np.regressioniv: Nonparametric Instrumental Regression
In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types
npregiv R Documentation
Nonparametric Instrumental Regression
Description
npregiv computes nonparametric estimation of an instrumental
regression function \varphi defined by conditional moment
restrictions stemming from a structural econometric model: E [Y -
\varphi (Z,X) | W ] = 0, and involving
endogenous variables Y and Z and exogenous variables
X and instruments W. The function \varphi is the
solution of an ill-posed inverse problem.
When method="Tikhonov", npregiv uses the approach of
Darolles, Fan, Florens and Renault (2011) modified for local
polynomial kernel regression of any order (Darolles et al use local
constant kernel weighting which corresponds to setting p=0; see
below for details). When method="Landweber-Fridman",
npregiv uses the approach of Horowitz (2011) again using local
polynomial kernel regression (Horowitz uses B-spline weighting).
Usage
npregiv(y,
z,
w,
x = NULL,
zeval = NULL,
xeval = NULL,
alpha = NULL,
alpha.iter = NULL,
alpha.max = 1e-01,
alpha.min = 1e-10,
alpha.tol = .Machine$double.eps^0.25,
bw = NULL,
constant = 0.5,
iterate.diff.tol = 1.0e-08,
iterate.max = 1000,
iterate.Tikhonov = TRUE,
iterate.Tikhonov.num = 1,
method = c("Landweber-Fridman","Tikhonov"),
nmulti = NULL,
optim.abstol = .Machine$double.eps,
optim.maxattempts = 10,
optim.maxit = 500,
optim.method = c("Nelder-Mead", "BFGS", "CG"),
optim.reltol = sqrt(.Machine$double.eps),
p = 1,
penalize.iteration = TRUE,
random.seed = 42,
return.weights.phi = FALSE,
return.weights.phi.deriv.1 = FALSE,
return.weights.phi.deriv.2 = FALSE,
smooth.residuals = TRUE,
start.from = c("Eyz","EEywz"),
starting.values = NULL,
stop.on.increase = TRUE,
...)
Arguments
Data, Bandwidth Inputs And Formula Interface
These arguments identify the response, endogenous variables, instruments, exogenous covariates, and evaluation data.
w
a q-variate data frame of instruments. The data types may be
continuous, discrete (unordered and ordered factors), or some
combination thereof.
x
an r-variate data frame of exogenous regressors. The data
types may be continuous, discrete (unordered and ordered factors),
or some combination thereof.
xeval
an r-variate data frame of exogenous regressors on which the
regression will be estimated (evaluation data). By default,
evaluation takes place on the data provided by x.
y
a one (1) dimensional numeric or integer vector of dependent data, each
element i corresponding to each observation (row) i of
z.
z
a p-variate data frame of endogenous regressors. The data
types may be continuous, discrete (unordered and ordered factors),
or some combination thereof.
zeval
a p-variate data frame of endogenous regressors on which the
regression will be estimated (evaluation data). By default, evaluation
takes place on the data provided by z.
Landweber-Fridman Iteration Controls
These arguments control the Landweber-Fridman iteration path.
constant
the constant to use when using method="Landweber-Fridman".
iterate.diff.tol
the search tolerance for the difference in the stopping rule from
iteration to iteration when using method="Landweber-Fridman"
(disable by setting to zero).
iterate.max
an integer indicating the maximum number of iterations permitted
before termination occurs when using method="Landweber-Fridman".
iterate.Tikhonov
a logical value indicating whether to use iterated Tikhonov (one
iteration) or not when using method="Tikhonov".
iterate.Tikhonov.num
an integer indicating the number of iterations to conduct when using
method="Tikhonov".
method
the regularization method employed (defaults to
"Landweber-Fridman", see Horowitz (2011); see Darolles,
Fan, Florens and Renault (2011) for details for
"Tikhonov").
nmulti
integer number of times to restart the process of finding extrema of
the cross-validation function from different (random) initial
points.
