vec_intro.hlp

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{smcl}
{* 07mar2005}{...}
{cmd:help vec intro}
{hline}

{title:Title}

{p2colset 5 23 25 2}{...}
{p2col :{hi:[TS] vec intro} {hline 2}}Introduction to vector error-correction models{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
Stata has a suite of commands for fitting, forecasting, interpreting, and
performing inference on vector error-correction models with cointegrating
variables (VECMs).  After fitting a VECM, the {cmd:irf} commands can be used
to obtain impulse-response functions (IRFs) and forecast-error variance
decompositions (FEVDs).  The table below describes the available commands.


    {title:Fitting a VECM}

{p2colset 5 22 27 2}{...}
{p2col:{helpb vec}}Fit vector error-correction models{p_end}

    {title:Model diagnostics and inference}

{p2col:{helpb vecrank}}Estimate the cointegrating rank using Johansen's framework{p_end}
{p2col:{helpb veclmar}}Obtain LM statistics for residual autocorrelation after {cmd:vec}{p_end}
{p2col:{helpb vecnorm}}Test for normally distributed disturbances after {cmd:vec}{p_end}
{p2col:{helpb vecstable}}Check the stability condition of VECM estimates{p_end}
{p2col:{helpb varsoc}}Obtain lag-order selection statistics for VARs and VECMs{p_end}

    {title:Forecasting from a VECM}

{p2col:{helpb fcast compute}}Compute dynamic forecasts of dependent variables after {cmd:var}, {cmd:svar}, {cmd:vec}{p_end}
{p2col:{helpb fcast graph}}Graph forecasts of dependent variables computed by {cmd:fcast compute}{p_end}

    {title:Working with IRFs and FEVDs}

{p2col:{helpb irf}}Create and analyze IRFs and FEVDs{p_end}
{p2colreset}{...}


{title:Also see}

{psee}
Manual:  {bf:[TS] vec intro}

{psee}
Online:  
{helpb fcast compute},
{helpb fcast graph},
{helpb vec}, 
{helpb veclmar},
{helpb vecnorm},
{helpb vecrank},
{helpb vecstable}
{p_end}