Fluctuation of Similarity to Detect Transitions Between Dynamical Regimes: Application to Crime Time Series
First published: 2013
Brief summary
Applies a nonlinear time-series method to 1975-1993 monthly US robbery and homicide data alongside unemployment figures, searching for dynamical regime shifts and relationships between crime and economic cycles.
Article
Fluctuation of Similarity to Detect Transitions Between Dynamical Regimes: Application to Crime Time Series is a preprint published by arXiv in 2013. It applies a nonlinear time-series method to 1975-1993 monthly US robbery and homicide data alongside unemployment figures, searching for dynamical regime shifts and relationships between crime and economic cycles.
Monthly-resolution analysis of robbery and homicide 1975-1993. The data source is ICPSR Uniform Crime Reports and US Bureau of Labor Statistics unemployment data. The study includes time series graphs. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.
The authors analyse time series of robberies and homicides in the United States from 1975 to 1993 with monthly resolution, attempting to understand the relationship between unemployment and crime rates over this period. The interpretation is strongest when it survives detrending, autocorrelation controls, structural-break tests and comparison with stochastic or random-walk alternatives.
For cycles researchers, the article brings together crime time series, dynamical regime shifts, homicide and robbery, unemployment. It is relevant to social-cycle research because apparent waves in historical or behavioural data must be separated from seasonality, autocorrelation, structural change and random walks.
Because it is a preprint, the work should be read alongside later peer-reviewed publications and independent replications. It remains useful because the proposed cycle, dataset and analytical approach are stated clearly enough to be scrutinised.
Source details and credits
- Source / publisher: arXiv
- Source type: Preprint
- URL type: PDF
- Credits: arXiv
- URL: https://arxiv.org/pdf/1310.7506