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2019•Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)Open access

Modeling and forecasting inflation in Burundi using ARIMA models

Thabani Nyoni

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Abstract

This research uses annual time series data on inflation rates in Burundi from 1966 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that B is I(1). The study presents the ARIMA (0, 1, 1). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 1) model is stable and acceptable for predicting inflation in Burundi. The results of the study apparently show that B will be approximately 9.4% by 2020. Policy makers, particulary, monetary authorities in Burundi are expected to tighten Burundi’s monetary policy in order to restore price stability.

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What this paper is about

This research uses annual time series data on inflation rates in Burundi from 1966 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that B is I(1). The study presents the ARIMA (0, 1, 1). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 1) model is stable and acceptable for predicting inflation in Burundi. The results of the study apparently show that B will be approximately 9.4% by 2020. Policy makers, particulary, monetary authorities in Burundi are expected to tighten Burundi’s monetary policy in order to restore price stability.

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Available abstract

This research uses annual time series data on inflation rates in Burundi from 1966 to 2017, to model and forecast inflation using ARIMA models. Diagnostic tests indicate that B is I(1). The study presents the ARIMA (0, 1, 1). The diagnostic tests further imply that the presented optimal ARIMA (0, 1, 1) model is stable and acceptable for predicting inflation in Burundi. The results of the study apparently show that B will be approximately 9.4% by 2020. Policy makers, particulary, monetary authorities in Burundi are expected to tighten Burundi’s monetary policy in order to restore price stability.

Key concepts: Autoregressive integrated moving average, Inflation (cosmology), Economics, Econometrics, Time series, Monetary policy, Macroeconomics, Statistics

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