Optimization Controls
These arguments control numerical optimization for the inverse problem.
optim.abstol
the absolute convergence tolerance used by optim. Only useful
for non-negative functions, as a tolerance for reaching
zero. Defaults to .Machine$double.eps.
optim.maxattempts
maximum number of attempts taken trying to achieve successful
convergence in optim. Defaults to 100.
optim.maxit
maximum number of iterations used by optim. Defaults
to 500.
optim.method
method used by optim for minimization of
the objective function. See ?optim for references. Defaults
to "Nelder-Mead".
the default method is an implementation of that of Nelder and Mead
(1965), that uses only function values and is robust but relatively
slow. It will work reasonably well for non-differentiable
functions.
method "BFGS" is quasi-Newton method (also known as a
variable metric algorithm), specifically that published
simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno.
This uses function values and gradients to build up a picture of the
surface to be optimized.
method "CG" is a conjugate gradients method based
on that by Fletcher and Reeves (1964) (but with the option of
Polak-Ribiere or Beale-Sorenson updates). Conjugate gradient
methods will generally be more fragile than the BFGS method, but as
they do not store a matrix they may be successful in much larger
optimization problems.
optim.reltol
relative convergence tolerance used by optim. The algorithm
stops if it is unable to reduce the value by a factor of 'reltol *
(abs(val) + reltol)' at a step. Defaults to
sqrt(.Machine$double.eps), typically about 1e-8.
p
the order of the local polynomial regression (defaults to
p=1, i.e. local linear).
Returned Weights And Smooth Residuals
These arguments control returned kernel weights, starting values, residual smoothing, and iteration stopping behavior.
penalize.iteration
a logical value indicating whether to
penalize the norm by the number of iterations or not (default
TRUE)
random.seed
an integer used to seed R's random number generator. This ensures
replicability of the numerical search. Defaults to 42.
return.weights.phi
a logical value (defaults to FALSE) indicating whether to
return the weight matrix which when postmultiplied by the response
y delivers the instrumental regression
return.weights.phi.deriv.1
a logical value (defaults to FALSE) indicating whether to
return the weight matrix which when postmultiplied by the response
y delivers the first partial derivative of the instrumental
regression with respect to z
return.weights.phi.deriv.2
a logical value (defaults to FALSE) indicating whether to
return the weight matrix which when postmultiplied by the response
y delivers the second partial derivative of the instrumental
regression with respect to z
smooth.residuals
a logical value indicating whether to
optimize bandwidths for the regression of
(y-\varphi(z)) on w (defaults to
TRUE) or for the regression of \varphi(z) on
w during iteration
start.from
a character string indicating whether to start from
E(Y|z) (default, "Eyz") or from E(E(Y|z)|z) (this can
be overridden by providing starting.values below)
starting.values
a value indicating whether to commence
Landweber-Fridman assuming
\varphi_{-1}=starting.values (proper
Landweber-Fridman) or instead begin from E(y|z) (defaults to
NULL, see details below)
stop.on.increase
a logical value (defaults to TRUE) indicating whether to halt
iteration if the stopping criterion (see below) increases over the
course of one iteration (i.e. it may be above the iteration tolerance
but increased)
Tikhonov Regularization Controls
These arguments control Tikhonov regularization and its bandwidth.
alpha
a numeric scalar that, if supplied, is used rather than numerically
solving for alpha, when using method="Tikhonov".
alpha.iter
a numeric scalar that, if supplied, is used for iterated Tikhonov
rather than numerically solving for alpha, when using
method="Tikhonov".
alpha.max
maximum of search range for \alpha, the Tikhonov
regularization parameter, when using method="Tikhonov".
alpha.min
minimum of search range for \alpha, the Tikhonov
regularization parameter, when using method="Tikhonov".
alpha.tol
the search tolerance for optimize when solving for
\alpha, the Tikhonov regularization parameter,
when using method="Tikhonov".
bw
an object which, if provided, contains bandwidths and parameters
(obtained from a previous invocation of npregiv) required to
re-compute the estimator without having to re-run cross-validation
and/or numerical optimization which is particularly costly in this
setting (see details below for an illustration of its use)
Additional Arguments
Further arguments are passed to lower-level kernel-sum and estimation routines.
