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7) Adjusted test for first-order serial correlation, which works even

under random effects

Tests 1, 2, 6, and 7 have asymptotic chi-squared distribution with one

degree of freedom under the null hypothesis. Test 5 has asymptotic

chi-squared distribution with two degrees of freedom under the null

hypothesis, and tests 3 and 4 have standard normal distribution under the

null.

2. If the results of the Hausman test indicate you should use a fixed effects model

it is important to see if time fixed effects are needed when running a fixed effects model.  In order to do this, you first need to create a series of time dummy variables and then execute the test.  Assuming you have a variable named “year” and the data are stacked by state (e.g., observation #1 is Alabama in year #1, observation #2 is Alabama in year #2, etc.) the following command will generate year dummy variables:

xi  i.year  (this created _Iyear_1913- _Iyear_2003)

xtreg top1 demcont repcont top1lag  _Iyear_1913- _Iyear_2003,  fe

testparm _Iyear_1913- _Iyear_2003

If Prob > F =  <.05 then you reject the null hypothesis that all year coefficients are jointly equal to zero.  If so, the time fixed-effects are needed.

3.Autocorrelation:

xtreg top1 demcont repcont top1lag, fe

xtserial top1 demcont repcont top1lag

If the number to the right of “Prob > F is .05 or lower reject the null hypothesis of no first-order autocorrelation in favor of the alternative hypothesis the residuals show first-order autocorrelation.  If you have first-order autocorrelation replace xtreg with xtregar.  Since xtserial does not permit lagged variables you would need to create a lagged variable (not in way Stata would read such as l.demcont but rather some other name).  Also, “abar” (another test for autocorrelation - see above) will allow obviously lagged variables.

4. If the results of the Hausman test indicate you should use a fixed effects model

the following test is useful. According to Baltagi, cross-sectional dependence is a problem in macro panels with long time series (over 20-30 years). This is not much of a problem in micro panels (few years and large number of cases). The null hypothesis in the B-P/LM test of independence is that residuals across entities are not correlated. The command to run this test is xttest2 (run it after xtreg, fe): According to Baltagi, cross-sectional dependence is a problem in macro panels with

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