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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