SAS/ETS® Programming Training

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SAS/ETS® (Econometrics and Time Series) training imparts the knowledge and skills to work on the models and techniques used for understanding the data and predicting the future based on the analysis carried over it. The business analyst can gain insight in complex data and provide valuable input regarding the marketing activities, price decision, customer demographics and other. Intensive training will develop the proficiency of providing time series forecasting and time series analysis that can help the management in drafting more effective strategic planning.

By the end of SAS/ETS® classes you will inculcate the following skill set:

  • Illustrate the features and functions available in SAS/ETS®
  • Demonstrate the use of different models used for time series
  • Recommend suggestions based on Autoregressive Integrated Moving Average (ARIMA) model
  • Find autoregressive errors
  • Elucidate State Space modeling
  • Work on periodic data, harmonic frequencies, spectral density for spectral analysis
  • Apply Scorecard, Forecasting data model, Goal-Seeking model for data mining and forecasting
Target audience
  • MBA graduates
  • Academicians
  • PhD scholars
  • Survey researcher
  • Market research
  • Banking operations professionals
  • IT professionals/consultants
  • Risk analyst
  • Forecaster
  • Understanding of the statistical concepts and Econometrics
  • Familiarity with regression analysis
  • Knowledge of Base SAS or at least two years of working experience in SAS environment

1. Overview of Time Series

  • Introduction
  • Analysis Methods and SAS/ETS Software
    • Options
    • How SAS/ETS Software Procedures Interrelate
  • Simple Models: Regression
    • Linear Regression
    • Highly Regular Seasonality
    • Regression with Transformed Data

2.  Simple Models: Autoregression

  • Introduction
    • Terminology and Notation
    •  Statistical Background
  • Forecasting
    • Forecasting with PROC ARIMA
    • Backshift Notation B for Time Series
    • Yule-Walker Equations for Covariances
  •  Fitting an AR Model in PROC REG

3.  General ARIMA Model

  • Introduction
    • Statistical Background
    • Terminology and Notation
  • Prediction
    • One-Step-Ahead Predictions
    • Future Predictions
  • Model Identification
    • Stationarity and Invertibility
    • Time Series Identification
    • Chi-Squared Check of Residuals
    • Summary of Model Identification
  • Examples and Instructions
    •  IDENTIFY Statement for Series
    • Example: Iron and Steel Export Analysis
    • Estimation Methods Used in PROC ARIMA
    • ESTIMATE Statement for Series
    • Nonstationary Series
    • Effect of Differencing on Forecasts
    • Examples: Forecasting IBM Series and Silver Series
    • Models for Nonstationary Data
    • Differencing to Remove a Linear Trend
    • Other Identification Techniques

4.  ARIMA Model: Introductory Applications

  • Seasonal Time Series
    • Introduction to Seasonal Modeling
    • Model Identification
  • Models with Explanatory Variables
    • Case 1: Regression with Time Series Errors
    • Case 1A: Intervention
    • Case 2: Simple Transfer Function
    • Case 3: General Transfer Function
    • Case 3A: Leading Indicators
    • Case 3B: Intervention
  • Methodology and Example
    • Case 1: Regression with Time Series Errors
    • Case 2: Simple Transfer Functions
    • Case 3: General Transfer Functions
    • Case 3B: Intervention
  • Further Examples
    • North Carolina Retail Sales
    • Construction Series Revisited
    • Milk Scare (Intervention)
    • Terrorist Attack

5.  ARIMA Model: Special Applications

  • Regression with Time Series Errors and Unequal Variances
    • Autoregressive Errors
    • Example: Energy Demand at a University
    • Unequal Variances
    • ARCH, GARCH, and IGARCH for Unequal Variances
  • Cointegration
    • Introduction
    • Cointegration and Eigenvalues
    • Impulse Response Function
    • Roots in Higher-Order Models
    • Cointegration and Unit Roots
    • An Illustrative Example
    • Estimating the Cointegrating Vector
    • Intercepts and More Lags
    • Interpreting the Estimates
    • Diagnostics and Forecasts

6.  State Space Modeling

  • Introduction
    • Some Simple Univariate Examples
      • A Simple Multivariate Example
    • Equivalence of Statespace and Vector ARMA Models
  • More Examples
    • Some Univariate Examples
    • ARMA (1, 1) of Dimension
    • State Vectors Determined from Covariances
    • Canonical Correlations
    • Simulated Example

7.  Spectral Analysis

  • Periodic Data: Introduction
  • Example: Plant Enzyme Activity
  • PROC SPECTRA Introduced
  • Testing for White Noise
  • Harmonic Frequencies
  • Extremely Fast Fluctuations and Aliasing
  • The Spectral Density
  • Some Mathematical Detail (Optional Reading)
  • Estimating the Spectrum: The Smoothed Periodogram
  • Cross Spectral Analysis
    • Interpreting Cross-Spectral Quantities
    • Interpreting Cross-Amplitude and Phase Spectra
    • PROC SPECTRA Statements
    • Cross-Spectral Analysis of the Neuse River Data
    • Details on Gain, Phase, and Pure Delay

8.  Data Mining and Forecasting

  • Introduction
  • Forecasting Data Model
  • Time Series Forecasting System
  • HPF Procedure
  • Scorecard Development
  • Business Goal Performance Metrics
  • Graphical Displays
  • Goal-Seeking Model Development

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