Regressors

FYI, in S-i-M's newer "Bayesian Averaging" robustness tests of growth models, he finds that the optimal number of regressors is something like 7.4, if I recall correctly. ❋ Unknown (2009)

In regression (you can think it as "prediction"), one has variables called regressors, and (in this case) a variable called response variable. ❋ Unknown (2006)

I also dispute that only using a subset of regressors, rather than the full set, is a "tougher test". ❋ Unknown (2009)

So, the short answer is that I don't know what would happen if I threw in more and more regressors into the same regression (it would be a snap to do it, though). ❋ Unknown (2009)

He used seven variables at a time: 3 fixed regressors (life expectancy in 1960, primary school enrollment in 1960, and log GDP per capita in 1960), 3 rotating regressors, and the regressor that he's testing (plus a constant, of course). ❋ Unknown (2009)

What happens if you choose all the combinations of five regressors, or three? ❋ Unknown (2009)

But when I was a grad student at UCSD, Rob Engle taught us that he strongly favored smaller models (four or five regressors, at most), and I've carried that atheoretical, soft knowledge with me into my career. ❋ Unknown (2009)

I always thought that if you're tossing out the garbage regressors, then the remaining robust ones are quite likely be eating up lots of the econometric space spanned by GDP growth. ❋ Unknown (2009)

We next regressed this transformed variable against the list of regressors mentioned above using the least squares method. ❋ Unknown (2010)

In introudction to econometrics students are teached about severe biases due to measurement errors, primarily of course if it is about the regressors. ❋ Unknown (2009)

A major difference in the two approaches is the allowance for nonsense regressors for the significance estimation, because only that yields higher scores. ❋ Unknown (2007)

SCROTUS – supreme court regressors of the united states. quite sophomoric, but very appropriate. ❋ Unknown (2005)

The xreg parameter in arima is used to specify the matrix of regressors. ❋ Unknown (2006)

As explained in the beginning of this post, you get higher RE values simply by adding more regressors regardless of the fact if they have anything to do with ! ❋ Unknown (2006)

This leaves very few degrees of freedom, and they can easily be matched by nonsense regressors. ❋ Unknown (2006)

B-C said that the Mannian method was "susceptible" to nonsense regressors, a view that I share. ❋ Unknown (2006)

It seems that this point, while "easy to see" for Gerd and me, is not widely accepted and, as noted above, showing RE statistics for some classic nonsense regressors like the Yule 1926 example will be worthwhile to illustrate the pitfalls of relying exclusively on this measure. ❋ Unknown (2006)

"Dude, why are you buying [adult] [pacifiers]?" "I'm an age regressor, It helps me [relax]" ❋ Autistic Deer (2017)