#Stata dedlete xlist install
#Stata dedlete xlist download

RE will give you better p-values as they are a more efficient estimator, so you should run random effects if it is statistically justifiable to do so.Statistically, FE are always a reasonable thing to do with panel data because they always give consistent results, but they may not be the most efficient model to run.Which one: Fixed Effects or Random Effects?: The generally accepted way of choosing between FE and RE is running a Hausman test.Stata's RE estimator is a weighted average of fixed and between effects. Random Effects (RE) is used if you believe that some omitted variables may be constant over time but vary between cases, and others may be fixed between cases but vary over time, then you can include both types by using RE.It works best when you have relatively fewer cases (N) and more time periods (T), as each dummy variable removes one degree of freedom from your model.This is equivalent to generating dummy variables for each of your cases and including them in a standard linear regression to control for these fixed 'case effects'. It lets you use the changes in the variables over time to estimate the effects of the independent variables on your dependent variable.Fixed effects (FE) is used to control for omitted variables that differ between cases but are constant over time.Dynamic Models: Mean Group, Pooled Mean Group, Dynamic Fixed Effects.

Static Models are Fixed Effects, Random Effects.

* Running Hausman test to choose between FE and RE: Static Models: Fixed Effects and Random Effects *1.1.

#Stata dedlete xlist install

* If says 'Not Found', then you need to install it. * If you are not sure, then go to Help -> Stata Command -> type estout. * If you haven't installed the estout package yet, run: ssc install estout, replace Twoway scatter $ylist Year, msymbol(circle_hollow) || connected y_mean_t Year, msymbol(diamond) To check for heterogeneity across years visually, run: Twoway scatter $ylist idc, msymbol(circle_hollow) || connected y_mean_x idc, msymbol(diamond) To check for heterogeneity across countries visually, run: * Tip: In case if you misdefined a global macro, delete it by running: macro drop xlist Defining dependent and independent variables: Get correlation matrix with 5% significance level:

Specify cross-sectional (x) and time-series (t) variables:

Label variable GDP 'Gross Domestic Product' Reshape wide y, i(idc Year) j(Variable) string

#Stata dedlete xlist download

* Download and open the dataset using Stata: LINK * To start recording the commands and outputs: