instrumental variables

Author
Abstract
This paper proposes a method in an environment with heterogeneous treatment effects to bound policy relevant treatment parameters (PRTP) without the monotonicity assumption that the instrumental variable works in the same direction for all individuals. While the procedure applies to all PRTP objects, this paper provides a detailed analysis for local average treatment effects in counterfactual environments (LATE*) that does not yet have a procedure for sensitivity analysis to monotonicity violations. The bounding framework uses the proportion of defiers relative to compliers as a sensitivity parameter and yields an identified set that is an interval. The bounds are sharp for binary outcomes. The method is illustrated in an example where the same sex instrument is used to find the effect of having a third child on labor force participation. I find that bounds are informative only for small violations in monotonicity.
Year of Publication
2022
Number
655
Date Published
08/2022
Yap, L. . (2022). Sensitivity of Policy Relevant Treatment Parameters to Violations of Monotonicity. Retrieved from http://arks.princeton.edu/ark:/88435/dsp015d86p341p (Original work published August 2022)
Working Papers
Year of Publication
2001
Number
455
Date Published
08/2001
Publication Language
eng
Citation Key
Journal of Economic Perspectives, Vol. 15, No 4, Fall 2001
Angrist, J. ., & Krueger, A. . (2001). Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01t435gc97f (Original work published August 2001)
Working Papers
Abstract

This paper is an examination of a potential problem inherent in instrumental variables
estimation in samples drawn from populations with a grouped structure. When data used in a
regression model are drawn from such a population, the regression errors may not satisfy the
assumption that they not be correlated. While the consequences of this correlation have been
recognized previously in the context of ordinary least squares estimation where the values of the
exogenous variables do not vary within group, little attention has been paid to the consequences of
such correlation for instrumental variables estimation. In this paper I examine the consequences of
intra-group correlation for instrumental variables estimation where the instruments (rather than the
exogenous variables) have repeated values within groups.
I first briefly summarize analytical results which demonstrate that ignoring the problem of
the grouped structure will yield estimated standard errors which are understated. While the
magnitude of the understatement depends on the size of the within-group variance relative to the
total variance, even small amounts of within-group correlation result in understatement. I then
perform simulations using different magnitudes of within-group correlation and various sample
sizes and calculate the standard errors with and without accounting for the correlation. I find that
with a data set comparable in size to many cross-sectional data sets used by empirical economists.
even within-group variance only one-tenth the size of the total variance yields estimated standard
errors that are as much as eight times too small relative to the correctly estimated standard errors.
Finally, I describe two methods for estimating standard errors which account for the within-group
correlation.

Year of Publication
1996
Number
374
Date Published
12/1996
Publication Language
eng
Citation Key
8082
Shore-Sheppard, L. . (1996). The Precision of Instrumental Variables Estimates With Grouped Data. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01v118rd530 (Original work published December 1996)
Working Papers
Abstract

Instrumental Variables (IV) estimates tend to be biased in the same direction as
Ordinary Least Squares (OLS) in finite samples if the instruments are weak. To address
this problem we propose a new IV estimator which we call Split Sample Instrumental
Variables (SSIV). SSIV works as follows: we randomly split the sample in half, and use
one half of the sample to estimate parameters of the first-stage equation. We then use these
estimated first-stage parameters to construct fitted values and second-stage parameter
estimates using data from the other half sample. SSIV is biased toward zero, rather than
toward the plim of the OLS estimate. However, an unbiased estimate of the attenuation
bias of SSIV can be calculated. We use this estimate of the attenuation bias to derive an
estimator that is asymptotically unbiased as the number of instruments tends to infinity,
holding the number of observations per instrument fixed. We label this new estimator
Unbiased Split Sample Instrumental Variables (USSIV). We apply SSIV and USSIV to the
data used by Angrist and Krueger (1991) to estimate the payoff to education.

Year of Publication
1993
Number
320
Date Published
10/1993
Publication Language
eng
Citation Key
Journal of Business and Economic Statistics, Vol. 13, No. 2, April, 1995
Angrist, J. ., & Krueger, A. . (1993). Split Sample Instrumental Variables. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01tq57nr01r (Original work published October 1993)
Working Papers