SAS/ETS
Title | Level | Training Formats |
---|---|---|
Introduction to Applied Econometrics ![]() This course, the first of two, focuses on the development and use of single-equation econometric models that enable analysts to better understand their economic/business landscape and to improve their ability to make sound economic/business forecasts. Through hands-on exercises, participants gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills of participants in dealing with problems and issues related to business and economics as well as to improve communication skills in reporting findings to decision makers. To learn more intermediate/advanced topics, consider registering for the Advanced Topics in Applied Econometrics course. |
2 Fundamental | ![]() ![]() ![]() ![]() ![]() |
Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM ![]() |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Advanced Topics in Applied Econometrics ![]() This sequel to Introduction to Applied Econometrics focuses on intermediate and advanced topics in working with econometric models. This course enables analysts to better understand their economic/business landscape and to improve their ability to make sound forecasts. Through applications, participants gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills of participants in dealing with problems and issues related to business and economics as well as to improve communication skills in reporting findings to decision makers. |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Electric Load Forecasting: Fundamentals and Best Practices ![]() |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models ![]() This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run? |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Stationarity Testing and Other Time Series Topics ![]() This course addresses a basic question in time series modeling and forecasting: whether a time series is nonstationary. This question is addressed by the unit root tests. One of the most common tests, the Dickey-Fuller test, is discussed in this lecture. |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Electric Load Forecasting: Advanced Topics and Case Studies ![]() |
4 Expert | ![]() ![]() ![]() ![]() ![]() |
FDP - Shaping an Advanced Analytics Curriculum
The course teaches you fundamental concepts and relevant techniques in statistical and analytical domains that are relevant in today's world. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy. |
0 No level | ![]() ![]() ![]() ![]() ![]() |
Business Forecasting Using SAS: A Point-and-Click Approach
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2 Fundamental | ![]() ![]() ![]() ![]() ![]() |
Using SAS Forecast Server Procedures
This course teaches you how to create and manage a complete forecasting system using the SAS Forecast Server procedures, giving you the power to confidently plan your business operations. |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Statistics 2: ANOVA and Regression
This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course. |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
Time Series Modeling Essentials
This course discusses the fundamentals of modeling time series data. The course focuses on the applied use of the three main model types used to analyze univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM). The e-learning format of this course includes Virtual Lab time to practice. |
3 Intermediate | ![]() ![]() ![]() ![]() ![]() |
State Space Modeling Essentials Using the SSM Procedure in SAS/ETS ![]() This course covers the fundamentals of building and applying state space models using the SSM procedure (SAS/ETS). Students are presented with an overview of the model and learn advantages of the State Space approach. The course also describes fundamental model details, presents some straightforward examples of specifying and fitting models using the SSM procedure, and considers estimation in SSM, focusing on the Kalman filter and related details. The course concludes with a variety of SSM modeling applications, focused mainly on time series. |
4 Expert | ![]() ![]() ![]() ![]() ![]() |
Forecasting Using SAS Software: A Programming Approach ![]() This course teaches analysts how to use SAS/ETS software to diagnose systematic variation in data collected over time, create forecast models to capture the systematic variation, evaluate a given forecast model for goodness-of-fit and accuracy, and forecast future values using the model. Topics include Box-Jenkins ARIMA models, dynamic regression models, and exponential smoothing models. |
4 Expert | ![]() ![]() ![]() ![]() ![]() |