Previsão de homicídios no Brasil: Proposta de variável antecedente

Arbeitspapier

In this article we investigate the capacity of antecedent variables, among them admissions for aggression, in the prediction of the number of homicides in Brazil. The main objective is to eliminate a gap regarding the large time lag in the dissemination of information about these deaths in the country, allowing updated conjuncture analyzes. For this, through a 'rolling window' scheme and 'Model Confidence Set' approach, we investigate whether multivariate models with leading variables show forecast performance superior to a set of univariate models. In applying the MCS approach, considering different evaluation statistics, loss functions and estimation windows, we find strong evidence of the ability of the leading variables used to provide additional information content in the prediction of the Brazilian criminal dynamics, with models of leading variables systematically surpassing univariate models, especially in extended periods of forecasting. In general, improvements related to the benchmark Random walk model, in terms of RMSE, MAE and MAD, are of the order of 60%.

Object Details

• Language: Portugiesisch

References and Relationships

• Bibliographic citation: Series: Texto para Discussão ; No. 2611

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Illegal Behavior and the Enforcement of Law ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: forecasting ; homicide forecasting ; model confidence set ; time series ; violence

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lins, Gabriel de Oliveira Accioly ; Cerqueira, Daniel ; Coelho, Danilo
• Event: Veröffentlichung
• (who): Instituto de Pesquisa Econômica Aplicada (IPEA)
• (where): Brasília
• (when): 2020

Further information

• DOI: doi:10.38116/td2611
• Handle: http://hdl.handle.net/10419/240805
• Last update: 10.03.2025, 11:44 AM CET