A Methodology for Stochastic Monitoring Of Macro-Economic Variables in Ghana

Abstract

The study is based on the stochastic monitoring of Macroeconomic variables in Ghana. The macroeconomic variables that were considered in the study included Inflation rate and the Gross Domestic Product (GDP). The data covered the period from 2009 to 2017 for the monthly inflation rate and 1961 to 2017 for the GDP. All analyses were done using the R software. The Augmented Dickey-Fuller test was used to test the data and the results showed that all the data were stationary after the data was transformed by differencing the data once. The performances of the models were tested using the Akaike Information Criterion (AIC). The models with the least AIC values were selected and subjected to the Box Pierce and L-Jung diagnostic tests. Based on the diagnostics tests, the ARIMA (1,1,2) with the highest p-value of 0.9669 at 5% level of significance was selected as the best model for the Inflation rate time series analysis and the ARIMA (1,1,1) with the highest p-value of 0.9892 was chosen as the best model for the GDP time series data. The residuals were obtained from the appropriate models to obtain the quantile values to set upper and lower bounds around the forecasted values using the expected trend lines. Both the linear and curve-linear expected trend lines were employed in the study. In monitoring the achievement of the set targets, the forecast accuracies were estimated using the Mean Absolute Percentage Error. The results showed that both variables performed well when preceding values were around a linear expected trend line. It also showed that the methodology performs well for both high targeted and low targeted variables. It is recommended from the study that Policy makers and governments should employ the methodology in monitoring achievement of set targets. It is also recommended that researchers should consider further studies using other non-linear models and different periods for the forecasting.