Stata Tutorial 2 by kg1VS9

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									                                      Stata Tutorial 2

1. Load the data
   cd "C:\Documents and Settings\Owner\Desktop"
   insheet using survey.csv

2. Regression
   reg friendhrs internet socialpos time2school


         friendhrs      Coef.       Std. Err.          t     P>t         [95% Conf. Interval]


         internet    -.3251806      .8691222      -0.37      0.711      -2.105497      1.455136
         socialpos -.1412255        4.701476      -0.03      0.976      -9.771763      9.489312
         time2school     .3710433 .2256273          1.64     0.111      -.0911332      .8332198
         _cons       11.19527       6.42476       1.74       0.092      -1.965257      24.35579


    reg friendhrs gamehrs socialpos time2school


         friendhrs     Coef.         Std. Err.     t         P>t         [95% Conf. Interval]
            gamehrs 1.838271        .7095352      2.59      0.015       .3824248      3.294117
         socialpos -2.493186        4.314445      -0.58     0.568        -11.3457      6.359324
         time2school     .492213 .2095013         2.35      0.026        .0623518      .9220743
         _cons       3.930471       4.580964       0.86      0.398      -5.468892      13.32983


P value: the lower the p value, the less likely the result, assuming the null hypothesis, so the more
significant the result.

3. F test

F test tests the joint significance of the independent variables. When testing the significance of the
goodness of fit, our null hypothesis is that the independent variables jointly equal to zero.


          RSSR  RSSu / m
    F
            RSSu / n  k
    m  number of restrictions
    k  parameters in unrestricted mod el
    RSSu  unrestricted RSS
    RSSR  restricted RSS

If our F-statistic is below the critical value we fail to reject the null and therefore we say the
goodness of fit is not significant.
    test gamehrs socialpos time2school
         (1) gamehrs = 0
         (2) socialpos = 0
         (3) time2school = 0
              F( 3,      27)= 3.59 (check F table,         and find that critical value is 2.96)
                Prob > F     = 0.026
    m=3: number of restrictions is 3
    n-k=27: df is 27

4. Predicting Y
Obtain predictions:
We have known the coefficient estimates and the x (independent variable) values, we want to find
the values for y.

    predict friendhrshat
    predict yhat

(note: the two command produce the same results, use “list” command to check)

Calculate standard errors of the predictions
    predict e, stdp



5. Ramsey RESET / Davidson MacKinnon specification tests

The RESET test is designed to detect omitted variables and incorrect functional form. It proceeds
as follows:
Suppose we have




After doing OLS, we obtain coefficient estimates, and by using the prediction command which we
mentioned above, we obtain yhat.
Consider the artificial model:




A test for misspecification is a test of         against the alternative           .
Rejection of the null (which means       is different from zero) implies the original model is
inadequate and can be improved. Failure to reject the null says the test has not been able to detect
misspecifications.

Ramsey RESET test using powers of the fitted values of friendhrs
   estat ovtest
            Ho: model has no omitted variables
                             F(3, 24) =   2.74
                             Prob > F =    0.0654
The null is not rejected at 5% level.
If rejected, try to correct the model by including new independent variables or change the
functional form.

6. BP or White test for heteroskedasticity
One of the 4 assumptions for classical linear regression is homoskedasticity. i.e. the variance of
error terms are constant across observations. If the assumptions is violated (heteroskedasticity),
OLS estimator will be biased. We can use BP test or White test to check heteroskedasticity.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
    estat hettest
           Ho: Constant variance
           Variables: fitted values of friendhrs

           chi2(1)     =          0.67
           Prob > chi2 =       0.4135

The null hypothesis is not rejected, and the variances are constant.

(note: a large p value or a small chi2 value would indicate the null is not rejected,
homoskedasticity assumption holds; a small p value or a large chi2 value indicates
heteroskedasticity is present)

White test for heteroskedasticity
    imtest, white
         White's test for Ho: homoskedasticity
         against Ha: unrestricted heteroskedasticity

         chi2(8)     =           3.03
         Prob > chi2 =        0.9324

         Cameron & Trivedi's decomposition of IM-test
         Source                    chi2       df            p

         Heteroskedasticity             3.03      8      0.9324
         Skewness                       3.11      3      0.3744
         Kurtosis                       1.14      1      0.2859

         Total                           7.28     12     0.8384
7. Robust standard errors

We can use robust standard errors to correct heteroskedaticity. Under contamination, RSE leads a
smaller bias.

   reg friendhrs gamehrs socialpos time2school, robust


                              Robust
friendhrs       Coef.    Std. Err.      t        P>t               [95% Conf.     Interval]
gamehrs       1.838271   .747191       2.46     0.021          .3051614           3.37138
socialpos     -2.493186 3.74317        -0.67    0.511           -10.17354        5.187164
time2school     .492213 .2217612        2.22    0.035           .0371966         .9472295
_cons       3.930471     4.856262       0.81    0.425          -6.033755          13.8947




    Reference:

Baltagi, B. (2001). Econometric Analysis of Panel Data, second edition, New York, John Wiley &
  Sons.
Online resources:
 www.nd.edu/~rwilliam/stats2/l25.pdf
 http://homepages.nyu.edu/~sc129/econometrics_handouts/hetero_tests_stata.pdf
 http://www.economics.soton.ac.uk/courses/ECON3012/Lecture2-2.pdf

								
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