SAS/ETS®

SAS/ETS User's Guide - Procedures

More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

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

For more SAS/ETS books, visit the bookstore.

SAS/ETS 15.1 User's Guide - Procedures

For the complete SAS/ETS 15.1 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 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   
    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 that contains the adjusted time series and intermediate calculations.
  • The X12 Procedure  PDF   |   HTML
    Makes additive or multiplicative adjustments and creates an output data set that contains 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

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

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

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

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

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

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

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

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

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

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]
More about This Product

Product Resources

Feedback

Send a Comment

Related Books for Purchase

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

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

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

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

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

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.