Autoregressive models are widely used for the analysis of time-series data, but they remain underutilized when estimating effects of interventions. This is in part due to endogeneity of the lagged outcome with any intervention of interest, which creates difficulty interpreting model coefficients. These problems are only exacerbated in nonlinear or nonadditive models that are common when studying crime, mortality, or disease. In this paper, we explore the use of negative binomial autoregressive models when estimating the effects of interventions on count data. We derive a simple approximation that facilitates direct interpretation of model parameters under any order autoregressive model. We illustrate the approach using an empirical simulation study using 36 years of state-level firearm mortality data from the United States and use the approach to estimate the effect of child access prevention laws on firearm mortality.
翻译:在分析时间序列数据时,广泛使用自动递减模型,但在估计干预效果时,这些模型仍然没有得到充分利用,部分原因是滞后结果与任何感兴趣的干预的内在性,这在解释模型系数方面造成了困难。这些问题仅在研究犯罪、死亡或疾病时常见的非线性或非补充模型中更加严重。在本文件中,我们探讨在估计干预对统计数据的影响时使用负二进制自动递减模型。我们得出一个简单的近似法,便于根据任何顺序自动递减模型直接解释模型参数。我们用美国36年的州一级火器死亡率数据进行经验模拟研究,并使用这一方法估计防止儿童接触法律对枪支死亡率的影响。