2022Proceedings of the International Seminar on Business Education and ScienceOpen access

Inflation Forecasting In East Java Using Autoregressive Integrated Moving Average Method

Imam Mukhtar Syarifudin, Ardhi Sanwidi, M. Nur Haqqul Qomarudin

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Abstract

Inflation is an economic event that often occurs even though it is not wanted. Based on data from Badan Pusat Statistik in 2015-2020 inflation in East Java was 3.08%, 2.74%, 4.04%, 2.86%, 2.12%, 1.44%. From these data, it can be seen that inflation data is fluctuating. Therefore it is necessary to control inflation because high and unstable inflation can have a negative impact on the socio-economic conditions of the community. In addition, it also makes it difficult for the government to determine future policies. Seeing the importance of controlling inflation, it is necessary to study to predict the inflation rate in the future. One of the studies/methods to predict that is often used is the Autoregressive Integrated Moving Average (ARIMA) method or also known as the Box-Jenkins method. The ARIMA method is a method that is easy to use because it is flexible in following existing data patterns and has high accuracy and tends to have a small error value because of the detailed process. From the analysis results, the best ARIMA (p,d,q) model is the ARIMA model (2,1,1) with an AIC value of 76.77. The results of forecasting with the ARIMA model (2,1,1) respectively are 0.2593698, 0.1892990, 0.1340639, 0.1368309, 0.1572021, 0.1642381, 0.1598897, 0.1557251, 0.1556074, 0.1570151, 0.1576092, 0.1573511, 0.1570423, and 0.1570111.

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Inflation is an economic event that often occurs even though it is not wanted. Based on data from Badan Pusat Statistik in 2015-2020 inflation in East Java was 3.08%, 2.74%, 4.04%, 2.86%, 2.12%, 1.44%. From these data, it can be seen that inflation data is fluctuating. Therefore it is necessary to control inflation because high and unstable inflation can have a negative impact on the socio-economic conditions of the community. In addition, it also makes it difficult for the government to determine future policies. Seeing the importance of controlling inflation, it is necessary to study to predict the inflation rate in the future. One of the studies/methods to predict that is often used is the Autoregressive Integrated Moving Average (ARIMA) method or also known as the Box-Jenkins method. The ARIMA method is a method that is easy to use because it is flexible in following existing data patterns and has high accuracy and tends to have a small error value because of the detailed process. From the analysis results, the best ARIMA (p,d,q) model is the ARIMA model (2,1,1) with an AIC value of 76.77. The results of forecasting with the ARIMA model (2,1,1) respectively are 0.2593698, 0.1892990, 0.1340639, 0.1368309, 0.1572021, 0.1642381, 0.1598897, 0.1557251, 0.1556074, 0.1570151, 0.1576092, 0.1573511, 0.1570423, and 0.1570111.

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Available abstract

Inflation is an economic event that often occurs even though it is not wanted. Based on data from Badan Pusat Statistik in 2015-2020 inflation in East Java was 3.08%, 2.74%, 4.04%, 2.86%, 2.12%, 1.44%. From these data, it can be seen that inflation data is fluctuating. Therefore it is necessary to control inflation because high and unstable inflation can have a negative impact on the socio-economic conditions of the community. In addition, it also makes it difficult for the government to determine future policies. Seeing the importance of controlling inflation, it is necessary to study to predict the inflation rate in the future. One of the studies/methods to predict that is often used is the Autoregressive Integrated Moving Average (ARIMA) method or also known as the Box-Jenkins method. The ARIMA method is a method that is easy to use because it is flexible in following existing data patterns and has high accuracy and tends to have a small error value because of the detailed process. From the analysis results, the best ARIMA (p,d,q) model is the ARIMA model (2,1,1) with an AIC value of 76.77. The results of forecasting with the ARIMA model (2,1,1) respectively are 0.2593698, 0.1892990, 0.1340639, 0.1368309, 0.1572021, 0.1642381, 0.1598897, 0.1557251, 0.1556074, 0.1570151, 0.1576092, 0.1573511, 0.1570423, and 0.1570111.

Key concepts: Autoregressive integrated moving average, Inflation (cosmology), Econometrics, Moving average, Java, Autoregressive model, Moving-average model, Value (mathematics)

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