Forecasting During The Great Recession

Tyler Cowen (here) has argued that since we do not have models that describe the current economic crisis, we really won't be able to forecast when the economy will recover (even though many forecasters are currently trying). Cowen makes a reasonable argument and has certainly thrown down the gauntlet to the forecasting community.

There are a group of forecasters led by the notorious J. Scott Armstrong (Armstrong bet Al Gore $10,000 US that future global temperature cold not be predicted--Gore refused the bet) who think the have the forecasting problem solved. The group argues (here) that there are 139 forecasting principles that are used by successful forecasters. Now would be an interesting time to see whether these principles could improve models of the US economy and lead to better forecasts.

Here are a few random thoughts on current approaches to forecasting:

• The focus should be on our existing models. However, I don't agree with Cowen that a model has to explain everything, for example, business confidence or regulatory uncertainty. A simple canonical forecasting model would be Q(t) = a + b Q(t-1) + e(t), the simple difference equation. A lot of forces such as "confidence" and "uncertainty" must fall into the error term, e(t). It's surprising how many forecasts are presented without prediction intervals based on observed error terms.
• The principles approach to forecast is way too ad hoc. If a forecast doesn't work, there's always a violated principle somewhere to explain the failure. One principle. for example, is that some things (like global warming if you believe Prof. Armstrong) cannot be forecast (the perfect escape clause). However, in that case, the model reduces to Q(t) = Q(t-1) + e(t), the random walk. In another blog (here), I'm searching for random walks in the stock market. Contrary to widely held academic opinion (here), finding stocks that are clearly random walks is not that easy.
• These considerations bring us back to the model again and particularly how the error term, e(t), is treated. One explanation for forecasting failure leading up to the Great Recession is that the Recession itself was a black swan event, unusual and improbable given widely held prejudices about normal distribution theory and error terms.
• There are problems with the way forecasts are typically constructed. If we try to predict tomorrow's Q(t) from yesterday's Q(t-1), we have to remember that Q(t-1) is not itself known with certainty. If errors tend to accumulate over time (as in the random walk model), the system may be very far away from reasonable values (as it was in the Subprime Mortgage crisis). In other words, forecasting during a bubble will work fine until the bubble bursts when the forecasts will be quite wrong.

To summarize, I think Tyler Cowen has it backwards. Forecasting right now should be easy. The bubble has burst. Forecasting when the next bubble will develop (a "return to normalcy") is another problem entirely.

Most of these issues are the subject of this blog and will be the topic of future posts.