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Submitted
Jan 24, 2022
Accepted
Mar 21, 2022
Published
Jun 30, 2022
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Abstract

This classical regression model is designed to handle the relationship between stationary variables and should not be applied to non-stationary series. A time series data is said to be stationary if the mean, variance, and covariance remain constant over time. The problem associated with non-stationary variables, and often encountered by researchers when dealing with time series data, is spurious regression. A clear indicator of false regression is the low Durbin-Watson statistic but has a higher coefficient of determination (R2). Therefore, before doing modeling or forecasting using time series data, it is very important to do a stationary test. In this study, we use inflation data in the City of Bukittinggi from January 2014 to December 2019 as a case study. The data shows an uptrend and correlated error terms. Empirical results show that inflation data in Bukittinggi City is a stationary series.

Keywords

stationary, non autocorrelation, Phillips-Peron Test, Augmented Dickey Fuller Test, Inflasi

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Roza A, Violita ES, Aktivani S. Study of Inflation using Stationary Test with Augmented Dickey Fuller & Phillips-Peron Unit Root Test (Case in Bukittinggi City Inflation for 2014-2019). EKSAKTA [Internet]. 2022Jun.30 [cited 2024Apr.26];23(02):106-1. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/303

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