Space-Time Autoregressive Models and Forecasting National, Regional and State Crime Rates

29 Pages Posted: 2 May 2014

See all articles by Gary Shoesmith

Gary Shoesmith

Wake Forest University - School of Business

Date Written: 2012

Abstract

The recently advanced space-time-autoregressive (ST-AR) model is used to forecast U.S., regional and state violent and property crime rates. The disaggregate state (Florida) violent crime model includes murder, rape, robbery, and assault and the property crime model, burglary, larceny, and motor vehicle theft. In experimental forecasts, ST-AR RMSEs are compared to those for aggregate univariate AR(p) models, vector autoregression (VAR), Bayesian VAR (BVAR), and two naïve models that predict future crime rates as either the most recent rate or according to the most recent change in rates. The ST-AR model is of particular interest, given its efficient use of data, much like panel-data estimation. The ST-AR, BVAR, and AR(p) models outperform the other three approaches, but the ST-AR models are generally superior.

Keywords: Crime forecasting, Autoregressive models, Disaggregation, Regional forecasting, Time-series

JEL Classification: K42, C22, R15

Suggested Citation

Shoesmith, Gary, Space-Time Autoregressive Models and Forecasting National, Regional and State Crime Rates (2012). International Journal of Forecasting, Vol. 29, No. 1, January-March 2013. Available at SSRN: https://ssrn.com/abstract=2431483

Gary Shoesmith (Contact Author)

Wake Forest University - School of Business ( email )

P.O. Box 7659
Winston-Salem, NC 27109-7285
United States
336-758-5053 (Phone)
336-758-4514 (Fax)

HOME PAGE: http://business.wfu.edu/directory/gary-l-shoesmith/

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