What’s New in SAS/ETS 9.3

Overview

This chapter summarizes the new features available in SAS/ETS 9.3.
If you have used SAS/ETS procedures in the past, you can review this chapter to learn about the new features that have been added. When you see a new feature that might be useful for your work, see the appropriate chapter in the SAS/ETS User's Guide to read about the feature in detail.

Highlights of Changes and Enhancements

The following new procedures have been added to SAS/ETS software:
  • COPULA procedure (experimental)
  • SSM procedure (experimental)
  • SASEXCCM interface engine (experimental)
New features have been added to the following SAS/ETS components:
  • AUTOREG procedure
  • ESM procedure
  • PANEL procedure
  • SASEFAME interface engine
  • SASEHAVR interface engine
  • SASECRSP interface engine
  • SEVERITY procedure
  • TCOUNTREG procedure
  • X12 procedure
The SAS/ETS Model Editor application, provided with SAS/ETS 9.22 as an experimental interactive graphical user interface for the MODEL procedure, is deprecated and no longer documented in the SAS/ETS User's Guide.

Highlights of Enhancements in SAS/ETS 9.22

Users who are updating directly to SAS/ETS 9.3 from a release prior to SAS/ETS 9.22 can find information about the SAS/ETS 9.22 changes and enhancements in the chapter "What’s New in SAS/ETS for SAS 9.22" in the SAS/ETS 9.22 User’s Guide (see http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/whatsnew_toc.htm).

AUTOREG Procedure

The AUTOREG procedure now supports heteroscedasticity consistent covariance matrix estimators (HCCME), which consistently estimate the covariance matrix even when the heteroscedasticity structure might be unknown or misspecified. Five forms of HCCMEs are supported: the plain sandwich form (HC0), the degrees-of-freedom-adjustment form (HC1), two types of leverage-adjustment forms (HC2 and HC3), and the high-leverage-adjustment form (HC4).

COPULA Procedure (Experimental)

The new experimental COPULA procedure enables you to simulate realizations or estimate parameters of multivariate distributions by using the copula approach. This approach is based on the fact that a typical multivariate distribution contains information about both the marginal behavior of individual random variables and also about the dependence structure between them. The COPULA procedure enables you to decouple these two effects and model the dependence structure of random variables by linking their cumulative distribution function (CDF) to a vector of their marginal CDFs as described by the Sklar’s Theorem.
The COPULA procedure supports the following types of distributions:
  • normal distribution
  • t distribution
  • Clayton distribution
  • Gumbel distribution
  • Frank distribution
The COPULA procedure can both estimate the parameters of copula models from data by using maximum likelihood and simulate random data from copula distributions by using either estimated or specified model parameters. The FIT statement is used for model estimation, and the SIMULATE statement is used for simulation. The PLOTS option in the FIT or SIMULATE statement provides various ODS Graphics plots that help you analyze the underlying data.

ESM Procedure

New ODS plots and plot options are available for the ESM procedure. You can plot the periodogram for the error series or a combined pediodogram and spectral density estimate plot.

SAS/ETS Model Editor Application (Experimental)

An experimental version of a new interactive application, the SAS/ETS Model Editor, was introduced with SAS/ETS 9.22. The SAS/ETS Model Editor enables you to use the powerful features of PROC MODEL through an interactive graphical user interface.
Based on experience with the experimental version in SAS/ETS 9.22, plans for GUI features to enable easier use of the MODEL procedure are being reconsidered. This experimental SAS/ETS Model Editor application is still available with SAS/ETS 9.3. However, because design changes are anticipated, documentation for this application is not included in the SAS/ETS 9.3 User’s Guide. Please refer to the SAS/ETS 9.22 User’s Guide if you want to use the experimental version of the SAS/ETS Model Editor.

PANEL Procedure

The heteroscedasticity consistent covariance matrix estimator (HCCME) was enhanced by adding the CLUSTER option for the plain sandwich form (HC0), the degrees-of-freedom-adjusted form (HC1), and two types of leverage-adjusted estimators (HC2 and HC3). The CLUSTER option enables you to calculate a cluster-corrected covariance matrix and provides cluster-adjusted standard errors for parameter estimates.

SASECRSP Engine

The SASECRSP interface engine enables you to access and process time series, events, portfolios, and group data that reside in CRSPAccess (2.99 and earlier) legacy databases.
It also provides a seamless interface between CRSP, COMPUSTAT, and SAS data processing. Currently, SASECRSP supports access of CRSP Stock databases, CRSP Indices databases, and CRSP/Compustat Merged databases.
The following enhancement has been made to the SASECRSP access engine:
  • Support has been added for Solaris (SUNOS5.10).

SASEFAME Engine

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 following enhancements have been made to the SASEFAME access engine for Fame databases:
  • Support has been added for 64-bit Windows.
  • Support has been added for AIX.
  • The SASEFAME interface uses FAME 10.

