Crime and arrests: An autoregressive integrated moving average (ARIMA) approach

Various theoretical perspectives suggest that marginal changes in the quantity of crime and arrests are related to one another. Unfortunately, they provide little guidance as to the amount of time that is required for these effects to be realized. In this paper, autoregressive integrated moving average (ARIMA) time-series modeling techniques, which necessitate making minima! assumptions concerning the lag structure one expects to find, are utilized to examine the crime-arrest relationship. The bivariate ARIMA analyses of monthly crime and arrest data for Oklahoma City and Tulsa, Oklahoma, for robbery, burglary, larceny, and auto theft reveal little evidence of a lagged crime-arrest relationship.