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首页> 外文学位 >Comparison of Different Approaches to Estimating Budgets for Kuhn-Tucker Demand Systems: Applications for Individuals' Time-Use Analysis and Households' Vehicle Ownership and Utilization Analysis.
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Comparison of Different Approaches to Estimating Budgets for Kuhn-Tucker Demand Systems: Applications for Individuals' Time-Use Analysis and Households' Vehicle Ownership and Utilization Analysis.

机译：Kuhn-Tucker需求系统预算估算的不同方法的比较：个人时间使用分析和家庭车辆拥有与使用分析的应用。

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摘要

This thesis compares different approaches to estimating budgets for Kuhn-Tucker (KT) demand systems, more specifically for the multiple discrete-continuous extreme value (MDCEV) model. The approaches tested include: (1) The log-linear regression approach (2) The stochastic frontier regression approach, and (3) arbitrarily assumed budgets that are not necessarily modeled as a function of decision maker characteristics and choice-environment characteristics.;The log-linear regression approach has been used in the literature to model the observed total expenditure as way of estimating budgets for the MDCEV models. This approach allows the total expenditure to depend on the characteristics of the choice-maker and the choice environment. However, this approach does not offer an easy way to allow the total expenditure to change due to changes in choice alternative-specific attributes, but only allows a reallocation of the observed total expenditure among the different choice alternatives. To address this issue, we propose the stochastic frontier regression approach. The approach is useful when the underlying budgets driving a choice situation are unobserved, but only the expenditures on the choice alternatives of interest are observed. The approach is based on the notion that consumers operate under latent budgets that can be conceived (and modeled using stochastic frontier regression) as the maximum possible expenditure they are willing to incur.;To compare the efficacy of the above-mentioned approaches, we performed two empirical assessments: (1) The analysis of out-of-home activity participation and time-use (with a budget on the total time available for out-of-home activities) for a sample of non-working adults in Florida, and (2) The analysis of household vehicle type/vintage holdings and usage (with a budget on the total annual mileage) for a sample of households in Florida. A comparison of the MDCEV model predictions (based on budgets from the above mentioned approaches) demonstrates that the log-linear regression approach and the stochastic frontier approach performed better than arbitrarily assumed budgets approaches. This is because both approaches consider heterogeneity in budgets due to socio-demographics and other explanatory factors rather than arbitrarily imposing uniform budgets on all consumers. Between the log-linear regression and the stochastic frontier regression approaches, the log-linear regression approach resulted in better predictions (vis-a-vis the observed distributions of the discrete-continuous choices) from the MDCEV model. However, policy simulations suggest that the stochastic frontier approach allows the total expenditures to either increase or decrease as a result of changes in alternative-specific attributes. While the log-linear regression approach allows the total expenditures to change as a result of changes in relevant socio-demographic and choice-environment characteristics, it does not allow the total expenditures to change as a result of changes in alternative-specific attributes.
机译：本文比较了用于估算Kuhn-Tucker（KT）需求系统的预算的不同方法，更具体地说，是针对多重离散连续极值（MDCEV）模型的预算。测试的方法包括：（1）对数线性回归方法（2）随机前沿回归方法，以及（3）任意假定的预算，这些预算不一定根据决策者特征和选择环境特征来建模。对数线性回归方法已在文献中用于对观察到的总支出进行建模，作为估计MDCEV模型预算的方式。这种方法使总支出取决于选择者的特征和选择环境。但是，这种方法不能提供一种简单的方法来允许总支出因选择备选方案特定属性的变化而发生变化，而只能允许在不同的选择方案之间重新分配观察到的总支出。为了解决这个问题，我们提出了随机前沿回归方法。当未观察到驱动选择情况的基本预算时，该方法很有用，但仅观察到感兴趣的选择方案上的支出。该方法基于以下观念：消费者在潜在预算下运营，可以将其设想为（并使用随机前沿回归建模）他们愿意承担的最大可能支出。为了比较上述方法的有效性，我们执行了两项经验评估：（1）对佛罗里达州一个非工作成年人的户外活动参与和时间使用情况进行分析（并预算可用于户外活动的总时间），以及（2）对佛罗里达州一个样本家庭的家用车辆类型/年份持有量和使用情况进行分析（并估算年度总行驶里程）。 MDCEV模型预测的比较（基于上述方法的预算）表明，对数线性回归方法和随机前沿方法的效果优于任意假定的预算方法。这是因为这两种方法都考虑到了由于社会人口统计学和其他解释性因素造成的预算异质性，而不是任意地将统一预算强加给所有消费者。在对数线性回归和随机边界回归方法之间，对数线性回归方法从MDCEV模型中得出了更好的预测（相对于离散连续选择的观察分布）。但是，政策模拟表明，随机前沿方法允许总支出因替代特定属性的变化而增加或减少。虽然对数线性回归方法允许总支出因相关的社会人口统计学和选择环境特征的变化而变化，但它不允许总支出因替代项特定属性的变化而变化。

著录项

  • 作者

    Augustin, Bertho.;

  • 作者单位

    University of South Florida.;

  • 授予单位 University of South Florida.;
  • 学科 Engineering Civil.;Engineering General.
  • 学位 M.S.E.S.
  • 年度 2014
  • 页码 105 p.
  • 总页数 105
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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