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SAS/ETS®

SAS/ETS User's Guide - Procedures

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Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 14.3 User's Guide - Procedures

For the complete SAS/ETS 14.3 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure  PDF   |   HTML
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model.
  • The AUTOREG Procedure  PDF   |   HTML
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic.
  • The COMPUTAB Procedure  PDF   |   HTML
    Produces tabular reports generated using a programmable data table.
  • The COPULA Procedure  PDF   |   HTML
    Enables the user to fit multivariate distributions or copulas from a given sample data set.
  • The COUNTREG Procedure  PDF   |   HTML
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values.
  • The DATASOURCE Procedure  PDF   |   HTML
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set.
  • The ENTROPY Procedure (Experimental)  PDF   |   HTML
    Implements a parametric method of linear estimation based on generalized maximum entropy.
  • The ESM Procedure  PDF   |   HTML
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.
  • The EXPAND Procedure  PDF   |   HTML
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series.
  • The FORECAST Procedure  PDF   |   HTML
    Provides a quick and automatic way to generate forecasts for many time series in one step.
  • The HPCDM Procedure   PDF   |   HTML
    Models compound distributions that are formed by combining models of the frequency of events and the severity of those events.
  • The HPCOPULA Procedure  PDF   |   HTML
    Models multivariate distributions by using copula methods.
  • The HPCOUNTREG Procedure  PDF   |   HTML
    Fits regression models to analyze and predict counts of the number of events.
  • The HPPANEL Procedure  PDF   |   HTML
    Fit regression models to analyze and predict panel data where variables are recorded both over cases and over time.
  • The HPQLIM Procedure  PDF   |   HTML
    Fits regression models to analyze and predict qualitative and limited dependent variables where limitations or selection of the observed values must be modeled.
  • The HPSEVERITY Procedure  PDF   |   HTML
    Fits regression models to analyze and predict the severity of events by using a variety of probability distributions.
  • The LOAN Procedure  PDF   |   HTML
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans.
  • The MDC Procedure  PDF   |   HTML
    Analyzes models in which the choice set consists of multiple alternatives.
  • The MODEL Procedure  PDF   |   HTML
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations.
  • The PANEL Procedure  PDF   |   HTML
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
  • The PDLREG Procedure  PDF   |   HTML
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time.
  • The QLIM Procedure  PDF   |   HTML
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values.
  • The SEVERITY Procedure  PDF   |   HTML
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest.
  • The SIMILARITY Procedure  PDF   |   HTML
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data.
  • The SIMLIN Procedure  PDF   |   HTML
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure.
  • The SPATIALREG Procedure  PDF   |   HTML
    Analyzes spatial econometric models for cross-sectional data whose observations are spatially referenced or georeferenced..
  • The SPECTRA Procedure  PDF   |   HTML
    Performs spectral and cross-spectral analysis of time series.
  • The SSM Procedure  PDF   |   HTML
    Performs state space modeling of univariate and multivariate time series and longitudinal data.
  • The STATESPACE Procedure  PDF   |   HTML
    Uses the state space model to analyze and forecast multivariate time series.
  • The SYSLIN Procedure  PDF   |   HTML
    Estimates parameters in an interdependent system of linear regression equations.
  • The TIMEDATA Procedure  PDF   |   HTML
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
  • The TIMEID Procedure  PDF   |   HTML
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis.
  • The TIMESERIES Procedure  PDF   |   HTML
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
  • The TMODEL Procedure  PDF   |   HTML   Experimental
    Incorporates high-performance computational techniques and offers new features that enhance the functionality of PROC MODEL.
  • The TSCSREG Procedure  PDF   |   HTML
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
  • The UCM Procedure  PDF   |   HTML
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM).
  • The VARMAX Procedure  PDF   |   HTML
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models.
  • The X11 Procedure  PDF   |   HTML
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
  • The X12 Procedure  PDF   |   HTML
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
  • The X13 Procedure  PDF   |   HTML
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 14.2 User's Guide - Procedures

