Reading the trends in the data at Calculated Risk

I’ve relied heavily on the blog Calculated Risk over the last few years for updated economic data, for some amazing graphs, for focused analysis, and for predictions that are pretty much always correct, especially regarding the housing market. Blogger Bill McBride is more interested in clear explanations and thoughtful analysis than in hyperbole.

In an era of breathless, impulsive media, his calm patient style has proven a winning approach. From Professor James Hamilton writing in Time magazine in March 2011, when Calculated Risk was selected one of the most important economics blogs in the country:

“If you only follow one economics blog, it has to be Calculated Risk, run by Bill McBride. The site provides concise and very accessible summaries of all the key economic data and developments. One of the reasons McBride is able to do this so well is that he has an almost uncanny knack of recognizing which facts really matter. He began the blog in 2005 because he saw a disaster brewing in the form of the housing bubble, and tried his best to warn the rest of us of what was coming. I’ve followed him closely ever since, and I don’t know if he’s ever been wrong. My advice is, if you’ve come up with a different conclusion from McBride on how economic developments are going to unfold, you’d be wise to think it over again!”

A few months ago, Calculated Risk began a monthly contest using Facebook logins for readers to pick the under or over or on-the-money for a wide range of predictions for upcoming releases of economic data.

The leaderboard for Calculated Risk's May Facebook contest

May was my first month participating actively in Calculated Risk’s contest, and I managed to win, with 13 correct predictions out of 16 in the month’s contest. I’m rather sillily proud of this accomplishment, especially since I even beat McBride himself.

And I shouldn’t have missed one question that I did miss. I correctly assumed that new home sales in April would be stronger than the consensus (all the data pointed higher), but for some reason I still thought that housing starts would be below consensus.

I also missed the call on the monthly trade deficit — not an area I have any particular expertise in.

And I also thought that the unemployment rate would stay at 8.2% despite decent job growth. I keep expecting to see enough of an increase in the labor force participation rate to push unemployment a little higher despite steady job creation. But I might need to let go of that notion soon if it’s not supported by the data.

Most of the other predictions seemed like pretty easy ones, given trends in various sectors of the economy. Of course, it’s possible that my reasoning on some or all of those picks was completely flawed, even though I managed to get the right answer.

So where does this “consensus” come from? Well, it comes from experts in various fields. So if the experts are the smartest people in the room as one would hope, then guessing the over or under on their projections should be pretty much a coin toss. But things aren’t that straightforward. In some cases, the consensus projections are calculated days or maybe even weeks in advance of the data release, so more current data might give clues about whether the prediction is too high, too low, or right on. And sometimes it seems pretty clear that the experts arriving at the consensus simply aren’t looking closely enough at what’s actually happening. I began discovering this back in the middle of the last decade in writing my columns in the Savannah Morning News.

I’ll certainly be curious to see how the contest goes in the coming months.