Short-term forecasting of crime
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References listed on IDEAS
- Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
- Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
- George Duncan & Wilpen Gorr & Janusz Szczypula, 1993. "Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting," Management Science, INFORMS, vol. 39(3), pages 275-293, March.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
- A. Hirschfield & K.J. Bowers, 1997. "The Effect of Social Cohesion on Levels of Recorded Crime in Disadvantaged Areas," Urban Studies, Urban Studies Journal Limited, vol. 34(8), pages 1275-1295, July.
Citations
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Cited by:
- Camacho-Collados, M. & Liberatore, F. & Angulo, J.M., 2015. "A multi-criteria Police Districting Problem for the efficient and effective design of patrol sector," European Journal of Operational Research, Elsevier, vol. 246(2), pages 674-684.
- Cohen, Jacqueline & Garman, Samuel & Gorr, Wilpen, 2009. "Empirical calibration of time series monitoring methods using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(3), pages 484-497, July.
- Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015.
"Golden rule of forecasting: Be conservative,"
Journal of Business Research,
Elsevier, vol. 68(8), pages 1717-1731.
- Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2014. "Golden Rule of Forecasting: Be conservative," MPRA Paper 53579, University Library of Munich, Germany.
- repec:wly:japmet:v:32:y:2017:i:3:p:600-620 is not listed on IDEAS
- Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
- Gorr, Wilpen & Harries, Richard, 2003. "Introduction to crime forecasting," International Journal of Forecasting, Elsevier, vol. 19(4), pages 551-555.
- Chen, Huijing & Boylan, John E., 2008. "Empirical evidence on individual, group and shrinkage seasonal indices," International Journal of Forecasting, Elsevier, vol. 24(3), pages 525-534.
- Corcoran, Jonathan J. & Wilson, Ian D. & Ware, J. Andrew, 2003. "Predicting the geo-temporal variations of crime and disorder," International Journal of Forecasting, Elsevier, vol. 19(4), pages 623-634.
- Jean-François Richard, 2015. "Likelihood Based Inference and Prediction in Spatio-temporal Panel Count Models for Urban Crimes," Working Paper 5657, Department of Economics, University of Pittsburgh.
- Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
- Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
- Roman Liesenfeld & Jean‐François Richard & Jan Vogler, 2017.
"Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 32(3), pages 600-620, April.
- Vogler, Jan & Liesenfeld, Roman & Richard, Jean-Francois, 2015. "Likelihood based inference and prediction in spatio-temporal panel count models for urban crimes," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113131, Verein für Socialpolitik / German Economic Association.
- Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
- Gorr, Wilpen L., 2009. "Forecast accuracy measures for exception reporting using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(1), pages 48-61.
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