SAS® High-Performance Econometrics

While data continues to grow, decision makers need answers faster than ever. SAS High-Performance Econometrics allows you to develop analytical models using the complete data set. You can estimate models that use thousands of variables to produce more accurate and timely insights.

SAS High-Performance Econometrics is available for execution in a highly scalable, distributed in-memory processing architecture.

With models that can run in minutes or seconds, you can perform more frequent modeling iterations and use sophisticated analytics to get answers to questions you never had time to ask. This solution is available on Greenplum and Teradata appliances, as well as on commodity hardware.


  • Seize new opportunities quickly and confidently, detect unknown risks and make the right choices.
  • Use all data with advanced modeling techniques and perform more model iterations to get answers to questions you never had time to ask.
  • Derive insights at breakthrough speeds for high-value and time-sensitive decision making.
  • Take advantage of a highly scalable and reliable analytics infrastructure to test more ideas and multiple scenarios with all your data.


High-performance count regression

  • Estimates regression models where the dependent variable represents counts (e.g., the number of events recorded in some period or for some subject).
  • Supports Poisson and negative binomial models.
  • Supports zero-inflated Poisson and negative binomial models, and can fit separate regressors for the zero-inflated distribution.
  • Estimates parameters by using the maximum likelihood method.

High-performance qualitative and limited independent variable models

  • Estimates linear, censored and truncated regression models with heteroscedasticity.
  • Estimates stochastic frontier production and cost models.
  • Contains options for Bayesian estimation.

High-performance loss distribution/severity models

  • Estimates probability distributions for the severity (magnitude) of random events (including those with negative effects – e.g., the magnitude of damages caused by natural disasters, distributions of losses claimed under insurance policies, or the severity of disease outbreaks – as well as events with positive effects – e.g., the intermittent demand for certain products).
  • Estimates regression models for the scale of the severity distribution.
  • Provides nine different probability distributions, including the Tweedie distribution, and can automatically select the best-fitting distribution.
  • Allows users to add additional probability distributions.
  • Can model data truncation and data censoring.

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