TY  - JOUR
AU  - Lee,David S.
AU  - Card,David
TI  - Regression Discontinuity Inference with Specification Error
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 322
PY  - 2006
Y2  - March 2006
UR  - http://www.nber.org/papers/t0322
L1  - http://www.nber.org/papers/t0322.pdf
N1  - Author contact info:
David Lee
Industrial Relations Section
Princeton University
Firestone Library A-16-J
Princeton, NJ 08544
Tel: 609/258-9548
Fax: 609/258-2907
E-Mail: davidlee@princeton.edu

David Card
Department of Economics
549 Evans Hall, #3880
University of California, Berkeley
Berkeley, CA  94720-3880
Tel: 510/642-5222
Fax: 510/643-7042
E-Mail: card@econ.berkeley.edu
AB  - A regression discontinuity (RD) research design is appropriate for program evaluation problems in which treatment status (or the probability of treatment) depends on whether an observed covariate exceeds a fixed threshold. In many applications the treatment-determining covariate is discrete. This makes it impossible to compare outcomes for observations "just above" and "just below" the treatment threshold, and requires the researcher to choose a functional form for the relationship between the treatment variable and the outcomes of interest. We propose a simple econometric procedure to account for uncertainty in the choice of functional form for RD designs with discrete support. In particular, we model deviations of the true regression function from a given approximating function -- the specification errors -- as random. Conventional standard errors ignore the group structure induced by specification errors and tend to overstate the precision of the estimated program impacts. The proposed inference procedure that allows for specification error also has a natural interpretation within a Bayesian framework.
ER  -