![]() ![]() For character variables, a dollar sign '$' must be added after the name of the variable (like for var2 above). SAS differentiates between variables whose values are numeric and variables whose values are character. Here there are 3 variables with the names: var1, var2, var3. Line 2: The input statement indicates which variables are included in the data set. No special characters are allowed in the name except for '_'. The data set name may be up to 32 alphanumeric characters and must begin with a letter. Line 1: In the first line we designate a name for the new data set. The general format of a data step is as follows: Use SAS mathematical expressions and SAS functions.Use the "set" statement to select a subset of data.Distinguish between temporary and permanent SAS data sets.Execute and interpret the "proc means" procedure.Learning ObjectivesĪfter completing this modules, the student will be able to: This module will introduce some basic, but very important and frequently used commands and operations in SAS. (See Cochrane's Asset Pricing book for details.Module 2: SAS Programming Basics - Part I Gives the same variance as the GMM procedure. This works because the Newey-West adjustment Time-series estimates on a constant, which is equivalent to taking a mean. The approach here is to use GMM to regress the Note that the lag length is set by defining a macro variable, lags. Var estimate-df format estimate stderr 7.4 Unlike Stata, this is somewhat complicated in SAS, but can be done as follows:įit estimate / gmm kernel=(bart,%eval(&lags+1),0) vardef=n run Since the results from this approach give a time-series, it is common practice to use the Newey-West adjustmentįor standard errors. Will run cross-sectional regressions by year for all firms and report the means. Running a Fama-Macbeth regression in SAS is quite easy, and doesn't require any special macros. More detail is provided here.Ĭlustering in two dimensions can be done using the method described by Thompson ( 2011) and others. ![]() Note that genmod does not report finite-sample adjusted statistics, so to make the results between these two methods consistent, you need to multiply the genmod results by (N-1)/(N-k)*M/(M-1) where N=number of observations, M=number of clusters, and k=number of regressors. The online SAS documentation for the genmod procedureĪlternatively, you may use surveyreg to do clustering: This method is quite general, and allows alternative regression specifications using different link functions. Repeated subject=identifier / type=ind run This will automatically generate a set of dummy variables for each level of the variable "identifier".Ĭlustered standard errors may be estimated as follows: ![]() Model depvar = indvars identifier / solution run Model depvar = indvars / solution noint run Ībsorption is computationally fast, but the individual fixed effects estimates will not be displayed. (Note that, unlike with Stata, we need to supress the intercept to avoid a dummy variable trap.) SAS finally caught up though.Ī regression with fixed effects using the absorption technique can be done as follows. Use ODS to capture these statistics, which always seemed silly to me. Thanks to Guan Yang at NYU for making me aware of this. The covariance matrix of the standard errors. You can use the option acov instead of hcc if you want to see SAS now reports heteroscedasticity-consistent standard errors and t-statistics with the hcc option: It is meant to help people who have looked at Mitch Petersen's ProgrammingĪdvice page, but want to use SAS instead of Stata.Ī test data set that you can use to compare the output below to see how well they agree. This page shows how to run regressions with fixed effect or clustered standard errors, or Fama-Macbeth regressions ![]() Clustering, Fixed Effects, and Fama-MacBeth in SAS Notes on Clustering, Fixed Effects, and Fama-MacBeth regressions in SAS Noah Stoffman, Kelley School of Business, Indiana UniversityĬode updated June, 2011 Links updated August, 2016 ![]()
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