Sunday, February 03, 2008
Jessica Hagy Explains why I have trouble in Interviews
by Ken Houghton
Which is presumably why Hagy is a best-selling author and not an econometrician.
Another well-conceived Venn diagram, though I agree with commenter Andrea.
Which is presumably why Hagy is a best-selling author and not an econometrician.
Labels: blogging, Econometrics, Economists View
Thursday, December 20, 2007
The Perfect Gift for a Certain New Aunt
by Ken Houghton
As a bonus, the same post leads to (and features a picture of) a graphic-laden representation of What Georgie Wanted the US Budget to Be.
Which, given my recent obsession, leads to the obvious question (zoom in on the penny in the lower right corner): If "the cost growth per beneficiary in the Medicare and Medicaid programs has tracked cost trends in private-sector health-care markets" (h/t DeLong; the original is WSJ subscriber-only, though it was probably Digged), why was the Medicare budget only projected for a 5% (nominal) increase? Or does that question answer itself?
Via the Social Science Statistics Blog, a flyswatter for the happy Milanophile.
As a bonus, the same post leads to (and features a picture of) a graphic-laden representation of What Georgie Wanted the US Budget to Be.
Which, given my recent obsession, leads to the obvious question (zoom in on the penny in the lower right corner): If "the cost growth per beneficiary in the Medicare and Medicaid programs has tracked cost trends in private-sector health-care markets" (h/t DeLong; the original is WSJ subscriber-only, though it was probably Digged), why was the Medicare budget only projected for a 5% (nominal) increase? Or does that question answer itself?
Labels: Brad DeLong, Bushonomics, Econometrics, Economists View, Health Care, Janelle, Politics, Statistics
Friday, June 22, 2007
The Death of Significance?
by Tom Bozzo
(Cross-posted at Total Drek.)
(*) This sometimes leads to wacky advice being given to everyday applied researchers from econo- or sociometricians, of the "if a result from an inconsistent esitmator goes away with a consistent (but inefficient) procedure, be suspicious [or vice-versa]." Armstrong's bottom-line recommendations address the reasonable suspicions that might arise.
At Decision Science News (another h/t to Brad DeLong), Dan Goldstein prints a comment from J. Scott Armstrong who has "concluded that tests of statistical significance should never be used." [Emphasis mine.] He is not conducting statistical performance art, and I substantially agree with the conclusion. A couple random remarks:
- There are results which lead to a conclusion that social science researchers tend to tweak their statistical models to cross significance thresholds so they can produce positive results with (presumably) greater probability of publication. But,
- To do so invalidates the published inferences. Because,
- The "classical" statistics reported by most software packages are invalid under any pretesting (i.e., deciding on a model specification based on results from preliminary estimation). And,
- The prospects for computing or simulating correct statistics are as good as the quality of the researcher's choice trail. But,
- A lot of social science "theories" don't determine the full set of explanatory variables, making the lure of statistical model diagnostics attractive. Though,
- There are families of models (e.g., the 'flexible functional form' cost models in economics, which I work with) where individual coefficients have no theoretical interpretation, in which case the researcher has no direct basis for evaluating the consequences of a restriction. More broadly,
- Properties of social science data often mean we need to use consistent but inefficient estimators; sometimes "better" significance from inappropriate estimation methods has little if any meaning. (*) Last,
- Some researchers (not least many who publish empirical results in top economics journals) tend to focus excessively on statistical significance to the detriment of more interesting discussions of the non-statistical significance of their results. (Views differ.)
Authors... instead... should report on effect sizes, confidence intervals, replications/extensions, and meta-analyses.For those of you with institutional access, links to the International Journal of Forecasting article are at the Decision Science News link.
(Cross-posted at Total Drek.)
(*) This sometimes leads to wacky advice being given to everyday applied researchers from econo- or sociometricians, of the "if a result from an inconsistent esitmator goes away with a consistent (but inefficient) procedure, be suspicious [or vice-versa]." Armstrong's bottom-line recommendations address the reasonable suspicions that might arise.
Labels: Econometrics, Economics, Social Science, Statistics
