SAS/ETS® Software
Econometric and time series analysis for modeling, forecasting and simulating business processes
SAS/ETS offers a broad array of time series, forecasting and econometric techniques that enable modeling, forecasting and simulation of business processes for improved strategic and tactical planning. It can help you understand the impact that factors such as economic and market conditions, customer demographics, pricing decisions and marketing activities have on business.
Benefits
- Analyze the impact of promotions and events.
- Model customer choices.
- Measure and predict marketing investment activities.
- Provide the information needed to make better staffing decisions.
- Model risk factors and predict economic outcomes.
Features
- Full range of forecasting and time series methods
- Time Series Forecasting System
- Econometric analysis
- State space modeling
- Time series data management and preparation
- Financial analysis
- Tools to access commercial and government databases
- SAS Econometrics and Time Series for JMP (also requires SAS Visual Data Discovery)
Screenshot
Understand how income and price impact the probability of purchase decisions.
More Demos and Screenshots
How SAS® Is Different
- Sophistication. The depth and flexibility of the SAS/ETS modeling environment can accommodate any business scenario. SAS/ETS can handle even the most complex business situations.
- Automation. SAS/ETS saves time and resources by automatically accounting for seasonal fluctuations and trends, and can automatically detect the most appropriate forecasting method for generating demand forecasts.
- Access to external data. Built-in capabilities provide users with easy access to external data that can improve forecasting models. SAS/ETS provides tools to access many commercial and government databases.
Benefits
- Analyze the impact of promotions and events. The time series and econometric capabilities of SAS/ETS software provide users with several mechanisms for determining promotional lift. The depth and flexibility of the SAS modeling environment can accommodate any business scenario. Determining the effectiveness of promotions and events enables you to better allocate marketing dollars in the future.
- Model customer choices. SAS/ETS enables you to maximize marketing efforts by understanding which product features are important to a particular audience. Modeling customer choices based on the attributes of customers and their choices helps improve business strategy by predicting customers' decisions. Understanding these choices and the factors that influence them enables you to adjust marketing strategies or fee structures to modify choices or target the right population.
- Measure and predict marketing investment activities. SAS/ETS software can help you understand which key business drivers are having the highest impact on consumer demand. You can model customer demand based on marketing mix activities that measure the impact of pricing, advertising, in-store merchandising, store distribution, sales promotions and competitive activities. Using simulation and optimization tools, you can maximize investments to drive profitable volume growth.
- Provide the information needed to make better staffing decisions. SAS/ETS can provide forecasts of demand for services so that organizations can maximize staff resources. It can automatically account for seasonal fluctuations and trends, and can select the best method for generating the demand forecasts. Efficient staff allocations mean customers' needs can be met with no wasted resources.
- Model risk factors and predict economic outcomes. Copula methods in SAS/ETS software let you model multivariate dimensions of risk factors. These methods are valuable in risk management applications where many correlated risk factors must be modeled but where the risk factors are non-normally distributed. SAS/ETS can fit probability distributions for the severity (magnitude) of random events, such as the distribution of insurance claims after a disaster, disease outbreaks or the ordering of products with intermittent demand.
Features
- Full range of forecasting and time series methods
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Trend extrapolation; exponential smoothing; Winters' method (additive and multiplicative); ARIMA (Box-Jenkins).
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Structural time series models or unobserved components models.
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Dynamic regression or transfer function models.
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Joint forecasting of multiple time series using vector time series analysis and general state space models.
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Automatic outlier and event detection.
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Time series decomposition and seasonal adjustment.
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Spectral and cross-spectral analysis for finding periodicities or cyclical patterns in your data.
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Singular spectrum analysis.
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Similarity analysis for sets of time series.
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Estimate model parameters and simulate random data from fitted copula distributions.
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- Time Series Forecasting System
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Point-and-click interface for exploring and forecasting time series data.
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Automatic selection of the best-fitting forecasting model for each time series.
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Mathematically optimized model parameters.
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Interactive model development facility for more experienced forecasters.
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Graphical display of time series diagnostics tests.
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Inclusion of regression variables and unusual events in the forecasting model.
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Diagnostic checks on fitted models.
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Option to statistically combine multiple forecasts.
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- Econometric analysis
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Regression with correction for autocorrelated errors.
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Fitting, analyzing and simulation of simultaneous systems of both linear and nonlinear regression models.
