I have mixed stationary/non-stationary variables, Panel data with (T=41, N=16, T>N). Need suggestions for appropriate model?

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Muhammad Dawood

For static data

use the following code in Stata:

*** Fixed effects and random effects regressions ***

xtreg dep_var indep_var_01 indep_var_02 ... indep_var_XX, fe

estimates store fix

xtreg dep_var indep_var_01 indep_var_02 ... indep_var_XX, re

estimates store ran

hausman fix ran, sigmamore

*** Wooldridge's autocorrelación test ***

xtserial dep_var indep_var_01 indep_var_02 ... indep_var_XX, output

*** Wald test heteroskedasticity ***

xttest3

If autocorrelation and heteroskedasticity (both) exists, you must use one of these methods:

*** Feasible Generalizad Least Squares (FGLS) ***

xtgls dep_var indep_var_01 indep_var_02 ... indep_var_XX, p(h) c(ar1)

*** Panel-corrected standard errors (PCSE) ***

xtpcse dep_var indep_var_01 indep_var_02 ... indep_var_XX, het c(ar1)

or can use ARDL as dynamic model

Rachid el Bettioui

Salam Mr Dawood.

I think that in this case you should use the cointegration to make all variables stationary. Then, you can choose nither a VAR or MCE Model.

Best regards

Anton Rainer

You should first state your theoretical model independent of (the quality) of your data. If you formulate this model in transformed variables, e.g. differences or percentage changes, you mey find that these data are better suited for an econometric estimation. If the statistical quality of an estimate is quite good, you need not much care about stationarity etc.

Stephen Obinozie Ogwu

For mixed staionarity i advice you make use of ARDL model. For the non-stationary variables; at times some data from some countries suffer from this challenge but you can adopt more than one unit root testing approach, by so doing you can include the breaking point unit root testing to take care of the problem, if it is from structural break problems.

Just saw this now. However, the appropriate model for analyzing this data is the Panel ARDL