SVAR Modeling in STATA. Armando Sánchez Vargas Economics Research Institute UNAM . I.- Motivation. 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.
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Armando Sánchez Vargas
Economics Research Institute UNAM
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.
In the presence of unit roots the structuralisation of a VAR model can take place at three distinct stages:
* 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).
Then, we start with an exactly-identified structure given by the lower triangular decomposition of the variance-covariance matrix of the estimated VAR disturbances and restrict the non-significant parameters to zero moving to a situation of over-identification (i.e).
The VAR, SVAR and VECM commands deal with non stationarity through the prior differencing or the incorporation of deterministic trend or cointegration.
Stata needs more flexibility for dealing with non stationary series.
In general, Stata is powerful, versatile and well designed program which maybe improved by adding some features and refinements.
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