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 lutkepohl2tssetvar dln_inv dln_inc dln_consump

webuse lutkepohl2

tsset

var dln_inv dln_inc dln_consump

Tells the computer to open a data set (lutkepohl2) it has stored in its memory

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)

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

Data definitions

dln_consump float %9.0g first-difference of ln_consumpln_consump float %9.0g log consumptiondln_inc float %9.0g first-difference of ln_incln_inc float %9.0g log incomedln_inv float %9.0g first-difference of ln_invln_inv float %9.0g log investment

The output from:var dln_inv dln_inc dln_consump

•• Coef. Std. Err. z P>z [95% Conf. Interval]• dln_inv• dln_inv• L1. -.2725654 .1093372 -2.49 0.013 -.4868623 -.0582684• L2. -.1340503 .1089367 -1.23 0.218 -.3475624 .0794617•• dln_inc• 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

This tells us the dependent variable this part of output relates toThis tells us first right hand side variable

Lagged once (L1) and twice (L2)

dln_inc and dln_consump are the other two right hand side variablesBoth are lagged once and twice. The output shows coefficients and t statistics

And the output for second equation, relating to dln_inc

_cons .0125949 .0032092 3.92 0.000 .0063049 .0188848 L2. .0490208 .1377087 0.36 0.722 -.2208833 .318925 L1. .3050571 .1381245 2.21 0.027 .034338 .5757762 dln_consump L2. .0209769 .1181555 0.18 0.859 -.2106036 .2525573 L1. -.1232543 .121761 -1.01 0.311 -.3619015 .1153928 dln_inc L2. .0616319 .0276039 2.23 0.026 .0075293 .1157345 L1. .0433473 .0277054 1.56 0.118 -.0109542 .0976488 dln_inv dln_inc

There is a third equation and also summary data

dln_consump 7 .009938 0.2400 28.09971 0.0001dln_inc 7 .011224 0.1514 15.87886 0.0144dln_inv 7 .044295 0.1051 10.45617 0.1067 Equation Parms RMSE R-sq chi2 P>chi2

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

Vector autoregression

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