...
additional arguments supplied to npksum.
Details
Documentation guide: see np.kernels for kernels, np.options for global options, and plot for plotting options.
Tikhonov regularization requires computation of weight matrices of
dimension n\times n which can be computationally costly
in terms of memory requirements and may be unsuitable for large
datasets. Landweber-Fridman will be preferred in such settings as it
does not require construction and storage of these weight matrices
while it also avoids the need for numerical optimization methods to
determine \alpha.
method="Landweber-Fridman" uses an optimal stopping rule based
upon ||E(y|w)-E(\varphi_k(z,x)|w)||^2
. However, if local rather than global
optima are encountered the resulting estimates can be overly noisy. To
best guard against this eventuality set nmulti to a larger
number than the default nmulti=min(2,p) for the first
iteration, where p is the dimension of the current smoothing
problem.
Note that for subsequent Landweber-Fridman iterations, a “warm
start” strategy is employed. The optimal bandwidths from the previous
iteration are used as starting values for the current iteration. The
user-supplied nmulti is respected for all iterations. For
iterations after the first successful one, these optimal bandwidths
serve as the first of the multiple initial points (a warm start),
while any remaining restarts are cold starts. If nmulti is not
explicitly supplied by the user, it defaults to min(2,p) for the first
iteration and to 1 for all subsequent iterations. This strategy
provides a balance between computational efficiency and robustness,
allowing the numerical optimizer to refine the structural bandwidths
as the residuals evolve incrementally while still guarding against
local optima.
When using method="Landweber-Fridman", iteration will terminate
when either the change in the value of
||(E(y|w)-E(\varphi_k(z,x)|w))/E(y|w)||^2
from iteration to iteration is
less than iterate.diff.tol or we hit iterate.max or
||(E(y|w)-E(\varphi_k(z,x)|w))/E(y|w)||^2
stops falling in value and
starts rising.
The option bw= would be useful, say, when bootstrapping is
necessary. Note that when passing bw, it must be obtained from
a previous invocation of npregiv. For instance, if
model.iv was obtained from an invocation of npregiv with
method="Landweber-Fridman", then the following needs to be fed
to the subsequent invocation of npregiv:
model.iv <- npregiv(\dots)
bw <- NULL
bw$bw.E.y.w <- model.iv$bw.E.y.w
bw$bw.E.y.z <- model.iv$bw.E.y.z
bw$bw.resid.w <- model.iv$bw.resid.w
bw$bw.resid.fitted.w.z <- model.iv$bw.resid.fitted.w.z
bw$norm.index <- model.iv$norm.index
foo <- npregiv(\dots,bw=bw)
If, on the other hand model.iv was obtained from an invocation
of npregiv with method="Tikhonov", then the following
needs to be fed to the subsequent invocation of npregiv:
model.iv <- npregiv(\dots)
bw <- NULL
bw$alpha <- model.iv$alpha
bw$alpha.iter <- model.iv$alpha.iter
bw$bw.E.y.w <- model.iv$bw.E.y.w
bw$bw.E.E.y.w.z <- model.iv$bw.E.E.y.w.z
bw$bw.E.phi.w <- model.iv$bw.E.phi.w
bw$bw.E.E.phi.w.z <- model.iv$bw.E.E.phi.w.z
foo <- npregiv(\dots,bw=bw)
Or, if model.iv was obtained from an invocation of
npregiv with either method="Landweber-Fridman" or
method="Tikhonov", then the following would also work:
model.iv <- npregiv(\dots)
foo <- npregiv(\dots,bw=model.iv)
When exogenous predictors x (xeval) are passed, they are
appended to both the endogenous predictors z and the
instruments w as additional columns. If this is not desired,
one can manually append the exogenous variables to z (or
w) prior to passing z (or w), and then they will
only appear among the z or w as desired.
