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

点击翻译按钮获取中文摘要

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. ??