Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes
针对匹兹堡人口普查区月度严重犯罪数据，构建含潜在时空异质状态的计数面板模型，用高效重要性抽样估计似然，验证破窗假说并计算弹性、预测等统计量。
Summary We develop a panel count model with a latent spatio‐temporal heterogeneous state process for monthly severe crimes at the census‐tract level in Pittsburgh, Pennsylvania. Our dataset combines Uniform Crime Reporting data with socio‐economic data. The likelihood is estimated by efficient importance sampling techniques for high‐dimensional spatial models. Estimation results confirm the broken‐windows hypothesis whereby less severe crimes are leading indicators for severe crimes. In addition to ML parameter estimates, we compute several other statistics of interest for law enforcement such as spatio‐temporal elasticities of severe crimes with respect to less severe crimes, out‐of‐sample forecasts, predictive distributions and validation test statistics. Copyright © 2016 John Wiley & Sons, Ltd.