SAS/ETS^{®}
SAS/ETS 14.2 Highlights

Procedures
 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
Estimates a compound distribution model (CDM) by modeling the severity (magnitude) of loss and frequency (count) of loss while aggregating them into one estimate.  The HPCOPULA Procedure
Enables you to simulate realizations of multivariate distributions by using the copula approach.  The HPCOUNTREG Procedure
Fits regression models in which the dependent variable takes on nonnegative integer or count values.  The HPPANEL Procedure
Fits a class of linear econometric models that commonly arise when time series and crosssectional data are combined.  The HPQLIM Procedure
Analyzes univariate limited dependent variable models in which dependent variables are observed only in a limited range of values  The HPSEVERITY Procedure
Fits models for statistical distributions of the severity (magnitude) of events.  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 SPATIALREG Procedure New Procedure!
Analyzes spatial econometric models for crosssectional data in which observations in the data are spatially referenced or georeferenced.  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.
Interface Engines
 The SASECRSP Interface Engine
Enables SAS users to access and process time series, events, portfolios, and group data that reside in Center for Research in Security Prices databases (CRSPAccess data).  The SASEFAME Interface Engine
Provides a seamless interface between Fame and SAS data to enable SAS users to access and process time series, case series, and formulas that reside in a Fame database.  The SASEFRED Interface Engine
Enables SAS users to retrieve economic data from the FRED website, which is hosted by the Economic Research Division of the Federal Reserve Bank of St. Louis. FRED stands for Federal Reserve Economic Data.  The SASEHAVR Interface Engine
Enables SAS users to read economic and financial time series data that reside in a Haver Analytics DLX (Data Link Express) database.  The SASENOAA Interface Engine New Interface Engine!
Enables SAS programmers to retrieve severe weather data from the National Oceanic and Atmospheric Administration (NOAA) Severe Weather Data Inventory (SWDI) web service.  The SASEQUAN Interface Engine
Enables SAS users to retrieve economic and other time series data from the Quandl website, which is hosted by Quandl.  The SASERAIN Interface Engine New Interface Engine!
Enables SAS users to retrieve weather data from the World Weather Online website.  The SASEXCCM Interface Engine
Enables SAS users to access the CRSP/Compustat Merged (CCM) Database  The SASEXFSD Interface Engine
Enables SAS users to access both FactSet data and FactSetsourced data that are provided by the FactSet OnDemand service (formerly known as FASTFetch).
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
Videos
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Air date: September 27, 2016 
2017 Technical Papers
 Automatic Singular Spectrum Analysis and Forecasting
Leonard, Michael; Elsheimer, Bruce; SAS Institute, Inc. 2017This paper provides an introduction to singular spectrum analysis and demonstrates how to use SAS/ETS software to perform it.
 Big Value from Big Data: SAS/ETS Methods for Spatial Econometric
Modeling in the Era of Big Data
Wu, Guohui, Chvosta, Jan; SAS Institute, Inc. 2017This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS software.
 Detecting and Adjusting Structural Breaks in Time Series and Panel Data
Using the SSM Procedure
Gutierrez, Roberto G.; SAS Institute, Inc. 2017This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios.
 Multivariate Time Series: Recent Additions to the VARMAX Procedure
Chen, Xilong; Kechagias, Stefanos; SAS Institute, Inc. 2017This paper focuses on two new additions to the VARMAX procedure in SAS/ETS 14.1 and 14.2: the enhancement of the vector error correction model (VECM) and the vector autoregressive fractionally integrated moving average (VARFIMA) model that you can use to model longrange dependent (LRD) time series.
 Using a Dynamic Panel Estimator to Model Change in Panel Data
Selukar, Rajesh; SAS Institute, Inc. 2017This paper guides you through the process of using the typical estimation method for this situation—the generalized method of moments (GMM)—and the process of selecting the optimal set of instrumental variables for your model.
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.