SASEHAVR Engine

The SASEHAVR interface engine is a seamless interface between Haver and SAS data processing that enables you to read economic and financial time series data that reside in a Haver Analytics DLX (Data Link Express) database. The following enhancements have been made to the SASEHAVR access engine for Haver Analytics databases:
  • Support has been added for 64-bit Windows.

SASEXCCM Engine (Experimental)

The new experimental SASEXCCM interface engine enables you to access the CRSP/Compustat Merged Database (CCM), created from data delivered via Compustat’s Xpressfeed product, and the CRSP Stock, Indices, and Treasury Databases. SASEXCCM provides a seamless interface for CRSP, Compustat, and SAS data processing. The following new features are provided by the SASEXCCM interface:
  • SETID= option supports item handling data access to CRSPAccess (300 and up) databases with the designated set identifier, including setid=250.
  • PERMNO= option enables you to select based on permno, a primary keytype for CRSP Stock data.
  • PERMCO= option enables you to select based on permco, a keytype for CRSP data.
  • CUSIP= option enables you to select based on cusip, a keytype for CRSP data.
  • HCUSIP= option enables you to select based on historical cusip, a keytype for CRSP data.
  • SICCD= option enables you to select based on siccd, a keytype for CRSP data.
  • TICKER= option enables you to select based on ticker, a keytype for CRSP data.
  • GVKEY= option enables you to select based on gvkey, a primary keytype for COMPUSTAT data.
  • INDNO= option enables you to select based on indno, a primary keytype for CRSP Indices data.
  • ITEMLIST= option specifies the data items to be selected for access. This option accepts a string in standard CRSP notation.

SEVERITY Procedure

The SEVERITY procedure was experimental in SAS/ETS 9.22. PROC SEVERITY is now production status. The following new features and updates have been added to the SEVERITY procedure:
  • The following updates have been made to the syntax:
    • The MODEL statement is now replaced with LOSS and SCALEMODEL statements. The LOSS statement specifies the response variable along with any censoring and truncation information. The SCALEMODEL statement specifies the regressor variables. The model-fitting options that were specified in the MODEL statement in SAS/ETS 9.22 should now be specified in the PROC SEVERITY statement.
    • You can now specify multiple distributions in one DIST statement. You can also use a keyword to specify a group of distributions. The syntax for specifying initial parameter values of a distribution has also been updated. If you do not specify a DIST statement, then PROC SEVERITY produces only the empirical CDF estimates and does not fit all predefined distributions by default.
  • You can specify the number of occurrences for each observation by using the new FREQ statement.
  • You can specify left-censoring and right-truncation by using the new LEFTCENSORED= and RIGHTTRUNCATED= options in the LOSS statement.
    The method of specifying censoring has been updated. Instead of using the indicator variable, you now specify censoring by using a variable that contains the censoring limit. This enables you to specify interval-censored data; that is, data in which observations are both right-censored and left-censored.
    For interval-censored data, PROC SEVERITY uses Turnbull’s method to estimate the empirical distribution function (EDF). Implementation of Turnbull’s EDF estimation method is an experimental feature in SAS/ETS 9.3.
  • Two predefined versions of Tweedie distributions, TWEEDIE and STWEEDIE, can be fitted with PROC SEVERITY. The TWEEDIE distribution has the more popular parameterization with mean, dispersion, and index parameters. The STWEEDIE distribution has an alternative parameterization with scale, Poisson mean, and index parameters. The STWEEDIE distribution can be used for analyzing regression effects.
  • You can estimate parameters by minimizing your own objective function, which can be specified using SAS programming statements. You can use various keyword functions in your SAS program, which are internally expanded by PROC SEVERITY with distribution-specific or problem-specific versions.
    Note: This is an experimental feature in SAS/ETS 9.3.
  • You can now compute quantile and limited moment for any distribution fitted with PROC SEVERITY by using the two new functions, INVCDF and LIMMOMENT, respectively. These functions are accessible in a PROC FCMP step.

SSM Procedure (Experimental)

