Wednesday, December 23, 2020

"How To Assess Which Model Will Make The Best Forecast"

Jourdan Dunn, Miranda Kerr and Aurélie Claudel have shown some of what the model makers refer to as forecast skill.

From the University of Chicago's Booth School of Business, ChicagoBoothReview, December 22:

Video Transcript

There is no single perfect model for forecasting GDP, or a company’s earnings. A model may work well sometimes, but other times be wildly inaccurate, like during a financial crisis, when changing interest rates and government actions can alter the economic landscape. 

According to University of Heidelberg postdoctoral scholar Stefan Richter and Chicago Booth’s Ekaterina Smetanina, testing for the best model assumes there actually is an always-best model. They’ve put together a framework for analyzing how forecasting models perform as conditions change, which allows for the possibility that the performance of a model, even the best ones, will change over time. 

Imagine that two race cars, each representing a forecasting model, are set loose on a track. The length of that track represents the data that are available. If the track were infinitely long and perfectly uniform, the winner would ultimately become very clear. But when you’re talking about forecasting something using real data, such as quarterly GDP figures, the data are limited in length and the model’s performance may change as the financial and economic landscape changes.

We need to decide how much data to use for estimation, how much room those cars have to reach their top speeds, what obstacles they encounter that may cause them to overtake each other a lot. Then the real question is: How do we determine which car is fastest overall? 

The method used by most researchers is to take much of the historical data available to build parameters for the model and to verify the model using a smaller, more recent data set....


Those models are best appreciated by hindcasts.