SAS® Econometrics Features
Hidden Markov models
- 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 number of states, number of lags.
- Provides regime-switching autoregressive model in mean-adjusted form.
- Provides stochastic gradient descent (SGD) optimization algorithm for all models.
Spatial econometrics modeling
- 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.
Other econometric models
Count regression models for integer-valued dependent variables
- CNTSELECT procedure models the frequency with which events may occur during a time period.
- 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.
- Regression models for severity distribution scale parameter.
- Left censoring and right truncation (e.g., deductibles and coverage limits).
- Many distributions, including:
- Generalized Pareto.
- 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.
- 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.
- Simulates copula models of the multivariate dependency structure among sets of potentially many variables.
- Supports simulations from the following copulas:
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.
- 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
- Graphical summary of aggregate loss distribution from large, distributed simulated samples.
- Simulation modes.
- Flexible way of specifying count distributions.
- More realistic simulations of loss modeling using stochastic variables.
- Perturbation analysis for estimating mean and variability of aggregate loss distribution statistics.
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.
Forecasting models for time series analysis
- 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, multi-step variable forecasts and modeled variable information.
Distributed, open and cloud-ready
- Runs on SAS Viya, a scalable, distributed in-memory engine of the SAS Platform.
- 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.
SASEMOOD Interface Engine
- 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.