Value
npregiv returns a npregiv object. The generic
functions print, summary, and
plot support objects of this type.
npregiv returns a list with components phi,
phi.mat and either alpha when method="Tikhonov"
or norm.index, norm.stop and convergence when
method="Landweber-Fridman", among others.
In addition, if any of return.weights.* are invoked
(*=1,2), then phi.weights and phi.deriv.*.weights
return weight matrices for computing the instrumental regression and
its partial derivatives. Note that these weights, post multiplied by
the response vector y, will deliver the estimates returned in
phi, phi.deriv.1, and phi.deriv.2 (the latter
only being produced when p is 2 or greater). When invoked with
evaluation data, similar matrices are returned but named
phi.eval.weights and phi.deriv.eval.*.weights. These
weights can be used for constrained estimation, among others.
When method="Landweber-Fridman" is invoked, bandwidth objects
are returned in bw.E.y.w (scalar/vector), bw.E.y.z
(scalar/vector), and bw.resid.w (matrix) and
bw.resid.fitted.w.z, the latter matrices containing bandwidths
for each iteration stored as rows. When method="Tikhonov" is
invoked, bandwidth objects are returned in bw.E.y.w,
bw.E.E.y.w.z, and bw.E.phi.w and bw.E.E.phi.w.z.
Note
This function should be considered to be in ‘beta test’ status until further notice.
Author(s)
Jeffrey S. Racine racinej@mcmaster.ca, Samuele Centorrino
samuele.centorrino@univ-tlse1.fr
References
Carrasco, M. and J.P. Florens and E. Renault (2007), “Linear
Inverse Problems in Structural Econometrics Estimation Based on
Spectral Decomposition and Regularization,” In: James J. Heckman and
Edward E. Leamer, Editor(s), Handbook of Econometrics, Elsevier, 2007,
Volume 6, Part 2, Chapter 77, Pages 5633-5751
Darolles, S. and Y. Fan and J.P. Florens and E. Renault (2011),
“Nonparametric instrumental regression,” Econometrica, 79,
1541-1565.
Feve, F. and J.P. Florens (2010), “The practice of
non-parametric estimation by solving inverse problems: the example of
transformation models,” Econometrics Journal, 13, S1-S27.
Florens, J.P. and J.S. Racine and S. Centorrino (2018),
“Nonparametric instrumental derivatives,” Journal of
Nonparametric Statistics, 30 (2), 368-391.
Fridman, V. M. (1956), “A method of successive approximations
for Fredholm integral equations of the first kind,” Uspeskhi,
Math. Nauk., 11, 233-334, in Russian.
Horowitz, J.L. (2011), “Applied nonparametric instrumental
variables estimation,” Econometrica, 79, 347-394.
Landweber, L. (1951), “An iterative formula for Fredholm
integral equations of the first kind,” American Journal of
Mathematics, 73, 615-24.
Li, Q. and J.S. Racine (2007), Nonparametric Econometrics:
Theory and Practice, Princeton University Press.
Li, Q. and J.S. Racine (2004), “Cross-validated Local Linear
Nonparametric Regression,” Statistica Sinica, 14, 485-512.
See Also
np.kernels, np.options, plot
npregivderiv,npreg
Examples
## Not run:
## This illustration was made possible by Samuele Centorrino
## <samuele.centorrino@univ-tlse1.fr>
set.seed(42)
n <- 500
## The DGP is as follows:
## 1) y = phi(z) + u
## 2) E(u|z) != 0 (endogeneity present)
## 3) Suppose there exists an instrument w such that z = f(w) + v and
## E(u|w) = 0
## 4) We generate v, w, and generate u such that u and z are
## correlated. To achieve this we express u as a function of v (i.e. u =
## gamma v + eps)
v <- rnorm(n,mean=0,sd=0.27)
eps <- rnorm(n,mean=0,sd=0.05)
u <- -0.5*v + eps
w <- rnorm(n,mean=0,sd=1)
