On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method (Published)
This paper examines the application of autoregressive integrated moving average (ARIMA) model and regression model with ARIMA errors for forecasting Nigeria’s GDP. The data used in this study are collected from the official website of World Bank for the period 1990-2019. A response variable (GDP) and four predictor variables are used for the study. The ARIMA model is fitted only to the response variable, while regression with ARIMA errors is fitted on the data as a whole. The Akaike Information Criterion Corrected (AICc) was used to select the best model among the selected ARIMA models, while the best model for forecasting GDP is selected using measures of forecast accuracy. The result showed that regression with ARIMA(2,0,1) errors is the best model for forecasting Nigeria’s GDP.
Citation: Christogonus Ifeanyichukwu Ugoh, Udochukwu Victor Echebiri, Gabriel Olawale Temisan, Johnpaul Kenechukwu Iwuchukwu, Emwinloghosa Kenneth Guobadia (2022) On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method, International Journal of Mathematics and Statistics Studies, Vol.10, No.4, pp.48-64
Keywords: AICc, ARIMA, GDP, Measures of Forecast Accuracy, Nigeria, Regression with ARIMA errors
Predicting Peak Time Inflation in Nigeria Using Sarima Model (Published)
The often-disturbing adverse effects of inflation in developing economies such as Nigeria necessitates developing dynamic inflation forecasting models for appraising shocks on macroeconomic variables. This work utilizes the Box-Jenkins methodology to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict peak time inflation in Nigeria’s inflation time series from January 2001 to December 2015 obtained from National Bureau of Statistics, Abuja. A test of parameter estimates was performed on the suggested models and, using the AIC and BIC criteria, SARIMA(1,1,2)(2,0,1)12 model was identified as the most fitted model. The diagnostic test of the residuals using ACF and PACF of residual plots showed that they follow white noise process. The result of the monthly forecast indicated that Nigeria will experience high (double digit) inflation rates which will be at its peak in the months of August and September and its lowest rate occurs in January of the year. The information contained here can be useful to ensure monetary and fiscal policies that will stabilize the economy.
Keywords: Nigeria, Peak Time Inflation, Purchasing Power of Money, Sarima Model, consumer prices
Cluster Analysis of the Incidences on HIV in Nigeria (Published)
Data clustering is a vital tool when it comes to understanding data items with similar characteristics in a data set for the sake of grouping. Clustering may be for understanding or utility. Clustering for understanding, which is the focus of this work deals with grouping items with common characteristics in order to better understand a dataset and to identify possible or pre-interest sub-groups that could be formed from such data. The HIV prevalence statistics in Nigeria is measured bi-annually across 36 states and FCT which were zoned under 6 geo-political zones happens to be a suitable data to implement this subject matter. Cluster Analysis was implemented through the general methods of Hierarchical (agglomerative nesting) and Partitioning methods (K-Means). These techniques where implemented on the platform of R (Statistical Computing Language) to cluster HIV prevalence rate in Nigeria so as to find out states that could be considered same category and to investigate the concentration of the disease in respect to geo-political zones. Relative type of validation was used for cluster validation (a mechanism for evaluating the correctness of clustering).
Keywords: Clustering analysis, HIV, Nigeria, Pregnant Women, data
Multiplicative Sarima Modelling of Daily Naira – Euro Exchange Rates (Published)
The time plot of the series DNEER shows an upward secular trend from early December 2012 to early February 2013 followed by a downward trend till end of March 2013. No seasonality is observable. A seven-day differencing yields the series SDDNEER with an overall slightly negative trend. Seasonality is still not discernible. A further (non-seasonal) differencing yields the series DSDDNEER which has an overall horizontal trend. The correlogram of DSDDNEER shows a negative significant spike at lag 7 and comparable spikes at lags 6 and 8. This reveals seven-day seasonality as suspected. It also suggests the involvement of a seasonal moving average component of order one and the product of two moving average components: one seasonal and the other non-seasonal, both of order one. Hence a (0, 1, 1)x(0, 1, 1)7 SARIMA model is proposed. It is fitted and shown to be adequate for the data
Keywords: Daily Naira – Euro Exchange Rates, Nigeria, SARIMA models