To be fair, in this industry, I am not sure I ever want the research question "addressed."  The beauty of working in small data modeling is that the efforts are necessary AND unending.  My industry employs a ton of quants, I like knowing that they will retain job security in the decades to come!  Chasing information amidst dozens of unstable econometric models is like a game of whack-a-mole.  And, whereas at some point, the game becomes boring; that is when it is time to leave it to the next generation and retire into senior management.  Generically stated (and this may offend more academic practitioners of my craft), my job is to cobble together a series of illustrative and useful lies in such a way that it passes the scrutiny of federal oversight, while being good enough to create a competitive advantage and provide for useful estimates to my firm.  This field requires a form of relaxed cynicism that many people in econometric practices could benefit from.  There is no right answer, everything we do is wrong, but some answers are more useful than others!  The constant searching for methodological improvements that will enhance the usefulness of these outputs should not be measured against the black and white notion of right and wrong.  Even competing approaches where one approach is "more wrong" may produce "more useful" outputs simply because "more wrong" on a particular concern can still be "more practical" in application.  I am an industry model theorist (a unicorn) and dedicate my time to trying to balance these two things while creating employable and interpretable methodologies that our model engineers can easily and efficiently apply.  Nonetheless, I am overjoyed to see your comments.  As a result of your comments, I have recently discovered a new vault of research (which is not yet well known in the field) that applies directly to a few of my concerns.  I have little doubt that the next methodology I create to mitigate these concerns will take a broader look at the potential of GLMM and hybrid-Bayesian approaches.  However, these approaches are difficult to deploy in our current environment due to end-user issues.  I came to this board with a simple question about scaling estimates for the VCVM for certain tests on an existing methodology, and came out with a head-start on research for the next generation of these models.  I can't thank you enough.  Keep on @SteveDenham ... View more