Change-Point Detection with ARIMA of Inflation Rate in Ghana

Change-point detection is the point or location in the series where the observations of that series are shifted to another point. Inflation is considered as one of the most important determinants of economic growth and also a key macroeconomic indicator which shows how prices of goods change from one period to another and this plays a critical role in economic stability and growth. The study therefore, aimed to determine structural change-point(s) of the inflation rate in Ghana, which will serve as an essential source of information to guide policy direction. Annual data on Ghana’s inflation rate were sourced from the World Bank website covering the years 1965-2025. To remove the effect of serial correlation since the inflation data was collected over time, an ARIMA model was considered and the errors which were independent and identically distributed, were extracted for multiple change point procedures. Change point methods considered were the Cumulative Sum (CUSUM) Test, the Binary Segmentation (BS) Method and the Pruned Exact Linear Time (PELT) Algorithm. We sought to determine change points in mean, variance (risk) and mean-variance jointly since they are the basic measured quantities for econometric analysis. Results show that the mean change point was detected at time (index) 12, which represents the year 1976, corresponding to Ghana’s mid-1970s macroeconomic instability. Variance (risk) change points were detected at time points 37 and 56, which represent the years 2001 and 2020, respectively corresponding to times of fiscal stress (Ghana joining the Heavily Indebted Poor Countries (HIPC)), electoral spending and COVID-19 shock. The mean-variance change points were also detected at time points 10 and 20, which represent the years 1974 and 1984, respectively aligning with the oil shock era and the economic recovery programme (ERP) regime. The study showed that Ghana’s inflation process has experienced multiple structural shifts associated with major economic shocks and policy transitions. It is highly recommended that credible macroeconomic management and fiscal discipline be adhered to during structural changes.

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Publication Details

Journal
American Journal of Theoretical and Applied Statistics
Published
2026-04-15
DOI
https://doi.org/10.11648/j.ajtas.20261502.13
Primary Topic
Monetary Policy and Economic Impact
Type
article
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article

Change-Point Detection with ARIMA of Inflation Rate in Ghana

Richard Puurbalanta, Angela Osei-Mainoo, Issah Zakaria, Gideon Engmann
American Journal of Theoretical and Applied Statistics
Monetary Policy and Economic Impact
article

Change-Point Detection with ARIMA of Inflation Rate in Ghana

Richard Puurbalanta, Angela Osei-Mainoo, Issah Zakaria, Gideon Engmann
article en

Abstract

Change-point detection is the point or location in the series where the observations of that series are shifted to another point. Inflation is considered as one of the most important determinants of economic growth and also a key macroeconomic indicator which shows how prices of goods change from one period to another and this plays a critical role in economic stability and growth. The study therefore, aimed to determine structural change-point(s) of the inflation rate in Ghana, which will serve as an essential source of information to guide policy direction. Annual data on Ghana’s inflation rate were sourced from the World Bank website covering the years 1965-2025. To remove the effect of serial correlation since the inflation data was collected over time, an ARIMA model was considered and the errors which were independent and identically distributed, were extracted for multiple change point procedures. Change point methods considered were the Cumulative Sum (CUSUM) Test, the Binary Segmentation (BS) Method and the Pruned Exact Linear Time (PELT) Algorithm. We sought to determine change points in mean, variance (risk) and mean-variance jointly since they are the basic measured quantities for econometric analysis. Results show that the mean change point was detected at time (index) 12, which represents the year 1976, corresponding to Ghana’s mid-1970s macroeconomic instability. Variance (risk) change points were detected at time points 37 and 56, which represent the years 2001 and 2020, respectively corresponding to times of fiscal stress (Ghana joining the Heavily Indebted Poor Countries (HIPC)), electoral spending and COVID-19 shock. The mean-variance change points were also detected at time points 10 and 20, which represent the years 1974 and 1984, respectively aligning with the oil shock era and the economic recovery programme (ERP) regime. The study showed that Ghana’s inflation process has experienced multiple structural shifts associated with major economic shocks and policy transitions. It is highly recommended that credible macroeconomic management and fiscal discipline be adhered to during structural changes.

American Journal of Theoretical and Applied StatisticsVol. 15(2)
Navrongo Health Research Centre (GH)
No poverty
Openalex Percentile: Top 8%
Monetary Policy and Economic Impact
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