I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?

Xiao-Li Meng and Xianchao Xie

Econometric Reviews, 2014, vol. 33, issue 1-4, 218-250

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 a 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 vulnerable 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. Using the example of estimating the autocorrelation ρ under a stationary AR(1) model, we also demonstrate the interaction between model assumptions and observation structures in seeking additional information, as the sampling interval s increases. Furthermore, for a given sample size, the optimal s for minimizing the asymptotic variance of is s = 1 if and only if ρ-super-2 ≤ 1/3; beyond that region the optimal s increases at the rate of log -super- - 1(ρ-super- - 2) as ρ approaches a unit root, as does the gain in efficiency relative to using s = 1. A practical implication of this result is that the so-called "non-informative" Jeffreys prior can be far from non-informative even for stationary time series models, because here it converges rapidly to a point mass at a unit root as s increases. Our overall emphasis is that intuition and conventional wisdom need to be examined via critical thinking and theoretical verification before they can be trusted fully.

Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2013.808567 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:33:y:2014:i:1-4:p:218-250

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2013.808567

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().