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
Autoregressive Integrated Moving Average (Arima) Model for the Major Airline Disasters in the World from 1960 Through 2013 (Published)
This research fit a univariate time series model to the major Airline Disasters in the world from 1960 through 2013. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model was estimated and the best fitting ARIMA model was used to obtain the post-sample forecasts for five years. The fitted model was ARIMA (0,1,1) with Akaike Information Criteria (AIC) of 323.14, Normalized Bayesian Information Criteria (BIC) of 327.04, Stationary R2 of 0.348.This model was further validated by Ljung-Box test with no significant Autocorrelation between the residuals at different lag times and subsequently by white noise of residuals from the diagnostic checks performed which clearly portray randomness of the standard error of the residuals, no significant spike in the residual plots of ACF and PACF. The forecasts value indicates that Airline Disasters will increase slightly with almost equal number of cases for the next five years (2014-2018).
Keywords: ARIMA, Airline Disasters, Box- Jenkins, Forecast, Ljung-Box, Stationarity, Time Series, Unit Root
Analysis of the Volatility of the Electricity Costs in Kenya Using Autoregressive Integrated Moving Average Model (Published)
Electricity has proved to be a vital input to most developing economies. As the Kenyan government aims at transforming Kenya into a newly-industrialized and globally competitive, more energy is expected to be used in the commercial sector on the road to 2030. Therefore, modelling and forecasting of electricity costs in Kenya is of vital concern. In this study, the monthly costs of electricity using Autoregressive Integrated Moving Average models (ARIMA) were used so as to determine the most efficient and adequate model for analysing the volatility of the electricity cost in Kenya. Finally, the fitted ARIMA model was used to do an out-off-sample forecasting for electricity cost for September 2013 to August 2016. The forecasting values obtained indicated that the costs will rise initially but later adapt a decreasing trend. A better understanding of electricity cost trend in the small commercial sector will enhance the producers make informed decisions about their products as electricity is a major input in the sector. Also it will assist the government in making appropriate policy measures to maintain or even lowers the electricity cost.
Keywords: ARIMA, Electricity, Forecast, Kenya
Modeling Inflation Rates in Nigeria: Box-Jenkins’ Approach (Published)
This study modeled the inflation rates in Nigeria using Box Jenkins’ time series approach. The data used for the work ware yearly collected data between 1961 and 2013. The empirical study revealed that the most adequate model for the inflation rates is ARIMA (0, 0, 1). The fitted Model was used to forecast the Nigerian inflation rates for a period of 12 years. Based on these results, we recommend effective fiscal policies aimed at monitoring Nigeria’s inflationary trend to avoid damaging consequences on the economy.
Keywords: ACF, ARIMA, Forecasting, Inflation, PACF
Modelling to Anticipate World Price of Each Ounce of Gold in International Markets (Published)
Any change in sale price may affect customers, distributers and sellers. Anticipating future prices is one of the best ways to face appropriately such these price changes in the market. Time series have wide range of application in various fields such as economy, management and marketing. Time series is a very important tool to analyze a collection of observations which are recorded as daily, weekly, monthly and annually reports. In this paper, the world price of each ounce of gold during 338 continuous months are considered (Average per month) and the target is to assess the behavior of data and to release a suitable model for this data to anticipate world price of each ounce of gold during upcoming months by means of analysis of time series. The first step to analyze time series is to draw data. Next step is to recognize effective parameters on the series (trend, cycle and seasonal) and to remove them from time series and at last to process a static model on time series. We drew autocorrelation function (ACF) and partial autocorrelation function (PACF) for data. Auto-regression model (AR), moving average model (MA) and a combination of AR and MA models (ARIMA, ARMA) were selected as the grade of recognition model and appropriate model. After all stages to analyze time series and creation of remained parameters and after consideration of fitness of represented model, anticipation of world price of gold for each ounce will take place. In this regard, the result of considering the data in this paper produces information for future to make appropriate and profitable decision based on current data. The process is done by means of MINITAB software.
Keywords: ACF, ARIMA, Forecast, PACF, Time Series