SAS Econometrics

SAS^® Econometrics

Better strategic and tactical planning for proactively shaping a profitable future.

Find answers to challenging, real-world questions by modeling complex business and economic scenarios using huge amounts of diverse data. With SAS Econometrics running on SAS^® Viya^™, you can analyze the dynamic impact that specific events might have over time – and make proactive, data-driven decisions.

SAS Econometrics shown on desktop monitor

Find new opportunities and hidden risks fast.

Now you can estimate models that use thousands of variables to uncover new opportunities and expose hidden risks with unprecedented speed and accuracy. Then use the insights gained to make timely data-driven choices and better business decisions. SAS Viya, our distributed, in-memory platform, delivers econometric modeling results fast. And in-memory data persistence eliminates the need to load data multiple times during iterative analysis, so you get faster answers to time- and event-specific questions.

Use your preferred programming language.

Python, Java, R and Lua programmers can experience the power of SAS Econometrics without having to learn SAS. Because SAS Viya is an open platform, programmers can access trusted and tested SAS econometric models from their programming language of choice – and take advantage of the latest open source methods.

Make better, more scientific decisions.

Understand how varying economic and market conditions, customer demographics, pricing decisions, marketing activities and more can affect your organization. The solution enables you to model complex business and economic scenarios, and analyze the impact specific events might have over time – even when time dependencies, simultaneous relationships or dynamic processes complicate the analyses.

Demo

See SAS^® Econometrics in action.

Mike Gilliland, Product Marketing Manager at SAS, demonstrates how you can use SAS Econometrics to analyze a class of linear econometric models that commonly occur when time series and cross sectional data are combined – often referred to as panel data.

Key Features

Count regression models. Analyze the number of times specific events occur during a time period using the CNTSELECT procedure to produce regression models for integer-valued dependent variables.
Severity regression models. Estimate probability distributions for the magnitude of both positive and negative random events.
Qualitative and limited-dependent variable regression models. Estimate regression models for univariate qualitative and limited-dependent variables using the CQLIM procedure.
Copula models. Model multivariate dimensions of risk factors, which is useful for modeling multiple correlated risk factors that are non-normally distributed. Supports simulations from Normal, T, Clayton, Gumbel and Frank copulas.

Panel data econometric models. Analyze relationships between the past and the future using regression models for panel data, which can have a large number of observations and more than one observation per time period.
Open, cloud-enabled, in-memory platform. Distribute analysis and data tasks across multiple computing nodes, and enable fast, concurrent, multiuser access to data in memory with our open, scalable, distributed in-memory processing platform – SAS Viya.

Full Features List

System Requirements