## In Darolles et al (2011) there exist two DGPs. The first is
## phi(z)=z^2 and the second is phi(z)=exp(-abs(z)) (which is
## discontinuous and has a kink at zero).
fun1 <- function(z) { z^2 }
fun2 <- function(z) { exp(-abs(z)) }
z <- 0.2*w + v
## Generate two y vectors for each function.
y1 <- fun1(z) + u
y2 <- fun2(z) + u
## You set y to be either y1 or y2 (ditto for phi) depending on which
## DGP you are considering:
y <- y1
phi <- fun1
## Sort on z (for plotting)
ivdata <- data.frame(y,z,w)
ivdata <- ivdata[order(ivdata$z),]
rm(y,z,w)
attach(ivdata)
model.iv <- npregiv(y=y,z=z,w=w)
phi.iv <- model.iv$phi
## Now the non-iv local linear estimator of E(y|z)
ll.mean <- fitted(npreg(y~z,regtype="ll"))
## For the plots, restrict focal attention to the bulk of the data
## (i.e. for the plotting area trim out 1/4 of one percent from each
## tail of y and z)
trim <- 0.0025
curve(phi,min(z),max(z),
xlim=quantile(z,c(trim,1-trim)),
ylim=quantile(y,c(trim,1-trim)),
ylab="Y",
xlab="Z",
main="Nonparametric Instrumental Kernel Regression",
lwd=2,lty=1)
points(z,y,type="p",cex=.25,col="grey")
lines(z,phi.iv,col="blue",lwd=2,lty=2)
lines(z,ll.mean,col="red",lwd=2,lty=4)
legend("topright",
c(expression(paste(varphi(z))),
expression(paste("Nonparametric ",hat(varphi)(z))),
"Nonparametric E(y|z)"),
lty=c(1,2,4),
col=c("black","blue","red"),
lwd=c(2,2,2),
bty="n")
## End(Not run)
np documentation built on May 3, 2026, 1:07 a.m.
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| npregiv | R Documentation |
Nonparametric Instrumental Regression
Description
npregiv computes nonparametric estimation of an instrumental
regression function \varphi defined by conditional moment
restrictions stemming from a structural econometric model: E [Y -
\varphi (Z,X) | W ] = 0, and involving
endogenous variables Y and Z and exogenous variables
X and instruments W. The function \varphi is the
solution of an ill-posed inverse problem.
When method="Tikhonov", npregiv uses the approach of
Darolles, Fan, Florens and Renault (2011) modified for local
polynomial kernel regression of any order (Darolles et al use local
constant kernel weighting which corresponds to setting p=0; see
below for details). When method="Landweber-Fridman",
npregiv uses the approach of Horowitz (2011) again using local
polynomial kernel regression (Horowitz uses B-spline weighting).
Usage
npregiv(y,
z,
w,
x = NULL,
zeval = NULL,
xeval = NULL,
alpha = NULL,
alpha.iter = NULL,
alpha.max = 1e-01,
alpha.min = 1e-10,
alpha.tol = .Machine$double.eps^0.25,
bw = NULL,
constant = 0.5,
iterate.diff.tol = 1.0e-08,
iterate.max = 1000,
iterate.Tikhonov = TRUE,
iterate.Tikhonov.num = 1,
method = c("Landweber-Fridman","Tikhonov"),
nmulti = NULL,
optim.abstol = .Machine$double.eps,
optim.maxattempts = 10,
optim.maxit = 500,
optim.method = c("Nelder-Mead", "BFGS", "CG"),
optim.reltol = sqrt(.Machine$double.eps),
p = 1,
penalize.iteration = TRUE,
random.seed = 42,
return.weights.phi = FALSE,
return.weights.phi.deriv.1 = FALSE,
return.weights.phi.deriv.2 = FALSE,
smooth.residuals = TRUE,
start.from = c("Eyz","EEywz"),
starting.values = NULL,
stop.on.increase = TRUE,
...)
