Products & Solutions / Econometrics & Time Series Analysis

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.

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Features

  • High-performance econometrics
  • Cross-sectional econometric methods
  • Time series analysis
  • Panel data econometrics
  • Time series data tools
  • Data acquisition tools

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Trend analysis: SAS/ETS provides seasonal decomposition and adjustment for time-series data.


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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

High-performance econometrics
  • Several procedures have been enabled for high performance in a single-server SMP environment:
    • HPCOUNTREG for high-performance count regression.
    • HPSEVERITY for high-performance loss distribution/severity.
    • HPQLIM for high-performance qualitative and limited independent variable models.
Cross-sectional econometric methods
  • Truncated regression (with Bayesian estimation options).
  • Censored regression (with Bayesian estimation options).
  • Bivariate probit.
  • Bivariate tobit.
  • Stochastic frontier production and cost models.
  • Multivariate limited dependent models.
  • Regression with heteroscedastic and autocorrelated consistent standard errors.
  • Sample selection and switching regression models.
  • Logit/probit with heteroscedastic errors.
  • Instrumental variables (2SLS, 3SLS, LIML, FIML, K-Class, MELO, GMM).
  • Seemingly unrelated regression (SUR, ISUR).
  • Multinomial/conditional logit.
  • Heteroscedastic extreme value model.
  • Mixed logit model.
  • Multinomial probit model.
  • Nested logit model.
  • Simulation and prediction tools.
  • Copula estimation and simulation.
  • Loss/severity modeling with censoring/truncation and regression effects.
  • Poisson (zero inflated Poisson) regression.
  • Negative binomial (zero inflated negative binomial) regression.
Time series analysis
  • ARIMA (ARIMAX) models.
  • Dynamic regression and transfer function models.
  • X-12-ARIMA.
  • Polynomial distributed lag models.
  • Unobserved components models.
  • Linear state space models.
  • Vector autoregressive models.
  • Vector error correction models.
  • Bayesian vector autoregressive models.
  • Vector autoregressive moving average models.
  • Multivariate GARCH models.
  • Exponential smoothing with optimized smoothing weights.
  • GARCH models (IGARCH, EGARCH, QGARCH, TGARCH, PGARCH, GARCH-M).
  • Spectral analysis.
  • Time series data mining tools.
  • Spectral and cross-spectral analysis.
Panel data econometrics
  • Pooled and between estimators with robust standard errors.
  • One- and two-way fixed effects regression with robust standard errors.
  • One- and two-way random effects regression with robust standard errors.
  • Autoregressive and moving average models.
  • Dynamic panel GMM.
  • Poisson (zero inflated Poisson) with fixed/random effects.
  • Negative binomial (zero inflated negative binomial) with fixed/random effects.
  • Linear state space panel data model.
Time series data tools
  • Transactional accumulation.
  • Seasonal adjustment.
  • X-11, X-12 seasonal adjustment.
  • Series imputation/extrapolation.
  • Time series frequency manipulation.
  • Time series differencing and transformations.
Data acquisition tools
  • FAME, DRI, Standard & Poor's (COMPUSTAT), FactSet, Haver Analytics DLX and CRSP.
  • Bureau of Economic Analysis, Bureau of Labor Statistics.
  • International Monetary Fund (IMF), Organization for Economic Cooperation and Development (OECD).

To learn more, see the SAS/ETS documentation.

Demos

Demo
Demo of SAS Econometrics and Time Series for JMP

Combine the flexibility and interactivity of JMP with the computational power of SAS.

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Screenshots

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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.

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The COPULA procedure provides various plots that help you analyze the underlying data.

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Panel data state space example in the SSM procedure.

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Repeated measures state space example in the SSM procedure.

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PROC X12 has new seasonal adjustment features.

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PROC X12 has new seasonal adjustment features.

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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.

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Diagnostics: Example of default diagnostics plot for the AUTOREG procedure.

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Diagnostics: Example of default diagnostics plot for the MODEL procedure.

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Model the probability of extreme events using PROC SEVERITY.

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The SEVERITY procedure also allows fitting models for interval-censored data.

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Generate forecast plots automatically in PROC ARIMA.

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System Requirements

Host Platforms/Server Tier
  • HP/UX on Itanium: 11iv3 (11.31)
  • IBM AIX R64 on POWER architecture 7.1
  • IBM z/OS: V1R11 and higher
  • Linux x64 (64-bit): Novell SuSE 11 SP1; Red Hat Enterprise Linux 6.1; Oracle Linux 6.1
  • Microsoft Windows on x64 (64-bit):
    Desktop: Windows 7* x64 SP1; Windows 8** x64
    Server: Windows Server 2008 x64 SP2 Family; Windows Server 2008 R2 SP1 Family; Windows Server 2012 Family
  • Solaris on SPARC: Version 10 Update 9
  • Solaris on x64 (x64-86): Version 10 Update 9; Version 11
Required Software
  • Base SAS® 

* NOTE: Windows 7 supported editions are: Professional, Ultimate and Enterprise.
** NOTE: Supported editions include: Windows 8, Windows 8 Pro, Windows 8 Enterprise.

Please consult your local SAS sales representative if you have questions about your platform requirements. Also, for more detailed information, please visit our support site at http://support.sas.com/resources/sysreq/.

Ready to learn more?

Call us at 1-800-727-0025 (US and Canada) or request more information.