st: Re: Too many sample points in microeconometric analyses?

[evan roberts <eroberts@hist.umn.edu>]

Along with all the other useful replies to Karsten Staehr's question, 
these papers by McCloskey might be useful reading for anyone interested 
in this topic.

McCloskey, Deirdre N., and Stephen T. Ziliak. "The Standard Error of 
Regressions." Journal of Economic Literature 34, no. 1 (1996): 97-114.

McCloskey, Donald N. "The Loss Function Has Been Mislaid: The Rhetoric 
of Significance Tests." American Economic Review 75, no. 2 (1985): 201-05.

The basic advice is to focus on substantive relationships and effects, 
not p-values.

Evan Roberts


> I have discussed with a co-author whether datasets used for microeconometric
> analyses can be "too large" in the sense of comprising "too many"
> observations? With a very large sample size (e.g. over 10,000 observations),
> very many estimated coefficients tend to be significant at the 1%-level. My
> co-author argues that such datasets with very many observations lead to
> "inflated significance levels" and one should be careful about the
> interpretation of the estimated standard errors. He suggests reducing the
> sample size by randomly drawing a smaller sample from the original sample.
> My questions are: 1) Can sample sizes be "too large" leading to too small
> standard errors? 2) Do anybody have a reference to papers discussing this
> issue? 3) Could it be related to possible misspecification problems of the
> model?
>
> Karsten Staehr
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