A random heaping model of annual vehicle kilometres travelled considering heterogeneous approximation in reporting
A random heaping model of annual vehicle kilometres travelled considering heterogeneous approximation in reporting
复制标题
考虑报告中异质近似的年度车辆行驶公里数随机堆积模型
DOI：
10.1007/s11116-018-9933-0
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发表时间：
2018
期刊：
影响因子：
4.3
通讯作者：
R. Collet
中科院分区：
文献类型：
--
作者：
Toshiyuki Yamamoto;J. Madre;M. de Lapparent;R. Collet
年度车辆行驶里程(VKT)是汽车使用时间较长的指标。通常，受访者报告的年度VKT用于分析。但报告的值几乎系统地包含近似，如舍入和堆积。我们使用隐类方法来模拟VKT，以解释Heitjan和Rubin(J am Stat Assoc 85(410)：304-314,1990；Ann Stat 19(4)：2244-2253,1991)提出的这个问题。我们的模型采用有序概率模型的混合形式。报告中的粗俗程度被认为是一个潜在变量，它决定了受访者可能属于的类别。针对每一类建立了VKT的有序响应概率模型。阈值是预先确定的，并对与该类别相关的粗糙程度进行建模。年度VKT本身被假设为影响报告中的粗略程度，因此被纳入潜在粗略模型的解释变量。它也是由有序的概率位模型建模的。本研究使用的数据集是法国家庭汽车保有量的面板数据(PARC-Auto面板调查)。结果证实，VKT越长，报告中的粗糙度越大。研究结果还表明，在VKT报告中，通勤汽车的粗糙度大于其他通勤车。VKT函数的系数估计值与传统的VKT回归模型的估计值没有统计学差异。然而，与传统回归模型相比，该模型的VKT函数中系数估计的误差项估计方差和标准误差均较小，这表明该模型比传统回归模型更能有效地研究解释变量对VKT的影响。
Annual vehicle kilometres travelled (VKT) is a long used index of car use. Usually, the annual VKT, as reported by respondents, is used for the analysis. But the reported values almost systematically contain approximations such as rounding and heaping. We apply a latent class approach in modelling VKT to account for this problem developed by Heitjan and Rubin (J Am Stat Assoc 85(410):304–314, 1990; Ann Stat 19(4):2244–2253, 1991). Our model takes the form of a mixture of ordered probit models. The level of coarseness in reporting is considered as a latent variable that determines a category the respondent may belong to. Ordered response probit models of VKT are developed for each category. Thresholds are predetermined and model the level of coarseness that relates to the category. Annual VKT is itself assumed to affect the level of coarseness in reporting, thus included as an explanatory variable of the latent coarseness model. It is also modelled by an ordered probit model. The data set used in this study is a panel data of French households’ vehicle ownership (Parc-Auto panel survey). The results confirm that the longer VKT results in a larger coarseness in the report. The results also suggest that the coarseness in the report of VKT is larger for commuting car than others. The coefficient estimates on the VKT function are not statistically different from those estimated by conventional regression model of VKT. However, the estimated variance of the error term and the standard errors of the coefficient estimates in the VKT function for the proposed model are smaller than those for conventional regression model, implying that the proposed model is more efficient to investigate the effect of the explanatory variables on VKT than the conventional regression model.