The new experimental SSM procedure enables linear state space modeling of time series and longitudinal data. An important feature of the SSM procedure is a modeling language that permits easy specification of possibly complex state space models. In particular, the system matrices, such as the state transition matrix and the covariance of the state disturbance, can be time-varying and their elements can depend on user-specified parameters in a complex way. Often a state space model can be specified by combining simpler submodels. This modeling language is especially suited for specification of such models. The following list identifies the key features of the SSM procedure:
  • Many commonly needed state space models, such as the basic univariate and multivariate structural time series models, can be easily specified using a few keywords. Similarly, models for panel data can also be easily specified.
  • The unknown model parameters are estimated by (restricted) maximum likelihood and a variety of likelihood-based information criteria are reported for model diagnostics.
  • One-step-ahead and full-sample estimates of various state effects (linear combinations of the underlying state vector) and one-step-ahead residuals can be output to a data set. In particular, model-based forecasts, backcasts, interpolated missing values of the response variables, and the estimates of the latent effects such as trend, cycles, and seasonals, can be output to a data set. These estimates are generated by using the Kalman filtering and smoothing algorithm.
  • State space modeling is commonly used for the analysis of regularly spaced univariate and multivariate time series data. In fact, state space modeling is quite useful for irregularly spaced, possibly with replicate measurements, longitudinal data also. An important feature of the SSM procedure is that it enables analysis of such longitudinal data, in addition to the regularly spaced univariate and multivariate time series data. Several trend models suitable for longitudinal data analysis can be easily specified using a few keywords.

TCOUNTREG Procedure (Experimental)

The new experimental TCOUNTREG procedure is an transitional version of the COUNTREG procedure. It includes all features of the COUNTREG procedure. In addition to features implemented in the COUNTREG procedure, PROC TCOUNTREG provides the following new features:
  • Two new variable selection methods are provided. The greedy search method can be used either with the forward or backward selection. In each step, the AIC or BIC criterion is evaluated, and the selection continues until the selection criterion is met. The second method uses the penalized likelihood approach to select significant variables. This method is not path-dependent as in the case of greedy search, which falls into the family of LASSO estimators. Using the penalized likelihood method, PROC TCOUNTREG fits a model to the set of all candidate variables and evaluates it simultaneously to find a subset of best-fitting variables.
  • Several conditional (fixed- and random-effect) count panel data models have been added to the TCOUNTREG procedure. The unconditional panel fixed-effect models can be easily estimated in the TCOUNTREG procedure by using the CLASS statement and the dummy variable approach. This technique is relatively simple but is suitable only for a model with small number of cross sections. If the number of cross sections is large, a conditional model is typically preferred to overcome the incidental parameters problem. The TCOUNTREG procedure enables you to estimate the following types of models:
    • Poisson regression model with fixed and random effects
    • negative binomial regression model with fixed and random effects

X12 Procedure

The following new features have been added to the X12 procedure:
  • The PLOTS option in the PROC X12 statement now includes forecast plots. You can now request four different plots for the forecast series on the original scale, and if the series is transformed, on the transformed scale. The following values can be specified in PLOTS=FORECAST(value-list):
    FORECAST
    plots the actual time series and its one-step-ahead forecasts over the historical period, and plots the forecast and its confidence bands over the forecast horizon.
    FORECASTONLY
    plots the forecast and its confidence bands over the forecast horizon only.
    MODELS
    plots the one-step-ahead model forecast and its confidence bands in the historical period.
    MODELFORECASTS
    plots the one-step-ahead model forecast and its confidence bands in the historical period, and plots the forecast and its confidence bands over the forecast horizon.
    TRANSFORECAST
    plots the transformed time series and its one-step-ahead forecast over the historical period, and plots the forecast and its confidence bands over the forecast horizon.
    TRANSFORECASTONLY
    plots the forecast of the transformed series and its confidence bands over the forecast horizon only.
    TRANSMODELS
    plots the one-step-ahead model forecast of the transformed series and its confidence bands in the historical period.
    TRANSMODELFORECASTS
    plots the one-step-ahead model forecast of the transformed series and its confidence bands in the historical period, and plots the forecast and its confidence bands over the forecast horizon.
  • The following new values are available in the PRINT= option in the AUTOMDL statement:
    ALL
    specifies that all automatic modeling output be displayed.
    NONE
    suppresses all display of automatic modeling output.
    ONLY
    specifies that only the requested automatic modeling tables be displayed.
  • The following new options are available in the FORECAST statement:
    NBACKCAST=
    specifies the number of periods to backcast for regARIMA extension of the series. Backcasting has been shown to improve seasonal adjustment for short series.
    OUT1STEP
    specifies that the one-step-ahead forecasts be computed and displayed in addition to the multistep forecasts. The one-step-ahead forecasts and associated statistics are useful in evaluating the ARIMA model.
    OUTBACKCAST
    includes backcasts in certain tables that are sent to the output data set.
    OUTFORECAST
    includes forecasts in certain tables that are sent to the output data set. This option is an alias of the OUTFORECAST option in the X11 statement.
  • The FINAL=USER option in the X11 statement specifies that user-defined regressors are to be removed from the final seasonally adjusted series.
  • The YEARSEAS option in the OUTPUT statement specifies that variables containing values for year and season are included in the OUT= data set. These values are useful when creating seasonal plots.
  • An auxiliary variable has been added to forecast data sets that are available through ODS OUTPUT. The variable _SCALE_ indicates whether the observation refers to the original series, "Original," or the transformed series, "Transformed." The variable helps you subset the output when the series is transformed.
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