For the complete SAS/ETS 14.2 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model.
    PDF   |   HTML
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic.
    PDF   |   HTML
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table.
    PDF   |   HTML
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set.
    PDF   |   HTML
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values.
    PDF   |   HTML
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set.
    PDF   |   HTML
  • The ENTROPY Procedure (Experimental)
    Implements a parametric method of linear estimation based on generalized maximum entropy.
    PDF   |   HTML
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.
    PDF   |   HTML
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series.
    PDF   |   HTML
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.
    PDF   |   HTML
  • The HPCDM Procedure
    Models compound distributions that are formed by combining models of the frequency of events and the severity of those events.
    PDF   |   HTML
  • The HPCOPULA Procedure
    Models multivariate distributions by using copula methods.
    PDF   |   HTML
  • The HPCOUNTREG Procedure
    Fits regression models to analyze and predict counts of the number of events.
    PDF   |   HTML
  • The HPPANEL Procedure
    Fit regression models to analyze and predict panel data where variables are recorded both over cases and over time.
    PDF   |   HTML
  • The HPQLIM Procedure
    Fits regression models to analyze and predict qualitative and limited dependent variables where limitations or selection of the observed values must be modeled.
    PDF   |   HTML
  • The HPSEVERITY Procedure
    Fits regression models to analyze and predict the severity of events by using a variety of probability distributions.
    PDF   |   HTML
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans.
    PDF   |   HTML
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives.
    PDF   |   HTML
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations.
    PDF   |   HTML
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF   |   HTML
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time.
    PDF   |   HTML
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values.
    PDF   |   HTML
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest.
    PDF   |   HTML
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data.
    PDF   |   HTML
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure.
    PDF   |   HTML
  • The SPATIALREG Procedure New Procedure!
    Analyzes spatial econometric models for cross-sectional data whose observations are spatially referenced or georeferenced.
    PDF   |   HTML
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series.
    PDF   |   HTML
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data.
    PDF   |   HTML
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series.
    PDF   |   HTML
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations.
    PDF   |   HTML
  • The TIMEDATA Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF   |   HTML
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis.
    PDF   |   HTML
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF   |   HTML
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF   |   HTML
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM).
    PDF   |   HTML
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models.
    PDF   |   HTML
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
    PDF   |   HTML
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
    PDF   |   HTML
  • The X13 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
    PDF   |   HTML
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 14.1 User's Guide - Procedures

For the complete SAS/ETS 14.1 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model.
    PDF   |   HTML
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic.
    PDF   |   HTML
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table.
    PDF   |   HTML
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set.
    PDF   |   HTML
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values.
    PDF   |   HTML
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set.
    PDF   |   HTML
  • The ENTROPY Procedure (Experimental)
    Implements a parametric method of linear estimation based on generalized maximum entropy.
    PDF   |   HTML
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.
    PDF   |   HTML
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series.
    PDF   |   HTML
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.
    PDF   |   HTML
  • The HPCDM Procedure
    Models compound distributions that are formed by combining models of the frequency of events and the severity of those events.
    PDF   |   HTML
  • The HPCOPULA Procedure
    Models multivariate distributions by using copula methods.
    PDF   |   HTML
  • The HPCOUNTREG Procedure
    Fits regression models to analyze and predict counts of the number of events.
    PDF   |   HTML
  • The HPPANEL Procedure
    Fit regression models to analyze and predict panel data where variables are recorded both over cases and over time.
    PDF   |   HTML
  • The HPQLIM Procedure
    Fits regression models to analyze and predict qualitative and limited dependent variables where limitations or selection of the observed values must be modeled.
    PDF   |   HTML
  • The HPSEVERITY Procedure
    Fits regression models to analyze and predict the severity of events by using a variety of probability distributions.
    PDF   |   HTML
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans.
    PDF   |   HTML
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives.
    PDF   |   HTML
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations.
    PDF   |   HTML
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF   |   HTML
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time.
    PDF   |   HTML
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values.
    PDF   |   HTML
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest.
    PDF   |   HTML
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data.
    PDF   |   HTML
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure.
    PDF   |   HTML
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series.
    PDF   |   HTML
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data.
    PDF   |   HTML
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series.
    PDF   |   HTML
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations.
    PDF   |   HTML
  • The TIMEDATA Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF   |   HTML
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis.
    PDF   |   HTML
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF   |   HTML
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF   |   HTML
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM).
    PDF   |   HTML
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models.
    PDF   |   HTML
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
    PDF   |   HTML
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
    PDF   |   HTML
  • The X13 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
    PDF   |   HTML
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 13.2 User's Guide - Procedures

