Cycles and Macroeconomic Forecasting

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Motivation

I saw an /r/askEconomics post about Mark Armstrong’s “Economic Pi / Economic Confidence Model” and decided to look into the theory. Armstrong’s site, “Armstrong Economics”, looks like your run-of-the-mill economics conspiracy / theory / forecasting website, complete with events, conferences, and lots of expensive DVDs. If you’ve been on the econ/finance web, you’ve probably come across one of these before.

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Literature Review

However, since somebody asked, I decided to look into the theory some more. Thankfully, this business doesn’t function unless you do a fair amount of salesmanship, so Armstrong has helpfully laid out his Economic Confidence Model (hence ECM) for you.

The key to comprehending the Global Economy lies in the realization that we are not alone. Everything is connected in an intricate dynamic nonlinear network where the slightest change in one region can set in motion a ripple effect of dramatic proportions around the world. Understanding this dynamic nonlinear global network is the first step in restructuring government and our idea of managing our political-social-economy.

Hmm, what’s a nonlinear function? Oh, I know, a sine wave! The ECM appears to involve a combination of a trend and three sine waves of different amplitude and period, where the longer waves are “cycles” and the shorter waves are “waves.” You can see this in his chart plotting the “Gold Cycle Wave,” where the successive waves follow a “rule of 8,” or the 1979 plot, where the successive waves follow a “rule of 6.”

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Simulation

After learning about these cycles that determine macroeconomic variation, I decided to implement the ECM and see for myself. I used the variables that Armstrong studies (except for the value of the Roman Bronze Follis, which isn’t on FRED) and added another to check for external validity.

DJIA

Here’s the ECM applied to the Dow Jones. For some reason, FRED only has the DJIA going back to 2007, but over this sample it doesn’t look terrible. Maybe the Armstrong model just needs a longer history?

http://imgur.com/rI8RRkL

Gold

The fit on Gold prices looks pretty good back to 1968.

http://imgur.com/5mjigSk

Dollar

The dollar index also looks solid going back to 1973.

http://imgur.com/31CZ9hV

Real Estate

The Armstrong model for real estate looks ok in some parts (great recession) but awful in other parts (post-2011 housing recovery). What’s going on here?

http://imgur.com/YHpkgzP

Out-of-Sample

Finally, I tried to apply the Armstrong model out of sample.

For the finance/macro gurus in the audience, I used a pretty major macro index in the following chart: can you guess what it is?

http://imgur.com/EMA41wq

…

For the knowledgeable ones among you, you’ve probably guessed already that the above series was nothing more than ... a unit root! I constructed this time series specifically to have no built-in cycles or predictability, and yet the ECM approach picks up on a lot of signals.

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Conclusion

There are two elements behind this bad economics and why it’s so prevalent.

1. People are used to observed quarterly or yearly macro data. This means that “forecasters” like Armstrong only have to fit a fairly small number of data points. I was fitting with just sine functions here too. With the right family of polynomials, you can fit a series very well with only a few “fundamental economic indicators.”

2. Most lay people don’t really understand that forecasting is about out-of-sample predictions. In order to gain credibility, all you have to do is demonstrate that your model fits historical data. Retail investors don’t know that they have to ask about forecast errors too. Of course, to get access to the forecasts, you have to

Finally, to be fair, economic cycles are real! From business cycles to credit cycles, academic economists talk about things that look very similar to the lay person. And maybe the ECM does pick up on things ... like seasonality in home construction!

http://imgur.com/sGD7qWQ

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Replication

All the code is in this Julia notebook. It demonstrates how to use a few handy Julia packages like FredData and Optim. Enjoy!