Arguments
Data, Bandwidth Inputs And Formula Interface
These arguments identify the response, endogenous variables, instruments, exogenous covariates, and evaluation data.
w |
a |
x |
an |
xeval |
an |
y |
a one (1) dimensional numeric or integer vector of dependent data, each
element |
z |
a |
zeval |
a |
Landweber-Fridman Iteration Controls
These arguments control the Landweber-Fridman iteration path.
constant |
the constant to use when using |
iterate.diff.tol |
the search tolerance for the difference in the stopping rule from
iteration to iteration when using |
iterate.max |
an integer indicating the maximum number of iterations permitted
before termination occurs when using |
iterate.Tikhonov |
a logical value indicating whether to use iterated Tikhonov (one
iteration) or not when using |
iterate.Tikhonov.num |
an integer indicating the number of iterations to conduct when using
|
method |
the regularization method employed (defaults to
|
nmulti |
integer number of times to restart the process of finding extrema of the cross-validation function from different (random) initial points. |
Optimization Controls
These arguments control numerical optimization for the inverse problem.
optim.abstol |
the absolute convergence tolerance used by |
optim.maxattempts |
maximum number of attempts taken trying to achieve successful
convergence in |
optim.maxit |
maximum number of iterations used by |
optim.method |
method used by the default method is an implementation of that of Nelder and Mead (1965), that uses only function values and is robust but relatively slow. It will work reasonably well for non-differentiable functions. method method |
optim.reltol |
relative convergence tolerance used by |
p |
the order of the local polynomial regression (defaults to
|
Returned Weights And Smooth Residuals
These arguments control returned kernel weights, starting values, residual smoothing, and iteration stopping behavior.
penalize.iteration |
a logical value indicating whether to
penalize the norm by the number of iterations or not (default
|
random.seed |
an integer used to seed R's random number generator. This ensures replicability of the numerical search. Defaults to 42. |
return.weights.phi |
a logical value (defaults to |
return.weights.phi.deriv.1 |
a logical value (defaults to |
return.weights.phi.deriv.2 |
a logical value (defaults to |
smooth.residuals |
a logical value indicating whether to
optimize bandwidths for the regression of
|
start.from |
a character string indicating whether to start from
|
starting.values |
a value indicating whether to commence
Landweber-Fridman assuming
|
stop.on.increase |
a logical value (defaults to |
Tikhonov Regularization Controls
These arguments control Tikhonov regularization and its bandwidth.
alpha |
a numeric scalar that, if supplied, is used rather than numerically
solving for |
alpha.iter |
a numeric scalar that, if supplied, is used for iterated Tikhonov
rather than numerically solving for |
alpha.max |
maximum of search range for |
alpha.min |
minimum of search range for |
alpha.tol |
the search tolerance for |
bw |
an object which, if provided, contains bandwidths and parameters
(obtained from a previous invocation of |
Additional Arguments
Further arguments are passed to lower-level kernel-sum and estimation routines.
... |
additional arguments supplied to |
Details
Documentation guide: see np.kernels for kernels, np.options for global options, and plot for plotting options.
Tikhonov regularization requires computation of weight matrices of
dimension n\times n which can be computationally costly
in terms of memory requirements and may be unsuitable for large
datasets. Landweber-Fridman will be preferred in such settings as it
does not require construction and storage of these weight matrices
while it also avoids the need for numerical optimization methods to
determine \alpha.
method="Landweber-Fridman" uses an optimal stopping rule based
upon ||E(y|w)-E(\varphi_k(z,x)|w)||^2
. However, if local rather than global
optima are encountered the resulting estimates can be overly noisy. To
best guard against this eventuality set nmulti to a larger
number than the default nmulti=min(2,p) for the first
iteration, where p is the dimension of the current smoothing
problem.
Note that for subsequent Landweber-Fridman iterations, a “warm
start” strategy is employed. The optimal bandwidths from the previous
iteration are used as starting values for the current iteration. The
user-supplied nmulti is respected for all iterations. For
iterations after the first successful one, these optimal bandwidths
serve as the first of the multiple initial points (a warm start),
while any remaining restarts are cold starts. If nmulti is not
explicitly supplied by the user, it defaults to min(2,p) for the first
iteration and to 1 for all subsequent iterations. This strategy
provides a balance between computational efficiency and robustness,
allowing the numerical optimizer to refine the structural bandwidths
as the residuals evolve incrementally while still guarding against
local optima.