For the complete SAS/ETS 13.2 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model.
    PDF (9.44MB)  |   HTML
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic.
    PDF (11.3MB)  |   HTML
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table.
    PDF (3.67MB)  |   HTML
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set.
    PDF (5MB)  |   HTML
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values.
    PDF (5.52MB)  |   HTML
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set.
    PDF (5.08MB)  |   HTML
  • The ENTROPY Procedure (Experimental)
    Implements a parametric method of linear estimation based on generalized maximum entropy.
    PDF (5.15MB)  |   HTML
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.
    PDF (3.23MB)  |   HTML
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series.
    PDF (3.28MB)  |   HTML
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.
    PDF (4.44MB)  |   HTML
  • The HPCDM Procedure (Experimental)
    Models compound distributions that are formed by combining models of the frequency of events and the severity of those events.
    PDF (5.22MB)  |   HTML
  • The HPCOPULA Procedure
    Models multivariate distributions by using copula methods.
    PDF (2.79MB)  |   HTML
  • The HPCOUNTREG Procedure
    Fits regression models to analyze and predict counts of the number of events.
    PDF (3.12MB)  |   HTML
  • The HPPANEL Procedure
    Fit regression models to analyze and predict panel data where variables are recorded both over cases and over time.
    PDF (2.99MB)  |   HTML
  • The HPQLIM Procedure
    Fits regression models to analyze and predict qualitative and limited dependent variables where limitations or selection of the observed values must be modeled.
    PDF (3.88MB)  |   HTML
  • The HPSEVERITY Procedure
    Fits regression models to analyze and predict the severity of events by using a variety of probability distributions.
    PDF (8.73MB)  |   HTML
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans.
    PDF (4.02MB)  |   HTML
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives.
    PDF (5.31MB)  |   HTML
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations.
    PDF (22.3MB)  |   HTML
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF (6.15MB)  |   HTML
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time.
    PDF (3.41MB)  |   HTML
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values.
    PDF (6.14MB)  |   HTML
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest.
    PDF (10.1MB)  |   HTML
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data.
    PDF (4.47MB)  |   HTML
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure.
    PDF (3.78MB)  |   HTML
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series.
    PDF (3.09MB)  |   HTML
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data.
    PDF (11.9MB)  |   HTML
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series.
    PDF (5.39MB)  |   HTML
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations.
    PDF (8.63MB)  |   HTML
  • The TIMEDATA Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF (2.49MB)  |   HTML
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis.
    PDF (3.09MB)  |   HTML
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format.
    PDF (3.91MB)  |   HTML
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined.
    PDF (2.39MB)  |   HTML
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM).
    PDF (8.08MB)  |   HTML
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models.
    PDF (10.3MB)  |   HTML
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
    PDF (3.62MB)  |   HTML
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
    PDF (8.45MB)  |   HTML
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 13.1 User's Guide - Procedures

For the complete SAS/ETS 13.1 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model. [HTML]
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic. [HTML]
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table. [HTML]
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set. [HTML]
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values. [HTML]
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set. [HTML]
  • The ENTROPY Procedure (Experimental)
    Implements a parametric method of linear estimation based on generalized maximum entropy. [HTML]
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data. [HTML]
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series. [HTML]
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.[HTML]
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans. [HTML]
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives. [HTML]
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. [HTML]
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time. [HTML]
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values. [HTML]
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest. [HTML]
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data. [HTML]
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure. [HTML]
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series. [HTML]
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data. [HTML]
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series. [HTML]
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations. [HTML]
  • The TIMEDATA Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. [HTML]
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis. [HTML]
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. [HTML]
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM). [HTML]
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models. [HTML]
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. [HTML]
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations. [HTML]
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Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 12.3 User's Guide - Procedures

Note: SAS/ETS 12.3 is essentially a maintenance release, with the exception that high-performance procedures for use in single-machine mode have been added. The SAS/ETS 12.3 documentation applies to both SAS/ETS 12.3 and 12.1.


