Local Indirect Least Squares and Average Marginal Effects in Nonseparable Structural Systems
ABSTRACT Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric identification holds under weaker assumptions than in (S); the proof here does not rely on decomposition of generalized functions into ordinary and singular parts, which may not hold. Conditions for continuity of the identification mapping are provided and a consistent nonparametric plug-in estimator for regression functions in the L₁ space constructed. Semiparametric identification via a finite set of moments is shown to hold for classes of functions that are explicitly characterized; unlike (S) existence of a moment generating function for the measurement
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ABSTRACT: The effect of errors in variables in nonparametric regression estimation is examined. To account for errors in covariates, deconvolution is involved in the construction of a new class of kernel estimators. It is shown that optimal local and global rates of convergence of these kernel estimators can be characterized by the tail behavior of the characteristic function of the error distribution. In fact, there are two types of rates of convergence according to whether the error is ordinary smooth or super smooth. It is also shown that these results hold uniformly over a class of joint distributions of the response and the covariate, which is rich enough for many practical applications. Furthermore, to achieve optimality, we show that the convergence rates of all possible estimators have a lower bound possessed by the kernel estimators.The Annals of Statistics 01/1993; · 2.53 Impact Factor
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ABSTRACT: We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group. Copyright The Econometric Society 2005.Econometrica 02/2005; 73(4):1053-1102. · 3.82 Impact Factor
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ABSTRACT: This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A fundamental asymmetry intrinsic to the method of instrumental variables (IV) is noted. Recent advances in IV estimation allow for heterogeneity in responses but not in choices, and the method breaks down when both choice and response equations are heterogeneous in a general way. Copyright The Econometric Society 2005.Econometrica 02/2005; 73(3):669-738. · 3.82 Impact Factor
Local Indirect Least Squares and Average Marginal
E¤ects in Nonseparable Structural Systems
University of Chicago
UC San Diego
December 26, 2009
We study the scope of local indirect least squares (LILS) methods for nonparamet-
rically estimating average marginal e¤ects of an endogenous cause X on a response
Y in triangular structural systems that need not exhibit linearity, separability, or
monotonicity in scalar unobservables. One main …nding is negative: in the fully
nonseparable case, LILS methods cannot recover the average marginal e¤ect. LILS
methods can nevertheless test the hypothesis of no e¤ect in the general nonseparable
case. We provide new nonparametric asymptotic theory, treating both the tradi-
tional case of observed exogenous instruments Z and the case where one observes
only error-laden proxies for Z.
Acknowledgement 0.1 We thank Stefan Hoderlein, Xun Lu, Andres Santos, and
Suyong Song for helpful comments and suggestions. Any errors are the authors’ re-
sponsibility. S. M. Schennach acknowledges support from the National Science Foun-
dation via grants SES-0452089 and SES-0752699. This is a revised version of a paper
titled "Estimating Average Marginal E¤ects in Nonseparable Structural Systems."
JEL Classi…cation Numbers: C13, C14, C31.
Keywords: indirect least squares, instrumental variables, measurement error, non-
parametric estimator, nonseparable structural equations.
?Corresponding author. Address: Dept. of Economics 0508, UCSD, La Jolla, CA 92093-0508. Tele-
phone: 858 534-3502; fax 858 523-2151; email address: email@example.com
This paper studies the scope of indirect least squares-like methods for the identi…cation
and nonparametric estimation of marginal e¤ects of an endogenous cause X on a response
of interest Y without assuming linearity, separability, monotonicity, or the presence of
solely scalar disturbances for the structural equations. As we show, control variables need
not be available in such circumstances, so we rely only on the availability of exogenous
instruments, Z; which may or may not be perfectly observed.
