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By Jason Stipp and Robert Johnson, CFA | 11-20-2012 03:00 PM

5 Guiding Principles for Economy Watchers

Investors can get a better read on the data by tuning out short-term quirks, understanding when indicators are most reliable, and properly sizing the economic magnitude of each report.

Jason Stipp: I'm Jason Stipp for Morningstar.

Our director of economic analysis Bob Johnson has been looking at the economic indicators for several years now, and he has a few guiding principles that can help you read those economic reports, too. He's here to share those with us today.

Thanks for joining me, Bob.

Bob Johnson: Great to be here.

Stipp: The first guiding principle is something that I hear you say a lot, and when you say it, you say, "There's a certain way that I like to look at the data and that's on a…"

Johnson: Three-month moving average, year-over-year basis.

Stipp: So, why do you look at the data that way. What does that do to help you get a better handle on the trends?

Johnson: Well, it strips out the monthly noise. So many of these reports are things that are mailed in, called in, and maybe one month somebody doesn't send something in, maybe some month somebody has a big sale they didn't have last year. … The economy moves and a lot of the data can get messed up in a single month.

So a three-month moving average is a great concept, [a great] way of looking at things, because it smoothes out some of those things. Sometimes there are strikes. There are a million things. So, that's one thing that I do for sure. So that's the "three-month" part.

The other part is the "year-over-year," and that deals with the seasonality issue. So many of the data are adjusted seasonally, and those seasonal adjustment factors have in many cases, in my opinion, been wrong. We've had a really volatile economy, we've had recessions, we've had auto industries completely change the way they account for their summer shutdowns, and all of those things make it a moving target. …

All of the government data assumes that everything's moving on a trend, and there's an underlying trend to everything, and then you strip out anything that's different from the trend, and a-ha--you've got the right answer. But when you've got things that really aren't trending, but instead have a bunch of really weird stuff happening …, the statistical data doesn't work out quite so well. So, you don't want to look at the individual pieces of data, because of the seasonal adjustment factors, and I can explain that a little bit more.

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