When using method="Landweber-Fridman", iteration will terminate
when either the change in the value of
||(E(y|w)-E(\varphi_k(z,x)|w))/E(y|w)||^2
from iteration to iteration is
less than iterate.diff.tol or we hit iterate.max or
||(E(y|w)-E(\varphi_k(z,x)|w))/E(y|w)||^2
stops falling in value and
starts rising.
The option bw= would be useful, say, when bootstrapping is
necessary. Note that when passing bw, it must be obtained from
a previous invocation of npregiv. For instance, if
model.iv was obtained from an invocation of npregiv with
method="Landweber-Fridman", then the following needs to be fed
to the subsequent invocation of npregiv:
model.iv <- npregiv(\dots)
bw <- NULL
bw$bw.E.y.w <- model.iv$bw.E.y.w
bw$bw.E.y.z <- model.iv$bw.E.y.z
bw$bw.resid.w <- model.iv$bw.resid.w
bw$bw.resid.fitted.w.z <- model.iv$bw.resid.fitted.w.z
bw$norm.index <- model.iv$norm.index
foo <- npregiv(\dots,bw=bw)
If, on the other hand model.iv was obtained from an invocation
of npregiv with method="Tikhonov", then the following
needs to be fed to the subsequent invocation of npregiv:
model.iv <- npregiv(\dots)
bw <- NULL
bw$alpha <- model.iv$alpha
bw$alpha.iter <- model.iv$alpha.iter
bw$bw.E.y.w <- model.iv$bw.E.y.w
bw$bw.E.E.y.w.z <- model.iv$bw.E.E.y.w.z
bw$bw.E.phi.w <- model.iv$bw.E.phi.w
bw$bw.E.E.phi.w.z <- model.iv$bw.E.E.phi.w.z
foo <- npregiv(\dots,bw=bw)
Or, if model.iv was obtained from an invocation of
npregiv with either method="Landweber-Fridman" or
method="Tikhonov", then the following would also work:
model.iv <- npregiv(\dots)
foo <- npregiv(\dots,bw=model.iv)
When exogenous predictors x (xeval) are passed, they are
appended to both the endogenous predictors z and the
instruments w as additional columns. If this is not desired,
one can manually append the exogenous variables to z (or
w) prior to passing z (or w), and then they will
only appear among the z or w as desired.
Value
npregiv returns a npregiv object. The generic
functions print, summary, and
plot support objects of this type.
npregiv returns a list with components phi,
phi.mat and either alpha when method="Tikhonov"
or norm.index, norm.stop and convergence when
method="Landweber-Fridman", among others.
In addition, if any of return.weights.* are invoked
(*=1,2), then phi.weights and phi.deriv.*.weights
return weight matrices for computing the instrumental regression and
its partial derivatives. Note that these weights, post multiplied by
the response vector y, will deliver the estimates returned in
phi, phi.deriv.1, and phi.deriv.2 (the latter
only being produced when p is 2 or greater). When invoked with
evaluation data, similar matrices are returned but named
phi.eval.weights and phi.deriv.eval.*.weights. These
weights can be used for constrained estimation, among others.
When method="Landweber-Fridman" is invoked, bandwidth objects
are returned in bw.E.y.w (scalar/vector), bw.E.y.z
(scalar/vector), and bw.resid.w (matrix) and
bw.resid.fitted.w.z, the latter matrices containing bandwidths
for each iteration stored as rows. When method="Tikhonov" is
invoked, bandwidth objects are returned in bw.E.y.w,
bw.E.E.y.w.z, and bw.E.phi.w and bw.E.E.phi.w.z.
Note
This function should be considered to be in ‘beta test’ status until further notice.
