GSO Spring Speaker 2013

I got more data, my model is more refined, but my estimator is getting worse! Am I just dumb?

Xiao-Li Meng
Professor
Department of Statistics Chair, Harvard University

Venue: SC239

Abstract:

Possibly, but more likely you are merely a victim of conventional wisdom.  More data or better models by no means guarantee better estimators (e.g., with smaller mean squared error), when you are not following probabilistically principled methods such as MLE (for large samples) or Bayesian approaches.  Estimating equations are particularly dangerous in this regard, almost a necessary price for their robustness.  These points will be demonstrated via common tasks of estimating regression parameters and correlations, under simple models such as bivariate normal and ARCH(1).  Some general strategies for detecting and avoiding such pitfalls are suggested, including checking for self-efficiency (Meng, 1994, Statistical Science) and adopting a guiding working model.

Of course, Bayesians are not automatically immune either to being a victim of conventional wisdom.  A simple example is given in the context of a stationary AR(1) model where the so-called "non-informative" Jeffreys prior can get arbitrarily close to a point mass at a unit root, hardly non-informative by any measure.

This talk is based on Meng and Xie (2013, Econometric Reviews).