SAS/ETS^{®}
SAS/ETS 14.2 Highlights

 The ARIMA Procedure
Analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data by using the autoregressive integrated movingaverage (ARIMA) or autoregressive movingaverage (ARMA) model.  The AUTOREG Procedure
Estimates and forecasts linear regression models for time series data when the errors are autocorrelated or heteroscedastic.  The COMPUTAB Procedure
Produces tabular reports generated using a programmable data table.  The COPULA Procedure
Enables the user to fit multivariate distributions or copulas from a given sample data set.  The COUNTREG Procedure
Analyzes regression models in which the dependent variable takes nonnegative integer or count values.  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.  The ENTROPY Procedure (Experimental)
Implements a parametric method of linear estimation based on generalized maximum entropy.  The ESM Procedure
Generates forecasts by using exponential smoothing models with optimized smoothing weights for many time series or transactional data.  The EXPAND Procedure
Converts time series from one sampling interval or frequency to another and interpolates missing values in time series.  The FORECAST Procedure
Provides a quick and automatic way to generate forecasts for many time series in one step.  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.  The MDC Procedure
Analyzes models in which the choice set consists of multiple alternatives.  The MODEL Procedure
Analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations.  The PANEL Procedure
Analyzes a class of linear econometric models that commonly arise when time series and crosssectional data are combined.  The PDLREG Procedure
Estimates regression models for time series data in which the effects of some of the regressor variables are distributed across time.  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.  The SEVERITY Procedure
Estimates parameters of any arbitrary continuous probability distribution that is used to model the magnitude (severity) of a continuousvalued event of interest.  The SIMILARITY Procedure
Computes similarity measures associated with timestamped data, time series, and other sequentially ordered numeric data.  The SIMLIN Procedure
Reads the coefficients for a set of linear structural equations, which are usually produced by the SYSLIN procedure.  The SPECTRA Procedure
Performs spectral and crossspectral analysis of time series.  The SSM Procedure
Performs state space modeling of univariate and multivariate time series and longitudinal data. <  The STATESPACE Procedure
Uses the state space model to analyze and forecast multivariate time series.  The SYSLIN Procedure
Estimates parameters in an interdependent system of linear regression equations.  The TIMEDATA Procedure
Analyzes timestamped transactional data with respect to time and accumulates the data into a time series format.  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.  The TIMESERIES Procedure
Analyzes timestamped transactional data with respect to time and accumulates the data into a time series format.  The TSCSREG Procedure
Analyzes a class of linear econometric models that commonly arise when time series and crosssectional data are combined.  The UCM Procedure
Analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM).  The VARMAX Procedure
Estimates the model parameters and generates forecasts associated with vector autoregressive movingaverage processes with exogenous regressors (VARMAX) models.  The X11 Procedure
Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.  The X12 Procedure
Makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.  The X13 Procedure
Makes additive or multiplicative adjustments and creates an output data set that contains the adjusted time series and intermediate calculations.
Topics
SAS/ETS Documentation Examples
For examples in the documentation, go to SAS/ETS software documentation examples.SAS/ETS Software Examples
The following SAS/ETS software examples are not included in the SAS/ETS documentation and are available only on the Web.
 Accounting for Missing Observations in Time Series Data
 Analysis of Unobserved Component Models Using PROC UCM
 Bivariate Granger Causality Test
 Bootstrapping Correct Critical Values in Tests for Structural Change
 Calculating Economic Indices
 Calculating Elasticities from a Translog Cost Function
 Calculating Elasticites in an Almost Ideal Demand System
 Calculating Price Elasticity of Demand
 Chow Test for Structural Breaks
 Computing Marginal Effects for Discrete Dependent Variable Models
 Efficiency Test for Estimators by Simulation
 Efficient Method of Moments Estimation of a Stochastic Volatility Model
 Estimating a Derived Demand System from a Translog Cost Function
 Estimating an Almost Ideal Demand System Model
 Estimating a ConsumptionBased Asset Pricing Model
 Estimating GARCH Models
 Fitting a Capital Asset Pricing Model
 Forecasting a Seasonal ARMA Process
 Heteroscedastic Modeling of the Fed Funds Rate
 Heteroscedastic TwoStage Least Squares Regression with PROC MODEL
 Multiple Imputation for a GARCH(1,1) Model
 Overlaying Multiple Forecast Methods in Time Series Plots
 Plotting Time Series Data
 Regression Model with Correction of Heteroscedasticity
 Specification Test for NonNested Models
 Testing for Returns to Scale in a CobbDouglas Production Function
 Tourism Demand Modeling and Forecasting with PROC VARMAX
 Transforming the Frequency of Time Series Data
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2016 Technical Papers
 How Do My Neighbors Affect Me? SAS/ETS Methods for Spatial Econometric Modeling
Wu, Guohui; Chvosta,Jan; SAS Institute, Inc. 2016This paper describes how geospatial methods are implemented in SAS/ETS and illustrates some ways you can incorporate spatial data into your modeling toolkit.
