SAS/ETS^{®}
SAS/ETS 14.1 introduces the X13 procedure and enhancements to many other procedures.
X13 Procedure
Because the US Census Bureau has included the X12ARIMA methodology in the X13ARIMASEATS program, the X12 procedure has been renamed the X13 procedure. You can continue to specify PROC X12 as before, but the X12ARIMA methodology will be based on the current method in the X13ARIMASEATS method, including enhancements and improvements.
The following features have been added to the X13 procedure:
 The DIFFID= option has been added to the AUTOMDL statement. The DIFFID= option specifies the estimation method of identifying difference orders. This option is experimental in SAS/ETS 14.1.
 The DIFFIDITER= option has been added to the AUTOMDL statement. The DIFFIDITER= option specifies the maximum number of iterations for exact likelihood for DIFFID=EXACTFIRST. This option is experimental in SAS/ETS 14.1.
 The EXACT= option has been added to the ESTIMATE statement. The EXACT= option specifies the likelihood function to be used for estimating autoregressive (AR) and moving average (MA) parameters. This option is experimental in SAS/ETS 14.1.
 The EASTERMEANS= option has been added to the REGRESSION statement. The EASTERMEANS= option specifies the method to be used to calculate the means for the Easter regression variable.
 The following tables have been added to the displayed output for automatic modeling: "Check of the Residual LjungBox Q Statistic," "Initial Automatic Model Selection," and "Final Checks for Identified Model." In addition, if the orders have been altered during the final checks, the new Orders Altered column in the "Final Automatic Model Selection" table displays a value of Yes. These changes clarify the final automatic model selection process.
 The default for the MAXITER= option in the ESTIMATE statement has been changed to 1,500.
 The following tables have been added to the tables available in the X11 statement: B7, B13, B17, B20, C1, D8B, and E18.
COUNTREG Procedure
The following features have been added to the COUNTREG procedure:
 The following Bayesian analysis features have been added:
 harmonic mean evaluation of the marginal likelihood to compare competing models (this estimator does not require additional simulations after the posterior samples have been obtained)
 evaluation of the marginal likelihood using an importance sampling algorithm that is based on the crossentropy theory (this estimator requires additional importance sampling simulations after the posterior samples have been obtained)
 The TEST statement has been added.
 Spatial lag models have been added, enabling you to include spatial effects in a model. The SPATIALEFFECTS, SPATIALDISPEFFECTS, and SPATIALZEROEFFECTS statements have been added to allow for spatial effects in the MODEL, DISPMODEL, and ZEROMODEL statements, respectively. In addition, variable selection functionality has been added to the DISPMODEL, SPATIALEFFECTS, SPATIALDISPEFFECTS, and SPATIALZEROEFFECTS statements.
HPCOUNTREG Procedure
The following features have been added to the HPCOUNTREG procedure:
 TEST statement
 support for the ConwayMaxwell Poisson distribution
HPPANEL Procedure
The following features have been added to the HPPANEL procedure:
 the betweengroups estimator, which is equivalent to regression on crosssectional means (to obtain this estimator, specify the BTWNG option in the MODEL statement)
 the betweentimeperiods estimator, which is equivalent to regression on means at each time point (to obtain this estimator, specify the BTWNT option in the MODEL statement)
 pooled OLS regression, which is obtained by specifying the POOLED option in the MODEL statement
MODEL Procedure
The %EQAR and %EQMA macros have been added to the MODEL procedure. Model programs that are expressed using general form equations can now use the %EQAR macro to specify autoregressive error processes or the %EQMA macro to specify moving average error processes. The %EQAR and %EQMA macros modify general form equations similar to how the %AR and %MA macros modify normal form equations.
PANEL Procedure
The following features have been added to the PANEL procedure:
 The Hausman and Taylor and Amemiya and MaCurdy estimators, which are hybrids that combine the desirable properties of fixedeffects and randomeffects models. Under the right circumstances, these estimators afford you the consistency of fixed effects and the efficiency and wider applicability of random effects. Both estimators are instrumentalvariables regressions, where you stipulate a set of regressors as correlated with individual effects. The instrumental variables are then determined internally from the set of uncorrelated regressors, their individuallevel means, and their deviations from individuallevel means. To obtain these estimators, first specify the correlated regressors in the CORRELATED= option in the INSTRUMENTS statement, and then specify the HTAYLOR or AMACURDY option in the MODEL statement.
 Comparison tables for multiple models. The new COMPARE statement creates tables of sidebyside comparisons of parameter estimates and other model statistics. You can fit multiple models in the PANEL procedure by issuing multiple MODEL statements. Also specifying a COMPARE statement creates tables that compare the models. The COMPARE statement creates two tables: the first table compares model fit statistics such as R^{2} and MSE; the second table compares regression coefficients, their standard errors, and (optionally) t tests.
 More general Hausman specification tests. In previous versions, Hausman tests for random effects required that the randomeffects model contain no timeinvariant regressors (regressors that would be dropped from the fixedeffects model). That requirement has been relaxed in SAS/ETS 14.1, and the Hausman test is now a comparison of regressors that are common to both the random and fixedeffects models. A new column labeled "Coefficients" has been added to the output table for the Hausman test. The "Coefficients" column tells you how many coefficients are common to both models, and thus also tells you the nominal rank of the test.
QLIM Procedure
The following features have been added to the QLIM procedure:
 The following Bayesian analysis features have been added:
 harmonic mean evaluation of the marginal likelihood to compare competing models (this estimator does not require additional simulations after the posterior samples have been obtained)
 evaluation of the marginal likelihood using an importance sampling algorithm based on the crossentropy theory (this estimator requires additional importance sampling simulations after the posterior samples have been obtained)
 Bayesian analysis support for multivariate models in which the likelihood function is not available in closed form
 The RANDOM statement, which enables you to estimate the randomintercept models, has been added to PROC
QLIM. Any singleequation model in PROC QLIM can be expanded to a randomintercept singleequation model.
