国外大学讲义：SVAR Modeling in STATA.ppt

* * * * * * * * * * * * * * * * * SVAR Modeling in STATA Armando Sánchez Vargas Economics Research Institute UNAM Stata is a powerful and flexible statistical package for modeling time series. Prospective and advanced users would want to know: SVAR modeling facilities the package offers. The main advantages of Stata compared with other time series packages. What is still needed and what might be refined to implement the whole SVAR methodology in Stata. I.- Motivation The main purpose of this presentation is to discuss STATA′s capability to implement the entire SVAR methodology with non-stationary series. A second objective is to discuss what is needed to improve the implementation of SVAR models in STATA. II.- Objectives III.- SVAR Methodology The main objective of SVAR models is to find out the dynamic responses of economic variables to disturbances by combining time series analysis and economic theory. III.- SVAR Methodology In the presence of unit roots the structuralisation of a VAR model can take place at three distinct stages: III.- SVAR Methodology The first step consists of specifying an appropriate VAR representation for the set of variables.* * Which implies to choose the lag order, the cointegration rank and the kind of associated deterministic polynomial and a sensible identification of the space spanned by the cointegrating vectors (Johansen, 1995). III.- SVAR Methodology In the second step, the “structuralisation” stage, we use the VAR model in its error correction form to identify the short run associations between the variables and their determinants, which are hidden in the covariance matrix of the residuals of such multivariate model. In order to recover the short run model coefficients we can use the variance covariance matrix of the VAR in its error correction form (*) and impose theoretical restrictions. (*) III.- SVAR Methodology Then, we start with an exactly-iden