CAREER: Efficient and Robust Semiparametric Estimation in Time Series Models

小行星9701959 经济时间序列模型通常可以简化为一系列不相关的创新，其特征可能是复杂形式的依赖性，异质性和非高斯性（即，非正态性），难以建模。 尽管人们早就意识到了这一事实，但实证研究者目前使用的计量经济学方法很少超出最大似然法的范围，这种方法假设这些创新是高斯的，比如普通最小二乘法。 该项目的目标是促进这一范围的扩展，通过自适应最大似然技术和半参数有效的估计适用于许多建模的金融和宏观经济学的利益，并通过开发一个软件包实现这些技术的发展。 该职业奖的教育部分为研究生提供自适应估计技术培训，作为非参数计量经济学技术，有效估计理论，应用时间序列计量经济学或应用金融计量经济学的高级研究生课程大纲的一部分。 研究生还将接受使用在该项目下开发的软件的培训。 .

9701959 Hodgson Economic time series models can often be reduced to a sequence of uncorrelated innovations that may be characterized by complicated forms of dependence, heterogeneity, and non-Gaussianity (i.e., non-normality) that are difficult to model. Despite long-standing awareness of this fact, the range of econometric methodologies currently used by empirical researchers rarely extends much beyond maximum likelihood techniques that assume these innovations to be Gaussian, such as ordinary least squares. The objective of the project is to facilitate the extensions of this range through the development of adaptive maximum likelihood techniques and semiparametric efficient estimators applicable in many modeling contexts of interest to financial and macroeconomics, and through the development of a software package implementing these techniques. The educational component of this CAREER award provides graduate students training in adaptive estimation techniques as part of the syllabi of advanced graduate level courses on either nonparametric econometric techniques, theory of efficient estimation, applied time series econometrics, or applied financial econometrics. Graduate students will also be trained in the use of the software developed under this project. .