![]() (These numbers are just indicators.)īecause our sex variable only has two categories, turning it into a dummy variable could be as simple as recoding the values of Male and Female from 1=Male and 2=Female to 0=Male and 1=Female. This allows us to enter in the sex values as numerical. For example, in a dummy variable for Female, all cases in which the respondent is female are coded as 1 and all other cases, in which the respondent is Male, are coded as 0. Each dummy variable represents one category of the explanatory variable and is coded 1 if the case falls in that category and zero if not. We can avoid this error in analysis by creating dummy variables.Ī dummy variable is a variable created to assign functional numerical values to levels of categorical variables. This would provide us with results that would not make sense, because for example, the sex Female does not have a value of 2. So, if we were to enter the variable s1gender into a linear regression model, the coded values of the two gender categories would be interpreted as the numerical values of each category. However, linear regression assumes that the numerical amounts in all independent, or explanatory, variables are meaningful data points. ![]() The codes 1 and 2 are assigned to each gender simply to represent which place each category occupies in the variable s1gender. However, before we begin our linear regression, we need to recode the values of Male and Female. ![]() (If you check the Values cell in the s1gender row in Variable View, you can see that the categories in this sex variable are labelled as 1= Male and 2= Female). (See Cochrane's Asset Pricing book for details.In order to answer the question posed above, we want to run a linear regression of s1gcseptsnew against s1gender, which is a binary categorical variable with two possible values. 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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