On the Informativeness of Descriptive Statistics for Structural Estimates / Isaiah Andrews, Matthew Gentzkow, Jesse M. Shapiro.

Format:

Book

Author/Creator:

Andrews, Isaiah.

Contributor:

National Bureau of Economic Research.

Gentzkow, Matthew.

Shapiro, Jesse M.

Series:

Working Paper Series (National Bureau of Economic Research) no. w25217.

NBER working paper series no. w25217

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2018.

Summary:

We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate <i>ĉ</i> of a structural quantity of interest <i>c</i> that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics <i>γ̂</i> that estimate features <i>γ</i> of the distribution of the data that are related to <i>c</i> under the base model. A reader entertains a less restrictive model that is local to the base model, under which the estimate <i>ĉ</i> may be biased. We study the reduction in worst-case bias from a restriction that requires the reader's model to respect the relationship between c and γ specified by the base model. Our main result shows that the proportional reduction in worst-case bias depends only on a quantity we call the <i>informativeness</i> of <i>γ̂</i> for <i>ĉ</i>. Informativeness can be easily estimated even for complex models. We recommend that researchers report estimated informativeness alongside their descriptive analyses, and we illustrate with applications to three recent papers.

Notes:

Print version record

November 2018.