Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models (original) (raw)
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MODELING AND FORECASTING INFLATION IN NIGERIA: A TIME SERIES REGRESSION WITH ARIMA METHOD
African Journal of Economics and Sustainable Development, 2023
This study uses time series regression with autoregressive integrated moving average (ARIMA) modeling to establish a model for forecasting inflation in Nigeria for the period 1981-2020. Akaike Information Criterion Corrected (AICC) and Bayesian Information Criterion (BIC) were used to select the best model among competing models. Through these methods, regression with ARIMA (0,0,1) error was selected as the most parsimonious model for inflation forecasting in Nigeria. The results of the out-sample-forecast show that a high inflation rate will be experienced by the end of 2023, and between 2024 and 2030, the inflation rate will be alternating but will maintain a lower rate than that of 2023.
Application of Arima Models to Nigerian Inflation Dynamics
Research Journal of Finance and Accounting, 2013
The objectives of this study were to empirically develop a univariate autoregressive integrated moving average (ARIMA) model suggested by Box & Jenkins (1976) for Nigerian inflation and analyze the forecasting performance of the estimated model between 1981 and 2010. In this study, the analyses were carried out with the aid of EViews and Excel softwares. The study used the Ordinary Least Squares (OLS) technique for estimation purposes. On the basis of various diagnostic and selection evaluation criteria the best model was selected for the short term forecasting of Nigerian inflation. The study found ARIMA (2,2,3) as the most appropriate model under model identification, parameter estimation, diagnostic checking and forecasting inflation. In-sample forecasting was attempted and the estimated ARIMA model remarkably tracked the actual inflation during the sample period. The study concluded that Nigerian inflation is largely expectations-driven. The major inference that can be drawn in this study is that expectations that are formed about future levels of prices affect the current purchase decisions. It was recommended that, to put inflation under control, there is need for high transparency in monetary policy making and implementation.
Time Series Modeling and Forecasting Inflation: Evidence from Nigeria
2014
A major concern of entrepreneurs and monetary authorities in Nigeria in the past decades was successful prediction general price level movements. The results allow successful planning on the part of monetary authorities and continued profit drive on the part of entrepreneurs and investors. This study uses a univariate model in the form of Autoregressive Integrated Moving Average model developed by Box and Jenkins and multivariate time series model in the form of Vector Autoregressive model to forecast inflation for Nigeria. This paper use changes in monthly consumer price index obtained from the National Bureau of Statistics and the Central bank of Nigeria over the period 2003 to 2012 to predict movements in the general price level. Based on different diagnostic and evaluation criteria, the best forecasting model for predicting inflation in Nigeria is identified. The results will enable policy makers and businesses to track the performance and stability of key macroeconomic indicators using the forecasted inflation.
2013
This paper describe an empirical study of modeling financial time series data with application to inflation rate data for Nigeria. The theory of univariate non-linear time series analysis is explored and applied to the inflation data spanning from January, 1995 to December, 2011. The diagnostic checking has shown that the fitted model (GARCH(1,0) + ARMA(1,0)) is appropriate. A two-year (24 months) forecast from January 2012 to December 2013 was made. This empirical results have more general implications for small scale macroeconomics and will also be helpful for policy makers and citizens of the Federal Republic of Nigeria.
Forecasting Inflation in Kenya Using Arima-Garch Models
2015
The aim of this study was to empirically develop ARIMA-GARCH models for Kenya inflation and to forecast the rates of inflation using the historical monthly data from 2000 to 2014. The empirical research employs time series analysis, ordinary least square and auto-regressive conditional heteroscedastic to find the estimators. The forecasting inflation analysis have been conducted using two models, the ARIMA (1, 1, 12) model was able to produce forecasts based on the stationarity test and history patterns in the data compared to GARCH (1,2) model. The empirical results of 180 monthly data series indicate that the combination between ARIMA(1,1,12)GARCH(1,2) model provide the optimum results and effectively improved estimating and forecasting accuracy compared to the other previous methods of forecasting.
Inflation dynamics in Niger unlocked: An ARMA approach
2019
This research uses annual time series data on inflation rates in Niger from 1964 to 2017, to model and forecast inflation using ARMA models. Diagnostic tests indicate that N is I(0). The study presents the ARMA (1, 0, 0) model, which is simply an AR (1) model. The diagnostic tests further imply that the presented optimal ARMA (1, 0, 0) model is stable. The results of the study apparently show that N will be approximately 4.3% by 2020. Policy makers and the business community in Niger are expected to take advantage of the anticipated stable inflation rates over the next decade.