For the complete SAS/ETS 12.3 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model. [HTML]
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic. [HTML]
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table. [HTML]
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set. [HTML]
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values. [HTML]
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set. [HTML]
  • The ENTROPY Procedure
    Implements a parametric method of linear estimation based on generalized maximum entropy. [HTML]
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data. [HTML]
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series. [HTML]
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.[HTML]
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans. [HTML]
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives. [HTML]
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. [HTML]
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time. [HTML]
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values. [HTML]
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest. [HTML]
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data. [HTML]
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure. [HTML]
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series. [HTML]
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data. [HTML]
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series. [HTML]
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations. [HTML]
  • The TCOUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values. [HTML]
  • The TIMEDATA Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. [HTML]
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis. [HTML]
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. [HTML]
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM). [HTML]
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models. [HTML]
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. [HTML]
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations. [HTML]
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 9.3 User's Guide - Procedures

For the complete SAS/ETS 9.3 User's Guide, go to the SAS/ETS product documentation page.

  • The ARIMA Procedure
    Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model. [HTML]
  • The AUTOREG Procedure
    Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic. [HTML]
  • The COMPUTAB Procedure
    Produces tabular reports generated using a programmable data table. [HTML]
  • The COPULA Procedure
    Enables the user to fit multivariate distributions or copulas from a given sample data set. [HTML]
  • The COUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values. [HTML]
  • The DATASOURCE Procedure
    Extracts time series and event data from many different kinds of data files distributed by various data vendors and stores them in a SAS data set. [HTML]
  • The ENTROPY Procedure
    Implements a parametric method of linear estimation based on generalized maximum entropy. [HTML]
  • The ESM Procedure
    Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data. [HTML]
  • The EXPAND Procedure
    Converts time series from one sampling interval or frequency to another and interpolates missing values in time series. [HTML]
  • The FORECAST Procedure
    Provides a quick and automatic way to generate forecasts for many time series in one step.[HTML]
  • The LOAN Procedure
    Analyzes and compares fixed rate, adjustable rate, buydown, and balloon payment loans. [HTML]
  • The MDC Procedure
    Analyzes models in which the choice set consists of multiple alternatives. [HTML]
  • The MODEL Procedure
    Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. [HTML]
  • The PANEL Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The PDLREG Procedure
    Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time. [HTML]
  • The QLIM Procedure
    Analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values. [HTML]
  • The SEVERITY Procedure
    Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest. [HTML]
  • The SIMILARITY Procedure
    Computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data. [HTML]
  • The SIMLIN Procedure
    Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure. [HTML]
  • The SPECTRA Procedure
    Performs spectral and cross-spectral analysis of time series. [HTML]
  • The SSM Procedure
    Performs state space modeling of univariate and multivariate time series and longitudinal data. [HTML]
  • The STATESPACE Procedure
    Uses the state space model to analyze and forecast multivariate time series. [HTML]
  • The SYSLIN Procedure
    Estimates parameters in an interdependent system of linear regression equations. [HTML]
  • The TCOUNTREG Procedure
    Analyzes regression models in which the dependent variable takes nonnegative integer or count values. [HTML]
  • The TIMEID Procedure
    Evaluates a variable in an input data set for its suitability as a time ID variable in SAS procedures and solutions that are used for time series analysis. [HTML]
  • The TIMESERIES Procedure
    Analyzes time-stamped transactional data with respect to time and accumulates the data into a time series format. [HTML]
  • The TSCSREG Procedure
    Analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. [HTML]
  • The UCM Procedure
    Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM). [HTML]
  • The VARMAX Procedure
    Estimates the model parameters and generates forecasts associated with vector autoregressive moving-average processes with exogenous regressors (VARMAX) models. [HTML]
  • The X11 Procedure
    Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. [HTML]
  • The X12 Procedure
    Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations. [HTML]

More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

Genetic Analysis of Complex Traits Using SAS
By Arnold Saxton

SAS for Forecasting Time Series, Second Edition
By John Brocklebank and David Dickey

Using SAS in Financial Research
By Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki

For more SAS/ETS books, visit the bookstore.

SAS/ETS 9.22 User's Guide - Procedures

For the complete SAS/ETS 9.22 User's Guide, go to the SAS/ETS product documentation page.