. 2, pp. 217–234, 2014
  24. R. J. R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2018
  25. U. Mahmudah, S. Surono, P. Wahyu Prasetyo, and A. E Haryati, “Forecasting educated unemployed people in Indonesia using the Bootstrap Technique,” J. Mahani Math. Res., pp. 171–182, 2023, doi: 10.22103/JMMR.2022.19368.1239
  26. A. M. De Livera, R. J. Hyndman, and R. D. Snyder, “Forecasting time series with complex seasonal patterns using exponential smoothing,” J. Am. Stat. Assoc., vol. 106, no. 496, pp. 1513–1527, 2011, doi: https://doi.org/10.1198/jasa.2011.tm09771
  27. M. Chamdani, U. Mahmudah, and S. Fatimah, “Prediction of Illiteracy Rates in Indonesia Using Time Series,” Int. J. Educ., vol. 12, no. 1, pp. 34–41, 2019, doi: 10.17509/ije.v12i1.16589

Last update:

No citation recorded.

Last update:

No citation recorded.