Minimum Distance Methods for Models with Cointegration in Time Series and Short Panels

小行星9720675 这个项目的目标是开发最小距离方法的协整时间序列回归和短面板数据模型的推理。 协整模型因其与经济理论的直接关系而受到应用研究人员的特别青睐，即，区分长期和短期动态。 最小距离方法似乎提供了理想的方法来估计和进行推断这些模型。 这是非常有用的，因为目前估计协整模型的方法已被证明很难扩展到对应用计量经济学家有用的方向。 方法往往是特殊的协整问题本身或导致复杂的计算方法。 在大多数情况下，当应用最小距离方法时，封闭形式的解可用于估计。 这些方法是非常简单的，很好地理解，使相当简单的扩展方法估计协整模型的方向，应该证明是有用的应用工作。 为时间序列模型设想的特定扩展是估计，当存在对协整向量的限制（线性或非线性，在方程内或方程间），存在平稳变量和存在异方差时。 在面板模型中，该项目将提供和评估估计和推理规则，在短时间维面板数据集时，许多个人的观察是可用的。 该模型的重点是尽可能多地整合个体之间的异质性。 这些方法也将在与时间序列模型相同的方向上扩展。 ??

9720675 Elliott The objective of this project is to develop minimum distance methods for inference in cointegrated time series regressions and short panel data models. Cointegrated models have found particular favor among applied researchers because of their direct relationship with economic theory, i.e., distinguishing between long-run and short-run dynamics. Minimum distance methods appear to provide ideal methods for estimating and conducting inference on these models. This is very useful as current methods of estimation of cointegrating models have proven quite difficult to extend in directions that are useful to applied econometricians. Methods are often special to the problems of cointegration per se or result in complicated computational methods. In most situations closed form solutions are available for estimation when minimum distance methods are applied. These methods are very simple and well understood enabling fairly simple extension of methods for estimation of cointegrated models in directions that should prove useful in applied work. The particular extensions envisaged for time series models are estimation when there are restrictions on the cointegrating vectors (linear or nonlinear, within or across equation), the presence of stationary variables, and the presence of heteroskedasticity. In the panel models, the project will be to provide and evaluate estimators and rules for inference in short time dimension panel data sets when observations on many individuals are available. The focus of this model is to incorporate as much heterogeneity across individuals as possible. These methods will be extended also in the same direction as the time series models. ??