Semiparametric Regression Models for Location, Scale and Shape

位置、尺度和形状的半参数回归模型

• 批准号: 397587368
• 负责人: Professor Dr. Thomas Kneib
• 金额: --
• 依托单位: Lehrstuhl für Statistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/397587368?language=en
• 关键词: Semiparametric; Regression; Models; Location; Scale

Generalised additive models for location, scale and shape (GAMLSS) allow one to relate not only the conditional expectation of a response variable in a regression context to potential explanatory variables but all parameters of the conditional distribution of the response. As a consequence, flexible regression relations beyond the mean can be detected such that more realistic and more informative regression specifications are obtained. In this project, different semiparametric extensions of GAMLSS will be developed and applied in complex case studies. The first part of the project will consider the analysis of time series data with regime switching based on Markov switching and smooth transition models combined with the flexibility of GAMLSS along the specific application of operational losses in the banking industry. The second part will deal with efficiency analysis based on stochastic frontier analysis which will be embedded in the context of GAMLSS to allow for a multivariate formulation with multiple output equations. A third model class considers censored GAMLSS specifications where only incomplete information is available. The most typical example are duration times, e.g. for labour market histories of individuals, but we will also consider censored data on income from observational studies. To increase the general applicability of GAMLSS, we will in addition study difference flexible extensions of the predictor structure (regularization and variable selection approaches, complex interaction types, functional explanatory variables), develop variable importance measures and investigate novel ways of interpreting and visualizing GAMLSS estimates. In summary, this project will provide important contributions to increase the applicability of GAMLSS in challenging economic applications. It will therefore increase the general awareness of such methods such that GAMLSS will turn into a standard tool for applied economists interested in regression effects beyond the mean.

位置、规模和形状的广义加性模型（GAMLSS）不仅可以将回归环境中响应变量的条件期望与潜在的解释变量联系起来，还可以将响应条件分布的所有参数联系起来。因此，可以检测到超出平均值的灵活回归关系，从而获得更现实和更有信息的回归规范。在这个项目中，GAMLSS的不同半参数扩展将被开发并应用于复杂的案例研究。项目的第一部分将考虑基于马尔可夫切换和平滑过渡模型的状态切换的时间序列数据分析，结合GAMLSS的灵活性，以及银行业运营损失的具体应用。第二部分将处理基于随机前沿分析的效率分析，该分析将嵌入在GAMLSS的背景下，以允许具有多个输出方程的多元公式。第三类模型考虑经过审查的GAMLSS规范，其中只有不完整的信息可用。最典型的例子是持续时间，例如个人的劳动力市场历史，但我们也将考虑观察性研究中关于收入的审查数据。为了提高GAMLSS的普遍适用性，我们还将研究预测器结构的不同灵活扩展（正则化和变量选择方法、复杂交互类型、功能解释变量），开发变量重要性度量，并研究解释和可视化GAMLSS估计的新方法。总之，该项目将为提高GAMLSS在具有挑战性的经济应用中的适用性做出重要贡献。因此，它将提高对这些方法的普遍认识，使GAMLSS将成为对均值以外的回归效应感兴趣的应用经济学家的标准工具。