 Linear State Space Models in Retail and Hospitality
Beth Cubbage; SAS Institute, Inc. 2016This paper explores a linear state space approach to understanding retail and hospitality industry challenges, applying the SAS/ETS SSM procedure.
 Macroeconomic Simulation Analysis for Multiasset Class Portfolio Returns
Jayaraman, Srikant ; Burdis, Joe; Nagar, Lokesh; SAS Institute, Inc. 2016This paper proposes a technique to extend scenario analysis to an unconditional simulation capturing the distribution of possible macroeconomic climates and hence, the true multivariate distribution of returns.
 Spatial Dependence, Nonlinear Panel Models, and More New Features in SAS/ETS 14.1
Chvosta, Jan; SAS Institute, Inc. 2016This paper highlights the many enhancements to SAS/ETS 14.1 software and demonstrates how these features can help your organization increase revenue and enhance productivity.
SAS/ETS software provides extensive facilities for analyzing time series and performing financial analysis.
Econometrics and Systems Modeling
Systems modeling for econometric data is done in three parts: econometric modeling, simulation, and forecasting. Often, these tasks are performed sequentially. A model is fitted to the data, then simulated with historical data, and finally used for forecasting. Models for estimation can consist of a single equation or a system of equations; they can be linear, nonlinear, or ordinary differential equations; they can require restrictions on parameters. SAS/ETS software enables you to estimate and test hypotheses for all these types of models.
Time Series AnalysisTime series are any univariate or multivariate data collected over time. SAS/ETS software includes a wide range of tools for analyzing time series data. You can estimate relationships and produce forecasts that make use of information in past values, independent or explanatory variables, and indicator or dummy variables. In addition, you can model and predict the autoregressive conditional heteroscedastic (ARCH) model or its generalizations (GARCH). Additional tools provide regression analysis for linear models with distributed lags and time series crosssectional regression analysis for panel data.
You can perform multiple regression in the presence of serially correlated error terms, fit models that allow for an error term generated by an autoregressive integrated movingaverage (ARIMA) process, or use spectral analysis to decompose a series into cyclical components or to perform frequency domain tests.
Automatic ForecastingForecasting is the combining of knowledge from the past and future expectations with an estimated model to produce likely outcomes for the future. It enables more accurate predictions of the future to be made, reducing the uncertainty inherent in the decisionmaking process.
Many of the SAS/ETS procedures have options that facilitate the forecasting of time series variables.
The Time Series Forecasting SystemSAS/ETS software includes a pointandclick application for exploring and analyzing univariate time series data. You can use the automatic model selection facility to select the bestfitting model for each time series, or you can use the system's diagnostic features and time series modeling tools interactively to develop forecasting models customized to best predict your time series. The system provides both graphical and statistical features to help you choose the best forecasting method for each series.
Data ManipulationSAS/ETS software contains tools that can be used to convert irregularly spaced data to equally spaced data, interpolate missing values, or convert time series data from one frequency to another (such as from weekly to monthly or vice versa).
Seasonal time series can be adjusted using the U.S. Bureau of the Census X11 or X13 Seasonal Adjustment algorithms, and the X11ARIMA or X13ARIMA methods developed by Statistics Canada.
Access to Economic and Financial DatabasesSAS/ETS software makes it easy to access directly many of the most popular commercially available economic and financial time series databases. Data can be extracted from files supplied by government and commercial data vendors and then converted into SAS data sets.
Financial Analysis and ReportingWidely varying credit market conditions in the past few decades have given rise to many new types of financing arrangements. SAS/ETS software provides the means to compare quickly and easily different loans, to analyze fixed and variable rate loans, to analyze buydown and balloon loans, to perform calculations, and to generate financial reports.