We follow the literature in distinguishing the “instrumental variable” (IV) and “control
variable” approaches for identifying and estimating structural e¤ects of endogenous causes
(see e.g. Blundell and Powell, 2003; Darolles, Florens, and Renault, 2003; and Hahn and
Ridder, 2009). Correspondingly, Chalak and White (2009) (CW) emphasize the structural
origins of instruments yielding (conditional) independence relationships that serve to iden-
tify e¤ects of interest. Classical IV methods make use of exogenous instruments that are
independent of the unobserved causes. On the other hand, control variable methods make
use of conditioning instruments that, once conditioned on, ensure the conditional indepen-
dence of the observed causes of interest and the unobserved causes. In general, neither of
these (conditional) independence relations is su¢cient for the other.
Using a control variable approach, Altonji and Matzkin (2005) and Hoderlein and Mam-
men (2007) study identifying and estimating local average structural derivatives (marginal
e¤ects) in general structures without specifying how the endogenous cause of interest or con-
ditioning instruments are generated. Hoderlein (2005, 2007) and Imbens and Newey (2009)
derive useful control variables in nonlinear structures where the cause of interest is deter-
mined by exogenous instruments and a scalar unobserved term and is strictly monotonic
(or even additively separable) in this scalar. Chalak and White (2007) and White and
Chalak (2008) discuss identifying and estimating causal e¤ects in structures nonseparable
between observables and multiple unobservables, providing structural conditions ensuring
the availability of useful conditioning instruments more generally.
In the absence of control variables, methods based on classical IVs may provide a way
to conduct structural inference in nonlinear systems. Two extensions of IV to nonlinear
systems have been studied in the literature. The …rst is based on what Darolles, Florens,
and Renault (2003) call "instrumental regression" (IR), where Y is separably determined
as, say, Y = r(X)+"; with E(" j Z) = 0: Blundell and Powell (2003), Darolles, Florens, and
Renault (2003), Newey and Powell (2003), and Santos (2006), among others, show that IR
methods can reliably identify speci…c e¤ect measures in separable structures. But they lose
their structural interpretation in the nonseparable case unless X is separably determined
(see e.g. Blundell and Powell, 2003; Hahn and Ridder, 2009).
A second extension of IV makes use of exogenous instruments to study e¤ect measures
constructed as ratios of certain derivatives, derivative ratio (DR) e¤ect measures, for short.
In classical linear structural systems with exogenous instruments, these e¤ects motivate and
underlie Haavelmo’s (1943) classical method of indirect least squares (ILS). In the treatment
e¤ects literature, Angrist and Imbens (1994) and Angrist, Imbens, and Rubin (1996) show
that DR e¤ect measures have causal interpretations for speci…c subgroups of the population
of interest. In selection models, such as the generalized Roy model, Heckman (1997),
Heckman and Vytlacil (1999, 2001, 2005), and Heckman, Urzua, and Vytlacil (2006), among
others, show that DR e¤ect measures correspond to a variety of structurally informative
weighted averages of e¤ects of interest; the corresponding estimators are "local IV" or local
ILS (LILS) estimators (see Heckman and Vytlacil, 2005; Carneiro, Heckman, and Vytlacil,
2009). A common feature of the treatment e¤ects and selection papers just mentioned is
their focus on speci…c triangular structures with binary or discrete treatment variables.
Although the work just cited establishes the usefulness of DR e¤ect measures and their
associated LILS estimators in speci…c contexts, an important open question is whether
these methods can be used to learn about the e¤ects of an endogenous cause on a response
of interest in more general triangular structures. We address this question here, studying
general structural equations that need not obey linearity, monotonicity, or separability. Nor
do we restrict the unobserved drivers to be scalar; these can be countably dimensioned.
Our analysis delivers contributions in a number of inter-related areas. The …rst is
a detailed analysis of the properties of DR/LILS methods that a¤ords clear insight into
their limitations and advantages, both inherently and relative to IR and control variable
methods. Our …ndings are a mixture of bad news and good news. One main …nding is
negative: in the fully nonseparable case, DR methods, like IR methods, cannot recover the
average marginal e¤ect of the endogenous cause on the response of interest. Nor can DR
methods identify local average marginal e¤ects of X on Y of the type recovered by control
variable methods. On the other hand, and also like IR methods, when X is separably
determined, DR methods do recover an instrument-conditioned average marginal e¤ect
more informative than the unconditional average marginal e¤ect.
