Advanced Forecasting Models with Excel

Last Update: April, 2019

1. Course Objective

Learn Advanced Forecasting Models main topics using Microsoft Excel® in this practical course for expert knowledge level. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Identify Box-Jenkins Non-Seasonal and Seasonal Auto Regressive Integrated Moving Average ARIMA and SARIMA models integration order (first order trend stationary deterministic test, Phillips-Perron unit root test), seasonal integration order (first order seasonal stationary deterministic test), non-seasonal, seasonal autoregressive and moving average orders (normal and partial autocorrelation functions charts).
  • Estimate ARIMA and SARIMA models (non-seasonal, seasonal random walk with drift and differentiated first order autoregressive).
  • Recognize Generalized Auto Regressive Conditional Heteroscedasticity GARCH modeling need (second order stationary Ljung-Box autocorrelation test) and non-Gaussian GARCH modeling need (multiple order stationary Jarque-Bera normality test).
  • Approximate ARIMA models with residuals assumed as Gaussian or Student’s t distributed and with Bollerslev simple or Glosten-Jagannathan-Runkle threshold GARCH effects (random walk with drift, differentiated first order autoregressive).
  • Select methods or models (Akaike, corrected Akaike and Schwarz Bayesian information loss criteria).
  • Evaluate methods or models forecasting accuracy (mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error).
  • Assess model standardized residuals strong white noise requirement (Ljung-Box autocorrelation test, Jarque-Bera normality test).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Academic researcher who wishes to deepen your knowledge in advanced applied statistics, econometrics or quantitative finance.
  • Experienced finance professional or business data scientist who desires to apply this knowledge in advanced investment management research or sales forecasting.