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Many economic time series are cointegrated and require specialized
statistical methods to analyze them. Economic variables, such as
consumption, investment, and income, tend to grow over time, while the
differences between any two of those variables never deviate too far
from a constant equilibrium value. VECMs are used to model such
relationships.
Stata's VECM suite includes commands for testing for cointegration and determining the number of cointegrating relationships, choosing the lag order, and fitting the model. Additional commands facilitate postestimation diagnostic analyses, including testing for stability, autocorrelated residuals, and normality. 

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The new vec command fits cointegrated
vector errorcorrection models, also known as VECMs.


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The new vecrank command
produces statistics used to determine the number of cointegrating equations in a VECM.


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The new fcast command replaces the old command varfcast and
produces dynamic forecasts of the dependent variables after fitting a VAR,
SVAR, or VECM.


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The new irf command replaces the old command varirf and
does everything the old command did and more. irf estimates the
impulse–response functions, cumulative impulse–response functions,
orthogonalized impulse–response functions, structural
impulse–response functions and forecasterror variance decompositions
(FEVDs) after fitting a VAR, SVAR, or VECM. Results can be graphed and
presented in tables.
The old varirf command continues to work but is not documented. If you have old .irf files, they will work with the old varirf command and the new irf command. 

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The varsoc command can be used to obtain lagorder selection statistics
for VECMs, as well as VARs.


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The new veclmar command computes Lagrangemultiplier test statistics
for residual autocorrelation after fitting a VECM.


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The new vecnorm command computes a series of test statistics against
the null hypothesis that the disturbances are normally distributed after
fitting a VECM. For each equation, and for all equations jointly, three
statistics are computed: a skewness statistic, a kurtosis statistic, and the
Jarque–Bera statistic.


* 
The new vecstable command checks the eigenvalue stability condition
after fitting a VECM.


* 
The new vecstable command and the command varstable
now have a graph option that produces publicationquality graphs to
facilitate interpreting and presenting the stability results.


*  The new haver command makes it easy to load and to analyze the economic and financial databases available from Haver analytics. 
*  Existing command clogit has new options robust and cluster. In addition, clogit has been converted from a builtin command to one that now uses ml. As a result, clogit now supports options that are available to mlprogrammed estimators, such as constraint() for linear constraints. 