The RANDOM statement enables you to use panel data in your estimations, and it offers three methods to
integrate out the random intercept: GaussHermite quadrature, simulation, and quasi–Monte
Carlo using a Halton sequence.
The randomintercept models include the following:
 randomintercept linear regression models
 randomintercept discrete choice models, including binary probit, binary logit, ordinal probit, and ordinal logit models
 randomintercept limited dependent variable models, including censored regression and truncated regression models
 randomintercept stochastic frontier models
SASECRSP Interface Engine
If you install SAS/ETS on a Windows system, you no longer need to install the CRSPAccess API, because it is now distributed automatically during the installation. Before you run SASECRSP, your Windows setup requires that the CRSPDB_SASCAL environment variable be set to the path where your database calendar files reside.
SASEFRED Interface Engine
The following features have been added to the SASEFRED interface engine:
 Linux X64 (LAX) host support has been added.
 The PROXY= option specifies a proxy server and port number to use if the connection times out without a proxy server.
 The RTSTART= and RTEND= options support the realtime periods for FRED data. Because the default is today, it is important to support the range of first to last available (the complete realtime period), in addition to other ranges for which data were available.
 You can use the URL= option to request the following useful information about categories, tags, groups,
and releases:
 a list of the vintage dates (release dates) for a particular series that is specified in the IDLIST= option<
 a list of the available series for a particular release or for a specified source, tag name, or category ID
 a list of available sources for today's date
 a list of the categories available for a specified series ID
 Blanks are now allowed in pathnames that are used in SASEFRED options.
 The DEBUG=ON option logs diagnostics in the SAS log.
SASEXCCM Interface Engine
If you install SAS/ETS on a Windows system, you no longer need to install the CRSPAccess API, because it is now distributed automatically during the installation. Your Windows setup does not require any special environment variables.
SASEXFSD Interface Engine
The following features have been added to the SASEXFSD interface engine:
 Linux X64 (LAX) host support has been added.
 The PROXY= option specifies a proxy server and port number to use if the connection times out without a proxy server.
 The CONNECT= option specifies whether to connect using the secure HTTP address in the PROXY= specification to obtain a secure connection.
 Blanks are now allowed in pathnames that are used in SASEXFSD options.
 The UNIVERSE= option is now supported on the ExtractFormulaHistory factlet.
 The DEBUG=ON option logs diagnostics in the SAS log.
SASEQUAN Interface Engine
The following features have been added to the SASEQUAN interface engine:
 Linux X64 (LAX) host support has been added.
 The PROXY= option specifies a proxy server and port number to use if the connection times out without a proxy server.
 When a long variable name (more than 32 bytes) is truncated, the 32byte name might not be unique, so SASEQUAN appends the variable number to the name in order to create a unique variable name.
 Blanks are now allowed in pathnames that are used in SASEQUAN options.
 Up to nine Quandl codes are allowed in the IDLIST= option. SASEQUAN returns an error message when more than nine are specified.
 The DEBUG=ON option logs diagnostics in the SAS log.
SSM Procedure
The DEPLAG statement has been added to the SSM procedure. It simplifies the specification of models that have lagged values of response variables in the observation equation. The DEPLAG statement enables you to define a linear combination of lagged response variables, which can be subsequently used as a righthandside term in the MODEL statement. Models that include lagged response variables are permitted only if the data form a time series (either univariate or multivariate).
VARMAX Procedure
The following features have been added to the VARMAX procedure:
 Vector error correction models in ARMAGARCH form are supported. You can use the COINTEG statement together with the Q= option in the MODEL statement and the GARCH statement to model the cointegration relationship between multiple time series that have GARCHtype innovations.
 The linear equality and inequality constraints for any parameters to be estimated in vector error correction models are supported. You can use the BOUND and RESTRICT statement to study the restricted cointegrated systems.
 The covariance and standard errors of the parameter estimates of the adjustment coefficient matrix and the covariance matrix of innovations in vector error correction models are supported. The outputs of parameter estimates of the longrun parameters and the error correction trend parameters are also supported.
 You can apply the Wald tests, by using the TEST statement, on any parameters in vector correction models except the longrun parameters and the error correction trend parameters.
 You can specify initial values, by using the INITIAL statement, for any parameters to be estimated in vector error correction models. If you do so, you must specify the fullrank initial matrices for both the adjustment coefficient matrix and the longrun parameters.

A new estimation method, the conditional maximum likelihood method (CML), is supported. This method is especially suitable for estimating VARMAX models on large samples.

The log likelihoods for all types of models are output. These outputs are especially useful if you need to execute the likelihood ratio (LR) tests.
 Definitions have been revised for the following information criteria: Akaike's information criterion (AIC), the corrected Akaike's information criterion (AICC), the HannanQuinn criterion (HQC), and the Schwarz Bayesian criterion (SBC, also referred to as BIC). You can compare more types of models, including all forms of multivariate GARCH models.
 The ECTREND option is supported in the COINTEG statement. All options in the ECM= option in the MODEL statement are now supported in the COINTEG statement, and the ECM= option becomes obsolete. Starting with SAS/ETS 14.1, it is recommended that you use the COINTEG statement instead of the ECM= option to fit vector error correction models.
 The new NLC option, is supported in the COINTEG statement. This option enables you to explicitly require that the adjustment coefficient matrix and the longrun parameters both be full rank when you numerically maximize the likelihood of a vector error correction model.
 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.