Homicide rates are forecasted for 1120 inland municipalities in Colombia. Using data from 2003 to 2018, five different forecasting methods are used: ETS, ARIMA, STAR, a classical beta convergence based model and a spatial beta convergence model. Time series cross validation for 4-year ahead forecasts is implemented to assess the accuracy of all models. It is found that the STAR and the beta models have the lowest root mean squared errors. Given the highest forecasting accuracy for the spatial beta model, municipal homicide rates are forecasted for the year 2022 and compared with the National SDG target in the Colombian National Development plan. Lastly, exploratory spatial analysis shows that regions that are likely to accomplish the target are geographically clustered and the same for regions that are forecasted to lag behind. Therefore, as time goes by, space appears to play a more important role in the evolution of homicide rates. The paper concludes with some policy implications in terms of spatial effects and the mitigation of crime.
