Large and Small Regression Coefficients

Here's a trap that newbies to regression analysis have been known to fall into. It's to do with comparing the numerical values of the point estimates of  regression coefficients, and drawing conclusions that may not actually be justified.

What I have in mind is the following sort of situation. Suppose that Betsy (name changed to protect the innocent) has estimated a regression model that looks like this:

               Y = 0.4 + 3.0X₁ - 0.7X₂ + 6.0X₃ +.....+ residual .

Betsy is really proud of her first OLS regression, and tells her friends that "X3 is two times more important  in explaining y than is X1" (or words to that effect).

Putting to one side such issues as statistical significance (I haven't reported any standard errors for the estimated coefficients), Is Betsy entitled to make such a statement - based on the earth-shattering observation that "six equals three times two"?

NBER-NSF Time-Series Conference

The 2013 NBER-NSF Time Series Conference is being hosted by the Federal Reserve Board, in Washington D.C. next month. You can read about this event here.

Even a cursory look at the conference program will convince you that there are some really interesting looking papers being presented by some top people at this conference.

Check out the program, and you'll see what I mean. I'm going to be contacting several of the authors for access to their papers and talks. 

© 203, David E. Giles