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STATA Using the menu by YEhmBCM

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									STATA Using the menu
    After starting Stata: Click on help
           (Top right menu bar)
Then type ‘var’. You get a help page giving you
  info on this instruction.
Half way down this page you see the following
  instructions:

 webuse lutkepohl2
tsset
var dln_inv dln_inc dln_consump
webuse lutkepohl2                 Tells the computer to open a data set
                                  (lutkepohl2) it has stored in its memory


tsset    tsset declares the data in memory to be a time series. tssetting the data is
         what makes Stata's time-series operators such as L. and F. (lag and lead)
         work. (This comes from going into ‘help’ and typing tsset)



var dln_inv dln_inc dln_consump
 This tells STATA to do a var. The three variables involved are dln_inv, dln_inc and
 dln_consump
               Data definitions



ln_inv        float   %9.0g   log investment
dln_inv       float   %9.0g   first-difference of ln_inv
ln_inc        float   %9.0g   log income
dln_inc       float   %9.0g   first-difference of ln_inc
ln_consump    float   %9.0g   log consumption
dln_consump   float   %9.0g   first-difference of ln_consump
               The output from:
       var dln_inv dln_inc dln_consump
•
•         Coef.     Std. Err.   z       P>z       [95% Conf. Interval]
•
  dln_inv                  This tells us the dependent variable this part of output relates to
• dln_inv                    This tells us first right hand side variable
• L1.     -.2725654 .1093372 -2.49        0.013      -.4868623 -.0582684
• L2.     -.1340503 .1089367 -1.23        0.218      -.3475624 .0794617
•
• dln_inc        Lagged once (L1) and twice (L2)
• L1.     .3374819 .4805209 0.70          0.482      -.6043217 1.279286
• L2.     .1827302 .466292    0.39        0.695      -.7311852 1.096646
•
• dln_consump
• L1.     .6520473 .5450985 1.20          0.232      -.4163261 1.720421
• L2.     .5980687 .5434576 1.10          0.271      -.4670886 1.663226
•
• _cons -.0099191 .0126649 -0.78          0.434      -.0347419 .0149037


    dln_inc and dln_consump are the other two right hand side variables
    Both are lagged once and twice. The output shows coefficients and t statistics
And the output for second equation,
         relating to dln_inc

dln_inc
     dln_inv
         L1.    .0433473   .0277054    1.56   0.118   -.0109542   .0976488
         L2.    .0616319   .0276039    2.23   0.026    .0075293   .1157345

     dln_inc
         L1.   -.1232543    .121761   -1.01   0.311   -.3619015   .1153928
         L2.    .0209769   .1181555    0.18   0.859   -.2106036   .2525573

 dln_consump
         L1.    .3050571   .1381245    2.21   0.027     .034338   .5757762
         L2.    .0490208   .1377087    0.36   0.722   -.2208833    .318925

       _cons    .0125949   .0032092    3.92   0.000    .0063049   .0188848
      There is a third equation and also
                summary data
Vector autoregression

Sample: 1960q4 -   1982q4                            No. of obs      =        89
Log likelihood =   742.2131                          AIC             = -16.20704
FPE            =   1.84e-11                          HQIC            = -15.97035
Det(Sigma_ml) =    1.15e-11                          SBIC            = -15.61983

Equation            Parms       RMSE     R-sq      chi2     P>chi2

dln_inv                 7     .044295   0.1051   10.45617   0.1067
dln_inc                 7     .011224   0.1514   15.87886   0.0144
dln_consump             7     .009938   0.2400   28.09971   0.0001




This gives us summary data, e.g. R2s, for each regression

								
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