Christian Hansen’s Research Page
Code for IVQR
Below are links to MATLAB and
Ox code for performing IVQR estimation and inference as developed in
“Instrumental Quantile Regression Inference for Structural and Treatment Effect
Models” (with Victor Chernozhukov) and “Instrumental Variable Quantile
Regression” (with Victor Chernozhukov).
Along with the code, each file contains examples illustrating how the
code may be implemented; the data for the examples may also be downloaded below.
1. MATLAB
Code
2. Ox
Code
4. Stata
Code available from Le Wang at University of New Hampshire
Code for Weak
Instrument Robust Inference
Below are links for the Stata code and data used in the empirical example in “A
Simple Approach to Heteroskedasticity and Autocorrelation Robust Inference with
Weak Instruments” (with Victor Chernozhukov).
The data are taken from Acemoglu, Johnson, and
Robinson (2001) “The Colonial Origins of Comparative Development: An Empirical
Investigation”. The code illustrates the
basic procedure and may easily be modified for other data sets and to provide
inference that is robust to autocorrelation or clustering.
1. Stata Code for weak instrument robust inference
2. Data
Code for
Sensitivity Analysis for IV (from “Plausibly Exogenous”)
Below are links for Stata code that produces some of the results from
“Plausibly Exogenous” (with Tim Conley and Peter Rossi). The code illustrates the basic procedure and
may easily be modified for other data sets.
The file with the Stata code also includes
sample data.
1. Stata Code for IV sensitivity analysis
Working Papers
Only unpublished work appears
here. A complete list of research
including publications may be found on my CV.
1. “Plausibly
Exogenous” (with Timothy Conley and Peter Rossi)
2. “Bias
Reduction for Bayesian and Frequentist Estimators” (with C. Alan Bester)
3. “Inference
with Dependent Data Using Cluster Covariance Estimators” (with C. Alan
Bester and Timothy Conley)
4. “Fixed-b
Asymptotics for Spatially Dependent Robust Nonparametric Covariance Matrix
Estimators” (with C. Alan Bester, Timothy Conley, and Timothy Vogelsang)
5. “Flexible
Correlated Random Effects Estimation in Panel Models with Unobserved
Heterogeneity” (with C. Alan Bester)
Other Material
Technical
Appendix for “Generalized Least Squares Inference in Panel and Multilevel
Models with Serial Correlation and Fixed Effects” Journal of Econometrics (October 2007).
Technical Appendix for “Asymptotic
Properties of a Robust Variance Matrix Estimator for Panel Data when T is
Large” Journal of Econometrics (December
2007).
Derivation
of F-statistic Result for “Asymptotic Properties of a Robust Variance
Matrix Estimator for Panel Data when T is Large” Journal of Econometrics (December 2007) contributed by Mark Watson
and James Stock. I am deeply indebted to
Stock and Watson for pointing this result out to me that they established while
working on their paper “Heteroskedasticity-Robust Standard Errors for Fixed
Effect Panel Data Regression” (Econometrica,
2008). I am also embarrassed that a
citation to their paper does not appear in the published version of my paper.
Working
paper version of "The Reduced Form: A Simple Approach to Inference with
Weak Instruments" (with Victor Chernozhukov, published as “The reduced
form: A simple approach to inference with weak instruments” Economics Letters, July 2008) with
additional tables and discussion excluded from published version.
Abstract for
Working Papers
“Plausibly
Exogenous” (with Timothy Conley and Peter Rossi)
Instrumental variables (IVs) are widely used to
identify effects in models with potentially endogenous explanatory variables.
In many cases, the instrument exclusion restriction that underlies the validity
of the usual IV inference holds only approximately; that is, the instruments
are ‘plausibly exogenous.’ We introduce a method of relaxing the exclusion
restriction and performing sensitivity analysis with respect to the degree of
violation. This provides practical tools for applied researchers who want to
proceed with less-than-perfect instruments. We illustrate our approach with
empirical examples that examine the effect of 401(k) participation upon asset
accumulation, demand for margarine, and returns-to-schooling.
“Bias
Reduction for Bayesian and Frequentist Estimators” (with C. Alan Bester)
We show that in parametric
likelihood models the first order bias in the posterior mode and the posterior
mean can be removed using objective Bayesian priors. These bias-reducing priors are defined as the
solution to a set of differential equations which may not be available in
closed form. We provide a simple and tractable
data dependent prior that solves the differential equations asymptotically and
removes the first order bias. When we
consider the posterior mode, this approach can be interpreted as penalized
maximum likelihood in a frequentist setting.
We illustrate the construction and use of the bias-reducing priors in
simple examples and a simulation study.
“Inference
with Dependent Data Using Cluster Covariance Estimators” (with C. Alan
Bester and Timothy Conley)
This paper presents a novel
way to conduct inference using dependent data in time series, spatial, and
panel data applications. Our method involves constructing t and Wald statistics
utilizing a cluster covariance matrix estimator (CCE). We then use an
approximation that takes the number of clusters/groups as fixed and the number
of observations per group to be large and calculate limiting distributions of
the t and Wald statistics. This approximation is analogous to `fixed-b'
asymptotics of Kiefer and Vogelsang (2002, 2005) (KV) for heteroskedasticity
and autocorrelation consistent inference, but in our case yields standard t and
F distributions where the number of groups essentially plays the role of sample
size. We provide simulation evidence that demonstrates our procedure outperforms
conventional inference procedures and performs comparably to KV.
“Fixed-b
Asymptotics for Spatially Dependent Robust Nonparametric Covariance Matrix
Estimators” (with C. Alan Bester, Timothy Conley, and Timothy Vogelsang)
This
paper develops a method for performing inference using spatially dependent
data. We consider test statistics formed using nonparametric covariance matrix
estimators that account for heteroskedasticity and spatial correlation (spatial
HAC). We provide distributions of commonly used test statistics under
“fixed-b" asymptotics, in which HAC smoothing parameters are proportional
to the sample size. Under this sequence, spatial HAC estimators are not
consistent but converge to non-degenerate limiting random variables that depend
on the HAC smoothing parameters and kernel. We show that the limit
distributions of commonly used test statistics are pivotal but non-standard, so
critical values must be obtained by simulation. We provide a simple and general
simulation procedure based on the i.i.d. bootstrap
that can be used to obtain critical values. We illustrate the potential gains
of the new approximation through simulations and an empirical example that
examines the effect of unjust dismissal doctrine on temporary help services
employment.
“Flexible
Correlated Random Effects Estimation in Panel Models with Unobserved
Heterogeneity” (with C. Alan Bester)
In
this paper, we consider identification in a correlated random effects model for
panel data. We assume that the likelihood for each individual in the panel is
known up to a finite dimensional common parameter and an individual specific
parameter. We allow the distribution of unobserved individual specific effects
to depend on observed explanatory variables and make no assumptions about the
particular functional form of this dependence.
This leads to a semiparametric problem where the parameters include a
finite dimensional common parameter, θ and an infinite dimensional
conditional density, q, that describes the
distribution of unobserved individual specific effects. For a given likelihood,
we establish restrictions on the space of functions H for the distribution of
unobserved heterogeneity under which {θ,q} are identified. We show the model parameters may
be consistently estimated by sieve maximum likelihood for a fixed panel length,
T. The conditions on H, which include assumptions about the support of
explanatory variables and smoothness of q in its arguments, are relatively mild
and are similar to those required for nonparametric density estimation.