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Multinomial discrete choice analysis.
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What-if, Monte Carlo simulation.
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Time series cross-sectional analysis.
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Qualitative and limited dependent variable models.
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- State space modeling
- Linear state space modeling and forecasting of time series and longitudinal data.
- Time series data management and preparation
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Conversion of time series from one sampling frequency to another.
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Interpolation of missing values.
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Aggregation of time-stamped transactional data into time series.
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More than 100 time series transformation operations.
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Custom time intervals (functionality provided in Base SAS).
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- Financial analysis
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- Interactive analysis system for time-value-of-money analysis.
- Tools to access commercial and government databases
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Commercial database vendors: FAME, DRI, Standard & Poor's (COMPUSTAT), Haver Analytics DLX and CRSP.
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US government data: Bureau of Economic Analysis, Bureau of Labor Statistics.
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International agency data: International Monetary Fund (IMF), Organization for Economic Cooperation and Development (OECD).
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SAS/ACCESS® interfaces and SAS Data Surveyors (licensed separately) provide seamless read, write and update access to other data sources, including relational and nonrelational databases, PC file formats, data warehouse appliances and enterprise applications.
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- SAS Econometrics and Time Series for JMP (also requires SAS Visual Data Discovery)
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Specify some complex econometrics and time series models in an easy-to-use interface that integrates SAS® and JMP® software:
- Fit models to time series data when the errors are autocorrelated or heteroscedastic.
- Fit panel analysis when you have both time series and cross-sectional data.
- Fit unobserved components models to equally spaced univariate time series data.
Demos
Demo of SAS Econometrics and Time Series for JMP
Build autoregressive or heteroscedastic error models, unobserved component models, and panel analysis models in an easy-to-use interface that integrates SAS and JMP software.
Screenshots
Understand how income and price impact the probability of purchase decisions.
SAS/ETS helps you understand which factors influence the choices people make, such as how income and price impact the probability of purchase decisions.
Estimate the impact of price changes on the demands for goods and services.
With SAS/ETS you can estimate the impact of price changes on the demands for goods and services. In this example, own- and cross-price elasticities were calculated for four products.
SAS/ETS software can automatically account for seasonal fluctuations.
SAS/ETS software can automatically account for seasonal fluctuations and selects the best method for generating forecasts.
Diagnostics: Here is an example of default diagnostic plots for the autoregression procedure (PROC AUTOREG).
Diagnostics: Here is an example of default diagnostic plots for the autoregression procedure (PROC AUTOREG).
Trend analysis: SAS/ETS provides seasonal decomposition and adjustment for time-series data.
Trend analysis: SAS/ETS provides seasonal decomposition and adjustment for time-series data.
The COPULA procedure provides various plots that help you analyze the underlying data.
The SEVERITY procedure fits probability distributions for the severity (magnitude) of random events.
SAS Econometrics and Time Series Analysis for JMP is a graphical user interface that provides easy access to SAS/ETS® procedures.
Build autoregressive or heteroscedastic error models, unobserved component models, and panel analysis models in an easy-to-use interface that integrates SAS and JMP software.
System Requirements
Host Platforms
- HP/UX on Itanium: 11iv3 (11.31)
- HP/UX on PA-RISC: 11iv3 (11.31)
- IBM AIX on POWER architectures: 6.1 and 7.1
- IBM z/OS: V1R10 and higher
- Linux (32-bit): Novell SuSE 10 and 11; RHEL 5 and 6
- Linux x64 (64-bit): Novell SuSE 10 and 11; RHEL 5 and 6
- Microsoft Windows (32-bit): Windows XP Professional, Windows Vista *, Windows 7**, Windows Server 2003 family, Windows Server 2008 family
- Microsoft Windows on x64 (64-bit): Windows XP Professional for x64, Windows Vista* for x64, Windows 7** for x64, Windows Server 2003 family for x64, Windows Server 2008 family for x64
- Solaris on SPARC: Version 10 Update 8
- Solaris on x64 (x64-86): Version 10 Update 8
Required Software
- Base SAS®
* NOTE: Windows Vista supported editions are: Enterprise, Ultimate and Business.
** NOTE: Windows 7 supported editions are: Enterprise, Ultimate and Professional.
Ready to learn more?
Call us at 1-800-727-0025 (US and Canada) or request more information.
