# clustered standard errors vs random effects

It’s not a bad idea to use a method that you’re comfortable with. So the standard errors for fixed effects have already taken into account the random effects in this model, and therefore accounted for the clusters in the data. This page shows how to run regressions with fixed effect or clustered standard errors, or Fama-Macbeth regressions in SAS. If you have data from a complex survey design with cluster sampling then you could use the CLUSTER statement in PROC SURVEYREG. The first assumption is that the error is uncorrelated with all observations of the variable \(X\) for the entity \(i\) over time. If so, though, then I think I'd prefer to see non-cluster robust SEs available with the RE estimator through an option rather than version control. schools) to adjust for general group-level differences (essentially demeaning by group) and that cluster standard errors to account for the nesting of participants in the groups. Similar as for heteroskedasticity, autocorrelation invalidates the usual standard error formulas as well as heteroskedasticity-robust standard errors since these are derived under the assumption that there is no autocorrelation. \[ Y_{it} = \beta_1 X_{it} + \alpha_i + u_{it} \ \ , \ \ i=1,\dots,n, \ t=1,\dots,T, \], \(E(u_{it}|X_{i1}, X_{i2},\dots, X_{iT})\), \((X_{i1}, X_{i2}, \dots, X_{i3}, u_{i1}, \dots, u_{iT})\), # obtain a summary based on heteroskedasticity-robust standard errors, # (no adjustment for heteroskedasticity only), #> Estimate Std. Consult Chapter 10.5 of the book for a detailed explanation for why autocorrelation is plausible in panel applications. I think that economists see multilevel models as general random effects models, which they typically find less compelling than fixed effects models. If your dependent variable is affected by unobservable variables that systematically vary across groups in your panel, then the coefficient on any variable that is correlated with this variation will be biased. I'm trying to run a regression in R's plm package with fixed effects and model = 'within', while having clustered standard errors. This section focuses on the entity fixed effects model and presents model assumptions that need to hold in order for OLS to produce unbiased estimates that are normally distributed in large samples. I will deal with linear models for continuous data in Section 2 and logit models for binary data in section 3. The outcomes differ rather strongly: imposing no autocorrelation we obtain a standard error of \(0.25\) which implies significance of \(\hat\beta_1\), the coefficient on \(BeerTax\) at the level of \(5\%\). Simple Illustration: Yij αj β1Xij1 βpXijp eij where eij are assumed to be independent across level 1 units, with mean zero 1. These situations are the most obvious use-cases for clustered SEs. It is perfectly acceptable to use fixed effects and clustered errors at the same time or independently from each other. They allow for heteroskedasticity and autocorrelated errors within an entity but not correlation across entities. The second assumption ensures that variables are i.i.d. Beyond that, it can be extremely helpful to fit complete-pooling and no-pooling models as … codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' You run -xtreg, re- to get a good account of within-panel correlations that you know how to model (via a random effect), and you top it with -cluster(PSU)- to account for the within-cluster correlations that you don't know how or don't want to model. From: Buzz Burhans

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