SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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.

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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.

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

**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]

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

For the complete

**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]

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

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]

SAS for Forecasting Time Series, Third Edition

By John Brocklebank and David Dickey

For more SAS/ETS books, visit the bookstore.

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

- ARIMA [HTML]
- AUTOREG [HTML]
- COMPUTAB [HTML]
- COUNTREG [HTML]
- DATASOURCE [HTML]
- ENTROPY [HTML]
- ESM [HTML]
- EXPAND [HTML]
- FORECAST [HTML]
- LOAN [HTML]
- MDC [HTML]
- MODEL [HTML]
- PANEL [HTML]
- PDLREG [HTML]
- QLIM [HTML]
- SEVERITY [HTML]
- SIMILARITY [HTML]
- SIMLIN [HTML]
- SPECTRA [HTML]
- STATESPACE [HTML]
- SYSLIN [HTML]
- TIMEID [HTML]
- TIMESERIES [HTML]
- TSCSREG [HTML]
- UCM [HTML]
- VARMAX [HTML]
- X11 [HTML]
- X12 [HTML]