We also …nd that, despite their failure to recover average marginal e¤ects in the fully
nonseparable case, DR/LILS methods can nevertheless generally be used to test the hy-
pothesis of no e¤ect. This is because DR methods identify a speci…c weighted average
marginal e¤ect that is always zero when the true marginal e¤ect is zero, and that is zero
only if a true average marginal e¤ect is zero given often plausible economic structure.
Thus, DR/LILS methods provide generally viable inference.
In the control variable literature, Imbens and Newey (2009) (see also Chesher (2003)
and Matzkin (2003)) study nonseparable structures in which although X is nonseparably
determined, it is strictly monotonic in a scalar unobserved cause. As we show, this structure
also enables suitably constructed DR ratios to measure average marginal e¤ects based on
IVs rather than control variables. Nevertheless, control variable methods, when available,
are more informative, as these provide local e¤ect measures, whereas DR methods do not.
IV methods based on restrictive functional form assumptions are typical in applications.
But economic theory is often uninformative about the validity of these restrictions, and all
methods (IR, control variable, and DR) are vulnerable to speci…c failures of these assump-
tions. Accordingly, it is important to develop speci…cation tests for critical functional form
assumptions. Thus, a second contribution is to show how DR methods can be used to
test the key hypothesis that X is separably determined. The results of this test inform
the interpretation of results, as a failure to reject implies that not only do LILS estimates
support inference about the absence of e¤ects, but the LILS estimates can be interpreted
as instrument-conditioned average marginal e¤ects. Given space limitations, however, we
leave to future work developing the statistical properties of these tests.
Our third area of contribution is to provide new nonparametric methods for DR/LILS
estimation and inference. We pay particular attention to the fact that in practice, one
may not be able to observe the true exogenous instruments. Instead, as in Butcher and
Case (1994) or Hausman (1997), one may use proxies for such unobserved instruments. In
linear structures, this poses no problem for structural inference despite the inconsistency
of the associated reduced form estimator, as CW discuss. As we show here, however, the
unobservability of instruments creates signi…cant obstacles to structural inference using DR
IV methods more generally. We introduce new methods that resolve this di¢culty.
In particular, we study two cases elucidated by CW: the traditional observed exogenous
instrument (OXI) case, where the exogenous instrument is observed without error; and the
proxies for unobserved exogenous instrument (PXI) case, where the exogenous instrument
is not directly observable, but error-contaminated measurements are available to serve
as proxy instruments. Standard IV su¢ces for both OXI and PXI in the linear case,
but otherwise OXI and PXI generally require fundamentally di¤erent estimation methods.
Generally, straightforward kernel or sieve methods su¢ce for OXI. The PXI case demands
a novel approach, however. Our PXI results are the …rst to cover the use of instrument
proxies in the general nonlinear nonparametric context.
For the OXI case, we apply in…nite order ("‡at-top") kernels (Politis and Romano, 1999)
to estimate functionals of the distributions of the observable variables that we then com-
bine to obtain new estimators of the average marginal e¤ect represented by the DR e¤ect
measure. We obtain new uniform convergence rates and asymptotic normality results for
estimators of instrument-conditioned average marginal e¤ects as well as root-n consistency
and asymptotic normality results for estimators of their unconditional weighted averages.
For the PXI case, we build on recent results of Schennach (2004a, 2004b) to obtain a
variety of new results. Speci…cally, we show that two error-contaminated measurements
of the unobserved exogenous instrument are su¢cient to identify objects of interest and
to deliver consistent estimators. The proxies need not be valid instruments. Our general
estimation theory covers densities of mismeasured variables and expectations conditional on
mismeasured variables, as well as their derivatives with respect to the mismeasured variable.
We provide new uniform convergence rates over expanding intervals (and, in some cases,
over the whole real line) as well as new asymptotic normality results in fully nonparametric
settings. We also consider nonlinear functionals of such nonparametric quantities and prove