Semiparametric Regression Models for Location, Scale and Shape

位置、尺度和形状的广义加性模型(GAMLSS)允许人们不仅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，而且还将响应的条件分布的所有参数联系起来。结果，可以检测超出平均值的灵活回归关系，从而获得更真实和更有信息量的回归规范。在这个项目中，将开发不同的GAMLSS半参数扩展，并将其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时间序列数据的切换分析，并结合GAMLSS在银行业操作损失的具体应用。第二部分将讨论基于随机前沿分析的效率分析，它将嵌入GAMLSS的背景下，以允许使用多个输出方程的多变量公式。第三个模型类考虑只有不完全信息可用的删失GAMLSS规范。最典型的例子是持续时间，例如个人的劳动力市场历史，但我们也将考虑来自观察性研究的收入的审查数据。为了提高GAMLSS的普遍适用性，我们还将研究预测者结构的不同灵活扩展(正则化和变量选择方法、复杂的相互作用类型、功能解释变量)，开发变量重要性度量，并探索解释和可视化GAMLSS估计的新方法。总而言之，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，它将提高人们对这些方法的普遍认识，使GAMLSS成为对超出均值的回归效应感兴趣的应用经济学家的标准工具。

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.