Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models

MSc (Statistics) === Department of Statistics === Over the years researchers have been modeling inflation rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown...

Full description

Bibliographic Details
Main Author: Antwi, Emmanuel
Other Authors: Kyei, K. A.
Format: Others
Language:en
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/11602/963
id ndltd-netd.ac.za-oai-union.ndltd.org-univen-oai-univendspace.univen.ac.za-11602-963
record_format oai_dc
spelling ndltd-netd.ac.za-oai-union.ndltd.org-univen-oai-univendspace.univen.ac.za-11602-9632020-05-07T03:17:23Z Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models Antwi, Emmanuel Kyei, K. A. Gyampi, E. N Inflation Nonlinear models Self-exciting threshold autoregression model Logistic smooth 332.4109667 Inflation (Economics) -- Ghana Ghana -- Economic conditions Finance -- Ghana Money -- Ghana Currency -- Ghana MSc (Statistics) Department of Statistics Over the years researchers have been modeling inflation rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate, does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear models and to establish the existence of nonlinear patterns in the monthly rates of inflation between the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level, and hence we can conclude that the rate of inflation was stationary over the period under consideration. The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable characteristic feature in most time series. Monthly rates of inflation were modeled using threshold models and their fitness and forecasting performance were compared with Autoregressive (AR ) models. Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess each of the fitted models such that the model with the minimum value of AIC and BIC, was judged the best model. Additionally, the fitted models were compared according to their forecasting performance using a criterion called mean absolute percentage error (MAPE). The model with the minimum MAPE emerged as the best forecast model and then the model was used to forecast monthly inflation rates for the year 2017. The rationale for choosing this type of model is contingent on the behaviour of the time-series data. Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform better than linear models. The study found that the SETAR and LSTAR models fit the data best. The simple AR models however, out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains i made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which can use the data to put in place coherent monetary and fiscal policies that would put the anticipated increase in inflation under control. 2017-11-14T13:44:14Z 2017-11-14T13:44:14Z 2017-09-18 Dissertation http://hdl.handle.net/11602/963 en University of Venda 1 online resource (xiii, 82 leaves : color illustrations)
collection NDLTD
language en
format Others
sources NDLTD
topic Inflation
Nonlinear models
Self-exciting threshold autoregression model
Logistic smooth
332.4109667
Inflation (Economics) -- Ghana
Ghana -- Economic conditions
Finance -- Ghana
Money -- Ghana
Currency -- Ghana
spellingShingle Inflation
Nonlinear models
Self-exciting threshold autoregression model
Logistic smooth
332.4109667
Inflation (Economics) -- Ghana
Ghana -- Economic conditions
Finance -- Ghana
Money -- Ghana
Currency -- Ghana
Antwi, Emmanuel
Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
description MSc (Statistics) === Department of Statistics === Over the years researchers have been modeling inflation rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate, does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear models and to establish the existence of nonlinear patterns in the monthly rates of inflation between the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level, and hence we can conclude that the rate of inflation was stationary over the period under consideration. The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable characteristic feature in most time series. Monthly rates of inflation were modeled using threshold models and their fitness and forecasting performance were compared with Autoregressive (AR ) models. Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess each of the fitted models such that the model with the minimum value of AIC and BIC, was judged the best model. Additionally, the fitted models were compared according to their forecasting performance using a criterion called mean absolute percentage error (MAPE). The model with the minimum MAPE emerged as the best forecast model and then the model was used to forecast monthly inflation rates for the year 2017. The rationale for choosing this type of model is contingent on the behaviour of the time-series data. Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform better than linear models. The study found that the SETAR and LSTAR models fit the data best. The simple AR models however, out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains i made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which can use the data to put in place coherent monetary and fiscal policies that would put the anticipated increase in inflation under control.
author2 Kyei, K. A.
author_facet Kyei, K. A.
Antwi, Emmanuel
author Antwi, Emmanuel
author_sort Antwi, Emmanuel
title Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
title_short Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
title_full Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
title_fullStr Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
title_full_unstemmed Modeling and Forecasting Ghana's Inflation Rate Under Threshold Models
title_sort modeling and forecasting ghana's inflation rate under threshold models
publishDate 2017
url http://hdl.handle.net/11602/963
work_keys_str_mv AT antwiemmanuel modelingandforecastingghanasinflationrateunderthresholdmodels
_version_ 1719314521140494336