Gusau International Journal of Managemnt and Social Sciences, 2021
This paper applies the Autoregressive Integrated Moving Average (ARIMA) methodology of Box-Jenkins (1976) to model and forecast inflation rate in Nigeria, using monthly time series dataset for the period from 2009:1 to 2018:12.The dataset has been subjected to test for unit root using the Augmented Dickey-Fuller (1981), as well as the Zivot and Andrews unit root test which accounts for structural break. The data has been established to be integrated of order1, that is I(1) in both tests, with the break date identified in 2017:01, which necessitates the use of sub sample from 2009:1 to 2016:01 and the entire sample to make comparison and determine the best model that fits the data. The study finds that the ARIMA (2, 1, 13) model using the sub sample data; which considers the structural break date is the parsimonious model as it has passed all the diagnostic tests and thus utilized to predict the future values of the rate of inflation for the period from 2018:7 to 2018:12.The forecast values are not much different from the actual values of the inflation. The study thus recommends the use of ARIMA modeling in forecasting inflation rate in Nigeria, so as to aid policy makers in designing policy measures to cushion the effects of inflation on the living conditions of Nigerians.
An Autoregressive Integrated Moving Average (ARIMA) Model For Ghana’s Inflation (1985 – 2011)
Mathematical Theory and Modeling, 2013
Inflation analysis is indispensable in a developing country like Ghana, which is struggling to achieve the Millennium Development goals. A literature gap exists in appropriate statistical model on economic variables in Ghana, thus motivating the authors to come up with a model that could be used to forecast inflation in Ghana. This paper presents a model of Ghana's monthly inflation from January 1985 to December 2011 and use the model to forecast twelve (12) months inflation for Ghana. Using the Box-Jenkins (1976) framework, the autoregressive integrated moving average (ARIMA) was employed to fit a best model of ARIMA. The seasonal ARIMA model, SARIMA (1, 1, 2) (1, 0, 1) was chosen as the best fitting from the ARIMA family of models with least Akaike Information Criteria (AIC) of 1156.08 and Bayesian Information Criteria (BIC) of 1178.52. The selected model was used to forecast monthly inflation for Ghana for twelve (12) months.
Structural Analysis and Forecast of Nigerian Monthly Inflation Movement between 1996 and 2022
CERN European Organization for Nuclear Research - Zenodo, 2022
Forecasting leads to adequate and comprehensive planning for sustainable development. A number of procedures are used to estimate, predict and forecast data, but not all are able to capture the historical path of the data generating process adequately. In view of this, the timeseries characteristics, structural changes and trend of inflation in Nigeria (1996-2022) were analyzed using ARMA, Holt-Winters, spline and other associated models. The results indicated that inflation in Nigeria has remained above acceptable limits in a cyclical trend during the period under study and that there is every possibility that Nigerian inflation would remain above 10% for some time to come. There were six shocks, the major stressors being food inflation, oil and gas prices and wages adjustment. For Nigeria to achieve a stable inflation rate regime of acceptable limits, a robust economic management and intelligence team using a global innovation platform as well as evidencedbased policies which ensure that Nigeria does not swerve away from the path to recovery should be established in consultation with the fiscal, monetary, and research authorities.
Short-Term Inflation Forecasting Models for Nigeria 1
2014
Short-term inflation forecasting is an essential component of the monetary policy projections at the Central Bank of Nigeria. This paper proposes four short-term headline inflation forecasting models using the SARIMA and SARIMAX processes and compares their performance using the pseudo-outof-sample forecasting procedure over July 2011 to September 2013. According to the results the best forecasting performance is demonstrated by the model based on the all items CPI estimated using the SARIMAX model. This model is, therefore, recommended for use in short-term forecasting of headline inflation in Nigeria. The forecasting performance up to eight months ahead, of the models based on the weighted sum of all items CPI components is relatively bad. For forecast of food inflation up to ten months ahead SARIMA is recommended, but for eleven to twelve months ahead the SARIMAX model performs better. However, the SARIMA model for core inflation consistently outperforms the SARIMAX model and sho...