![]() ![]() ![]() SAS Viya SDK for JavaScript The SAS Viya SDK for JavaScript is a collection of JavaScript libraries for interfacing with SAS Viya resources and embedding SAS Viya content into custom web pages and portals.ĪPI End-to-End Use Cases This GitHub project aims to leverage the knowledge base of the SAS users and developers community for better SAS API documentation, user guides and quick starts. SAS Customer Intelligence 360 Use data, analytics and insights on prospects and customers to create relevant, individualized experiences in real time. The REST APIs are written to make it easy to integrate the capabilities of SAS Viya to help build applications or create scripts. REST REST APIs for any client language to access SAS analytics, data and services. Integrate open source languages and agile technology with the capabilities of SAS analytics. SAS provides application and enterprise developers, data scientists, and analysts with access to SAS services. (See Cochrane's Asset Pricing book for details.Open Source SAS Open source development resources for developers. 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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