Advanced Forecasting Models with Excel

Learn advanced forecasting models through a practical course with Microsoft Excel® using S&P 500® Index ETF prices historical data. It explores main concepts from proficient to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your advanced investment management or sales forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field.

Become an Advanced Forecasting Models Expert in this Practical Course with Excel

• Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary deterministic test and Phillips-Perron unit root test.

• Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.

• Estimate autoregressive integrated moving average models such as random walk with drift and differentiated first order autoregressive.

• Identify seasonal autoregressive integrated moving average model seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test.

• Estimate seasonal autoregressive integrated moving average models such as seasonal random walk with drift and seasonally differentiated first order autoregressive.

• Select non-seasonal or seasonal autoregressive integrated moving average model with lowest Akaike, corrected Akaike and Schwarz Bayesian information loss criteria.

• Evaluate autoregressive integrated moving average models forecasting accuracy through mean absolute error, root mean squared error scale-dependent and mean absolute percentage error, mean absolute scaled error scale-independent metrics.

• Identify generalized autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Ljung-Box lagged autocorrelation test.

• Recognize non-Gaussian generalized autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test.

• Estimate autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or Student’s t distributed and with Bollerslev simple or Glosten-Jagannathan-Runkle threshold generalized autoregressive conditional heteroscedasticity effects such as random walk with drift and differentiated first order autoregressive.

• Assess autoregressive integrated moving average model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement.

Become an Advanced Forecasting Models Expert and Put Your Knowledge in Practice

Learning advanced forecasting models is indispensable for finance careers in areas such as portfolio management and risk management. It is also essential for academic careers in advanced applied statistics, econometrics and quantitative finance. And it’s necessary for advanced sales forecasting research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for advanced forecast modelling to achieve greater effectiveness.

Content and Overview

This practical course contains 43 lectures and 8 hours of content. It’s designed for advanced forecasting models knowledge level and a basic understanding of Microsoft Excel® is useful but not required.

At first, you’ll learn how to perform advanced forecasting models operations using built-in functions and array calculations. Next, you’ll learn how to do optimal parameter estimation or fine tuning and linear regression calculation using Microsoft Excel® Add-ins.

Then, you’ll define Box-Jenkins autoregressive integrated moving average models. Next, you’ll identify autoregressive integrated moving average models integration order through level and differentiated time series first order trend stationary deterministic test and Phillips-Perron unit root test. After that, you’ll identify autoregressive integrated moving average models autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. For autoregressive integrated moving average models, you’ll define random walk with drift and differentiated first order autoregressive models. Later, you’ll define seasonal autoregressive integrated moving average models. Then, you’ll identify seasonal autoregressive integrated moving average models seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test. Next, you’ll identify seasonal autoregressive integrated moving average models seasonal autoregressive and seasonal moving average orders through autocorrelation and partial autocorrelation functions. For seasonal autoregressive integrated moving average models, you’ll define seasonal random walk with drift and seasonally differentiated first order autoregressive. After that, you’ll select non-seasonal or seasonal autoregressive integrated moving average model with lowest information loss criteria. For information loss criteria, you’ll define Akaike, corrected Akaike and Schwarz Bayesian information criteria. Later, you’ll evaluate autoregressive integrated moving average models forecasting accuracy through scale-dependent and scale-independent error metrics. For scale-dependent metrics, you’ll define mean absolute error and root mean squared error. For scale-independent metrics, you’ll define mean absolute percentage error and mean absolute scaled error.

Next, you’ll define generalized autoregressive conditional heteroscedasticity models. Then, you’ll identify generalized autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Ljung-Box lagged autocorrelation test. After that, you’ll identify generalized autoregressive conditional heteroscedasticity model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions. Later, you’ll define autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or normally distributed and with Bollerslev simple or Glosten-Jagannathan-Runkle threshold generalized autoregressive conditional heteroscedasticity effects. For generalized autoregressive conditional heteroscedasticity models, you’ll define random walk with drift and differentiated first order autoregressive. Then, you’ll evaluate generalized autoregressive conditional heteroscedasticity models forecasting accuracy.

After that, you’ll define non-Gaussian generalized autoregressive conditional heteroscedasticity models. Next, you’ll identify non-Gaussian generalized autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test. Then, you’ll define autoregressive integrated moving average models with residuals or forecasting errors assumed as Student’s t distributed and with Bollerslev simple or Glosten-Jagannathan-Runkle threshold generalized autoregressive conditional heteroscedasticity effects. Later, you’ll evaluate non-Gaussian generalized autoregressive conditional heteroscedasticity models forecasting accuracy. Finally, you’ll evaluate autoregressive integrated moving average and non-Gaussian generalized autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement.