Wednesday, December 21, 2011

Information and Entropy Econometrics

The eminent physicist Ed. Jaynes (1957a) wrote:
"Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum entropy estimate. It is least biased estimate possible on the given information; i.e., it is maximally noncommittal with regard to missing information."
In other words, when we want to describe noisy data with a statistical model, we should always choose the one that has Maximum Entropy.

Friday, December 16, 2011

"An Information Theoretic Approach to Econometrics"

George Judge & Ron Mittelhammer have a new book, hot off the press: An Information Theoretic Approach to Econometrics (CUP, 2012).

Thursday, December 15, 2011

Reported "Accuracy" for Regression Results

In a recent post I posed the question: "How many decimal places (or maybe significant digits) are appropriate when reporting OLS regression results?"

Tuesday, December 13, 2011

Heteros*edasticity

Earlier this year I had a post about the decline in the amount of attention paid to the concept of "multicollinearity" in econometrics texts over the years. It's a fully justifiable decline, in my view.

Sunday, December 11, 2011

Confidence Bands for the H-P Filter: Correction!

Aaaaaghhhh!!

In a post a couple of days ago I posted about constructing confidence bands for the trend that's extracted from a time-series using the Hodrick-Prescott (HP) filter. There was an error in my EViews program code that affected the last graph I showed in that post.

Friday, December 9, 2011

So, Sue Me!

"In a case that’s sending a frightening message to the blogger community, a U.S. District Court judge ruled that a blogger must pay $2.5 million to an investment firm she wrote about — because she isn’t a real journalist."

Thursday, December 8, 2011

Confidence Bands for the Hodrick-Prescott Filter

Signal extraction is a common pastime in empirical economics. When we fit a regression model we're extracting a signal about the dependent variable from the data, and separating it from the "noise". When we use the Hodrick-Prescott (HP) filter to extract the trend from a time-series, we're also engaging in signal extraction. In the case of a regression model we wouldn't dream of reporting estimated coefficients without their standard errors; or predictions without confidence bands. Why is it, then, that the trend that's extracted using the HP filter is always reported without any indication of the associated uncertainty?

Wednesday, December 7, 2011

Choconomics

My wife enjoys travelling with me to conferences, so I guess I'll be in Belgium next September for this one!

Tuesday, December 6, 2011

Professor of Official Statistics

A recent post, titled Free Data!, drew comments about some aspects of the availability and cost of official data in Canada. I was therefore intrigued to come across a recent advertisement for the position of Professor of Official Statistics, at an Australian University.

Monday, December 5, 2011

Precision Competition

In a recent post I raised the point about the spurious degree of precision that is often encountered with reported regression results. So, here's a challenge for you - how many decimal places (or maybe significant digits) are appropriate when reporting OLS regression results?