Author(s)
Jeffrey S. Racine racinej@mcmaster.ca, Samuele Centorrino samuele.centorrino@univ-tlse1.fr
References
Carrasco, M. and J.P. Florens and E. Renault (2007), “Linear Inverse Problems in Structural Econometrics Estimation Based on Spectral Decomposition and Regularization,” In: James J. Heckman and Edward E. Leamer, Editor(s), Handbook of Econometrics, Elsevier, 2007, Volume 6, Part 2, Chapter 77, Pages 5633-5751
Darolles, S. and Y. Fan and J.P. Florens and E. Renault (2011), “Nonparametric instrumental regression,” Econometrica, 79, 1541-1565.
Feve, F. and J.P. Florens (2010), “The practice of non-parametric estimation by solving inverse problems: the example of transformation models,” Econometrics Journal, 13, S1-S27.
Florens, J.P. and J.S. Racine and S. Centorrino (2018), “Nonparametric instrumental derivatives,” Journal of Nonparametric Statistics, 30 (2), 368-391.
Fridman, V. M. (1956), “A method of successive approximations for Fredholm integral equations of the first kind,” Uspeskhi, Math. Nauk., 11, 233-334, in Russian.
Horowitz, J.L. (2011), “Applied nonparametric instrumental variables estimation,” Econometrica, 79, 347-394.
Landweber, L. (1951), “An iterative formula for Fredholm integral equations of the first kind,” American Journal of Mathematics, 73, 615-24.
Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press.
Li, Q. and J.S. Racine (2004), “Cross-validated Local Linear Nonparametric Regression,” Statistica Sinica, 14, 485-512.
See Also
np.kernels, np.options, plot
npregivderiv,npreg
Examples
## Not run:
## This illustration was made possible by Samuele Centorrino
## <samuele.centorrino@univ-tlse1.fr>
set.seed(42)
n <- 500
## The DGP is as follows:
## 1) y = phi(z) + u
## 2) E(u|z) != 0 (endogeneity present)
## 3) Suppose there exists an instrument w such that z = f(w) + v and
## E(u|w) = 0
## 4) We generate v, w, and generate u such that u and z are
## correlated. To achieve this we express u as a function of v (i.e. u =
## gamma v + eps)
v <- rnorm(n,mean=0,sd=0.27)
eps <- rnorm(n,mean=0,sd=0.05)
u <- -0.5*v + eps
w <- rnorm(n,mean=0,sd=1)
## In Darolles et al (2011) there exist two DGPs. The first is
## phi(z)=z^2 and the second is phi(z)=exp(-abs(z)) (which is
## discontinuous and has a kink at zero).
fun1 <- function(z) { z^2 }
fun2 <- function(z) { exp(-abs(z)) }
z <- 0.2*w + v
## Generate two y vectors for each function.
y1 <- fun1(z) + u
y2 <- fun2(z) + u
## You set y to be either y1 or y2 (ditto for phi) depending on which
## DGP you are considering:
y <- y1
phi <- fun1
## Sort on z (for plotting)
ivdata <- data.frame(y,z,w)
ivdata <- ivdata[order(ivdata$z),]
rm(y,z,w)
attach(ivdata)
model.iv <- npregiv(y=y,z=z,w=w)
phi.iv <- model.iv$phi
## Now the non-iv local linear estimator of E(y|z)
ll.mean <- fitted(npreg(y~z,regtype="ll"))
## For the plots, restrict focal attention to the bulk of the data
## (i.e. for the plotting area trim out 1/4 of one percent from each
## tail of y and z)
trim <- 0.0025
curve(phi,min(z),max(z),
xlim=quantile(z,c(trim,1-trim)),
ylim=quantile(y,c(trim,1-trim)),
ylab="Y",
xlab="Z",
main="Nonparametric Instrumental Kernel Regression",
lwd=2,lty=1)
points(z,y,type="p",cex=.25,col="grey")
lines(z,phi.iv,col="blue",lwd=2,lty=2)
lines(z,ll.mean,col="red",lwd=2,lty=4)
legend("topright",
c(expression(paste(varphi(z))),
expression(paste("Nonparametric ",hat(varphi)(z))),
"Nonparametric E(y|z)"),
lty=c(1,2,4),
col=c("black","blue","red"),
lwd=c(2,2,2),
bty="n")
## End(Not run)
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