Stata has since changed its default setting to always compute clustered error in panel FE with the robust option. The intent is to show how the various cluster approaches relate to one another. 2). Rho is the intraclass correlation coefficient, which tells you the percent of variance in the dependent variable that is at the higher level of the data hieracrchy (here the individual). College Station, TX: Stata press.' Robust and cluster–robust standard errors ; Panel-corrected standard errors (PCSE) for linear cross-sectional models. The standard regress command in Stata only allows one-way clustering. xtreg health retired , re // + time-constant explanatory variable . xtreg health retired female , re // + cluster robust inference & period effect . In selecting a method to be used in analyzing clustered data the user must think carefully about the nature of their data and the assumptions underlying each of the … It is not meant as a way to select a particular model or cluster approach for your data. Create a group identifier for the interaction of your two levels of clustering; Run regress and cluster by the newly created group identifier However, the bloggers make the issue a bit more complicated than it really is. Setting panel data: xtset The Stata command to run fixed/random effecst is xtreg. 04 Jan 2018, 10:35. Models for Clustered and Panel Data We will illustrate the analysis of clustered or panel data using three examples, two dealing with linear models and with with logits models. xtset id wave // RE . This page was created to show various ways that Stata can analyze clustered data. Thus cluster-robust statistics that account for … Panel Data Panel data is obtained by observing the same person, ﬁrm, county, etc over several periods. Yes, this topic can be confusing. Unlike the pooled cross sections, the observations for the same cross section unit (panel, entity, cluster) in general are dependent. You don't say what kind of panel regression you are doing, though since you are concerned about heteroscedasticity and autocorrelation, I'll guess you're running -xtreg-. I would reshape wide so each year's data is its own variable and then cluster. xtreg health retired female i.wave, re cluster(id) If that value is anywhere north of .01, that's a good indication that you should be concerned about clustering. If it is -xtreg, fe-, then the non-cluster robust VCE is not available, and if you specify -vce (robust)-, Stata automatically uses -vce (cluster ID)- instead (assuming ID is the panel … Try something like this in Stata: reshape wide var@1 var@2 var@3 var@4 var@5 var@6, i (country) j (year); cluster … The linear model examples use clustered school data on IQ and language ability, and longitudinal state-level data on Aid to Families with Dependent Children (AFDC). Stata provides an estimate of rho in the xtreg output. This will group countries that follow similar timepaths for your 6 variables. There have been several posts about computing cluster-robust standard errors in R equivalently to how Stata does it, for example (here, here and here). // declare panel data structure . In Stata: vce(cluster clustvar).Whereclustvar is a variable that identiﬁes the groups in which onobservables are allowed to correlate. Getting around that restriction, one might be tempted to. Before using xtregyou need to set Stata to handle panel data by using the command xtset. Microeconometrics using stata (Vol. type: xtset country year delta: 1 unit time variable: year, 1990 to 1999 panel variable: country (strongly balanced). xtset country year Hello Stata-listers: I am a bit puzzled by some regression results I obtained using -xtreg, re- and -regress, cluster()- on the same sample. Created to show various ways that Stata can analyze clustered data data by the. Health retired female, re // + time-constant explanatory variable your 6 variables for 6... 'S a good indication that you should be concerned about clustering a good indication you... Thus cluster-robust statistics that account for … Microeconometrics using Stata ( Vol inference & period effect that identiﬁes the in. Your 6 variables robust inference & period effect show how the various cluster approaches relate to one another own! 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