Alcohol and liver cirrhosis mortality in the United States: comparison of methods for the analyses of time-series panel data models.
Alcohol and liver cirrhosis mortality in the United States: comparison of methods for the analyses of time-series panel data models.
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美国的酒精和肝硬化死亡率：时间序列面板数据模型分析方法的比较。
DOI：
10.1111/j.1530-0277.2010.01327.x
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Kerr,WilliamC
中科院分区：
文献类型：
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作者：
Ye,Yu;Kerr,WilliamC
背景：探索在总体水平横截面时间序列数据中估计肝硬化死亡率与人均酒精消费量之间关系的各种模型规范。方法：使用美国 47 个州 1950 年至 2002 年的一系列肝硬化死亡率，通过合并自回归积分移动平均 (ARIMA) 模型和 4 种面板数据模型（广义估计方程、广义估计方程）估计酒精消耗的影响。最小二乘模型、固定效应模型和多层次模型。还检查了每种模型类型下误差项结构的各种规格。还评估了控制时间趋势以及使用同时或累积消耗作为预测因子的不同方法。结果：当通过总酒精预测肝硬化死亡率时，ARIMA 和面板数据分析之间的估计值高度一致，平均总体效果为 0.07 至 0.09。使用烈酒、啤酒和葡萄酒消费量作为预测变量得出的估计值不太一致。结论：当将多个地理时间序列组合为面板数据时，现有模型无法容纳所有异质性来源，因此任何类型的面板模型都必须采用某种形式的概括。因此，应该估计不同类型的面板数据模型，以检验研究结果的稳健性。当使用饮料特定体积作为预测因子时，我们还建议谨慎解释。
Background:To explore various model specifications in estimating relationships between liver cirrhosis mortality rates and per capita alcohol consumption in aggregate‐level cross‐section time‐series data.Methods:Using a series of liver cirrhosis mortality rates from 1950 to 2002 for 47 U.S. states, the effects of alcohol consumption were estimated from pooled autoregressive integrated moving average (ARIMA) models and 4 types of panel data models: generalized estimating equation, generalized least square, fixed effect, and multilevel models. Various specifications of error term structure under each type of model were also examined. Different approaches controlling for time trends and for using concurrent or accumulated consumption as predictors were also evaluated.Results:When cirrhosis mortality was predicted by total alcohol, highly consistent estimates were found between ARIMA and panel data analyses, with an average overall effect of 0.07 to 0.09. Less consistent estimates were derived using spirits, beer, and wine consumption as predictors.Conclusions:When multiple geographic time series are combined as panel data, none of existent models could accommodate all sources of heterogeneity such that any type of panel model must employ some form of generalization. Different types of panel data models should thus be estimated to examine the robustness of findings. We also suggest cautious interpretation when beverage‐specific volumes are used as predictors.