SAS Econometrics Features List

• Fits and applies hidden Markov models to time series data.
• Does fitting (or learning), smoothing, filtering, decoding and scoring.
• Supports univariate or multivariate models, regime-switching regression models and regime-switching autoregression models.
• Supports discrete state Gaussian models.
• Provides methods for efficiently handling very long (big data) series.
• Automates selection of the number of states and the number of lags.
• Provides regime-switching autoregressive model in mean-adjusted form.
• Provides stochastic gradient descent (SGD) optimization algorithm for all models.

• Supports the following:
  • Linear models.
  • Linear models with spatial lag of X (SLX) effects.
  • Spatial autoregressive (SAR) models.
  • Spatial Durbin models (SDM).
  • Spatial error models (SEM).
  • Spatial Durbin error models (SDEM).
  • Spatial moving average (SMA) models.
  • Spatial Durbin moving average (SDMA) models.
  • Spatial autoregressive moving average (SARMA) models.
  • Spatial Durbin autoregressive moving average (SDARMA) models.
  • Spatial autoregressive confused (SAC) models.
  • Spatial Durbin autoregressive confused (SDAC) models.
• Provides output tables that enable you to fully understand and interpret the impact of individual variables in the model.

Count regression models for integer-valued dependent variables

• CNTSELECT procedure models the frequency with which events may occur during a time period.
• Supports:
  • Poisson, negative binomial and Conway-Maxwell-Poisson (CMP) regression.
  • Zero-inflation models conditional on covariates.
  • Overdispersion models conditional on covariates (with CMP model).
  • Random-effect panel data models for counts.
  • Spatial count data models.
  • Bayesian estimation.
• Provides automated variable selection methods.
• Includes many diagnostic tests and plots, including plots for focused visualization of specific parts of the fitted probability distribution.
• Display tables to assess covariance and correlation among estimated model parameters.

Severity regression models

• Fits the distribution to the size or severity of losses or other events.
• Supports:
  • Regression models for severity distribution scale parameter.
  • Left censoring and right truncation (e.g., deductibles and coverage limits).
  • Many distributions, including:
    • Burr.
    • Exponential.
    • Gamma.
    • Generalized Pareto.
    • Wald.
    • Log-normal.
    • Tweedie.
    • Weibull.
• Provides the ability to program additional distributions.
• Fits multiple distributions and automatically selects the best.
• Provides many diagnostic tests and plots, including plots for focused visualization of specific parts of the fitted probability distribution.
• Includes display tables for assessing covariance and correlation among estimated model parameters.

Qualitative and limited-dependent variable regression models

• CQLIM procedure estimates regression models for univariate qualitative and limited-dependent variables.
• Supports:
  • Censored and truncated models.
  • Logit, probit and tobit models, and bivariate probit and tobit models.
  • Models with heteroscedasticity.
  • Univariate limited-dependent variables models.
  • Bivariate and multivariate limited dependent variable models.
  • Bivariate and multivariate discrete response variable models.
  • Multivariate linear equation models.
• Estimates stochastic frontier production and cost models.
• Heckman sample selection model.

Copula models

• Simulates copula models of the multivariate dependency structure among sets of potentially many variables.
• Supports simulations from the following copulas:
  • Normal.
  • t.
  • Clayton.
  • Gumbel.
  • Frank.

Regression models for panel data

• Analyzes relationships between the past and the future using a large number of observations and more than one observation per time period.
• Supports:
  • One-way and two-way models.
  • Fixed-effects, random-effects and hybrid models.
  • Autoregressive and moving average models.
  • Dynamic panel models.
• Provides Hausman-Taylor and Amemiya-MaCurdy estimators.
• Provides different kinds of heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators.
• Fits and compares multiple models.
• Includes many diagnostics and tests.

Economic capital modeling

• Combines results from frequency, severity and copula modeling.

Compound distribution modeling

• Provides a graphical summary of aggregate loss distribution from large, distributed simulated samples.
• Includes simulation modes.
• Provides a flexible way to specify count distributions.
• Enables more realistic simulations of loss modeling using stochastic variables.
• Enables perturbation analysis for estimating mean and variability of aggregate loss distribution statistics.

• Enables you to programmatically create forecasting models on time series data.
• Lets you create time series models:
  • User-defined ARIMA.
  • Exponential smoothing models (ESM).
• Lets you create time series analysis, decomposition models and diagnostic testing.
• Provides output tables with parameter estimates of fitted models, multistep variable forecasts and modeled variable information.

• Runs on the SAS Viya platform, a scalable, distributed in-memory engine.
• Distributes analysis and data tasks across multiple computing nodes.
• Provides fast, concurrent, multiuser access to data in memory.
• Includes fault tolerance for high availability.
• Lets you add the power of SAS Analytics to other applications using SAS Viya REST APIs.

• Retrieve time series data from the Moody's Analytics Data Buffet. Access over 600 sources of global historical statistical data and 40 forecast databases – over 220 million time series.