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variable is stationary.  The null hypothesis is that it has a “unit root.”  Rejecting the null hypothesis of a unit root means the variable is stationary.  With error correction models a useful test is to see is the residuals are stationary.  If so, you don’t need to worry about some important problems.  After estimating the model type: predict yhat  (to obtain the residuals). Then type: xtfisher yhat if yhat==1 This will yield results for no lags.  To specify lag length change the command to read: xtfisher yhat if yhat==1, lag(2)  (for two time periods)   If you reject this with confidence then the residuals are stationary.  The results should say:

Ho: unit root

                             chi2(0)  = 0.000 (i.e., less than .001 of a unit root)

                             Prob > chi2 =       

Models/Commands that “run” but not sure why they should be used:

xtreg top1 demcont repcont top1lag, i(id) fe

reg top1 demcont repcont top1lag i.year i.stnum

xtreg top1 demcont repcont top1lag i.year, fe

xtreg top1 demcont repcont top1lag i.year i.stnum, fe

xtgls top1 demcont repcont top1lag, i (id)


Long panels are where time is much greater than the number of units (e.g., states). Short panels are the opposite. Use fixed-effects (FE) whenever you are only interested in analyzing the impact of variables that vary over time. FE explore the relationship between predictor and outcome variables within an entity (country, person, company, etc.). Each entity has its own individual characteristics that may or may not influence the predictor variables (for

example being a male or female could influence the opinion toward certain

issue or the political system of a particular country could have some effect on

trade or GDP or the business practices of a company may influence its stock

price). When using FE we assume that something within the individual may impact or bias the predictor or outcome variables and we need to control for this. This is the rationale behind the assumption of the correlation between entity’s error term and predictor variables. FE remove the effect of those time-invariant

characteristics from the predictor variables so we can assess the predictors’ net

effect.  Another important assumption of the FE model is that those time-invariant

characteristics are unique to the individual and should not be correlated with

other individual characteristics. Each entity is different therefore the entity’s

error term and the constant (which captures individual characteristics) should

not be correlated with the others. If the error terms are correlated then FE is no

suitable since inferences may not be correct and you need to model that

relationship (probably using random-effects), this is the main rationale for the

Hausman test (presented later on in this document). Control for time effects (in our example year dummy variables) whenever unexpected variation or special events my affect the dependent variable.  The fixed-effects model controls for all time-invariant differences between the individuals, so the estimated coefficients

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