On Modeling Murder Crimes in Nigeria

This paper examines the modeling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twentynine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

Keywords: Crime; Forecasting; ARIMA model; Murder; Box-jenkins method; Akaike information criteria; Bayesian information criteria; Hannanquinn criteria