Prediction of Inflation Rate in Indonesia Using Local Polynomial Estimator for Time Series Data

Vita Fibriyani, N. Chamidah

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Inflation prediction is needed to determine strategies and policies to control a country's economic stability. Inflation is one of the important macroeconomic indicators. Fluctuating inflation rates can disrupt a country's economy, so this is a particular concern. In this paper, inflation prediction was carried out through two approaches: The parametric regression model approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric regression approach based on the local polynomial estimator. The model's accuracy is determined based on prediction results using the mean absolute percentage error (MAPE)value. We obtained a MAPE value of 9.0% for the ARIMA model approach and MAPE values of 4.0% and 1.778% for first and second orders, respectively, of local polynomial nonparametric regression model approach. It means that the best model for predicting Indonesia's inflation rate is the second order of local polynomial nonparametric regression model because it has the smallest MAPE value.

Original languageEnglish
Article number012065
JournalJournal of Physics: Conference Series
Volume1776
Issue number1
DOIs
Publication statusPublished - 3 Feb 2021
Event5th National Conference on Mathematics Research and Its Learning, KNPMP 2020 - Surakarta, Indonesia
Duration: 5 Aug 2020 → …

Keywords

  • inflation rate
  • local polynomial estimator
  • prediction
  • times series

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