Christian Hansen
Biographic Data
| ID | 3960608 |
|---|---|
| NAME | Christian Hansen |
| GIVEN NAMES | Christian |
| FAMILY NAME | Hansen |
| SIGNATURE | HANSEN C |
| AFFILIATIONS | University of Chicago |
| ORCID | 0000-0002-5734-7529 |
| VERIFIED | Yes |
| TOTAL WORKS | 20 |
| TOTAL CITATIONS | 23 |
| WORKS AS AUTHOR | 20 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
We study a panel data model with heterogeneous effects, allowing slopes to vary across individuals and time. To reduce dimensionality, we assume these slopes follow a factor structure, so slope matrices can be estimated via low-rank regularized regression. We propose a multi-step estimation procedure incorporating sample splitting and partialing-out to enable valid inference after penalized estimation. We establish the asymptotic normality of the…
Model Averaging and Double Machine Learning
This paper discusses pairing double/debiased machine learning (DDML) with stacking , a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: Short‐stacking exploits the cross‐fitting step of DDML to substantially reduce the computational burden, and pooled stacking enforces common stacking weights over cross‐f…
Inference for Dependent Data with Learned Clusters
This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the p…
Clutch & Grasp
Presenting medical volume data using augmented reality (AR) can facilitate the identification of anatomical structures, the perception of their spatial relations and the development of mental maps compared to more commonly used monitors. However, interaction methods explored in these conventional settings may not be applicable in AR environments, or perform differently. In terms of mode activation, gestural interaction was shown to be a viable, t…
Targeted Undersmoothing
This paper proposes a procedure for assessing the sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By…
Pre-Event Trends in the Panel Event-Study Design
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference …
Double/debiased machine learning for treatment and structural parameters
We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) method…
Prevalence of intimate partner violence and abuse and associated factors among women enrolled into a cluster randomised trial in northwestern Tanzania
ClinicalTrials.gov - ID NCT02592252 , registered retrospectively on 13 August 2015
Inference in High-Dimensional Panel Models With an Application to Gun Control
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relativ…
Inference on Treatment Effects after Selection among High-Dimensional Controls
We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlle…
High-Dimensional Methods and Inference on Structural and Treatment Effects
Data with a large number of variables relative to the sample size-"high-dimensional data"-are readily available and increasingly common in empirical economics. Highdimensional data arise through a combination of two phenomena. First, the data may be inherently high dimensional in that many different characteristics per observation are available. For example, the US Census collects information on hundreds of individual characteristics and scanner …
Plausibly Exogenous
Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We present practical methods for performing inference while relaxing the exclusion restriction. We illustrate the approaches with empirical examples that examine t…
Instrumental Variables Estimation With Flexible Distributions
Instrumental variables are often associated with low estimator precision. This article explores efficiency gains that might be achievable using moment conditions that are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demon…
Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model
In this paper, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing strong functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function and the assumption that at least three time periods are available for each cross sec…
A Penalty Function Approach to Bias Reduction in Nonlinear Panel Models with Fixed Effects
We consider estimation of nonlinear panel data models with individual specific fixed effects. Estimation of these models is complicated since estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of all model parameters. We present a penalized objective function that reduces the bias in the resulting point estimates. The penalty function is simple to construct and requires no modification for…
Instrumental variable quantile regression
Estimation With Many Instrumental Variables
Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker to nonnormal disturbances, that adjust for many instruments. We find that this adjustment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors in t-ratios leads to an asymptotic approximation order that …
Instrumental quantile regression inference for structural and treatment effect models
An IV Model of Quantile Treatment Effects
The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles…
The Effects of 401(K) Participation on the Wealth Distribution
We use instrumental quantile regression approach to examine the effects of 401(k) plans on wealth using data from the Survey of Income and Program Participation. Using 401(k) eligibility as an instrument for 401(k) participation, we estimate the quantile treatment effects of participation in a 401(k) plan on several measures of wealth. The results show the effects of 401(k) participation on net financial assets are positive and significant over t…
High-Dimensional Methods and Inference on Structural and Treatment Effects
Data with a large number of variables relative to the sample size-"high-dimensional data"-are readily available and increasingly common in empirical economics. Highdimensional data arise through a combination of two phenomena. First, the data may be inherently high dimensional in that many different characteristics per observation are available. For example, the US Census collects information on hundreds of individual characteristics and scanner …
The Effects of 401(K) Participation on the Wealth Distribution
We use instrumental quantile regression approach to examine the effects of 401(k) plans on wealth using data from the Survey of Income and Program Participation. Using 401(k) eligibility as an instrument for 401(k) participation, we estimate the quantile treatment effects of participation in a 401(k) plan on several measures of wealth. The results show the effects of 401(k) participation on net financial assets are positive and significant over t…
An IV Model of Quantile Treatment Effects
The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles…
Instrumental quantile regression inference for structural and treatment effect models
Instrumental variable quantile regression
Estimation With Many Instrumental Variables
Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker to nonnormal disturbances, that adjust for many instruments. We find that this adjustment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors in t-ratios leads to an asymptotic approximation order that …
Identification of Marginal Effects in a Nonparametric Correlated Random Effects Model
In this paper, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing strong functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function and the assumption that at least three time periods are available for each cross sec…
A Penalty Function Approach to Bias Reduction in Nonlinear Panel Models with Fixed Effects
We consider estimation of nonlinear panel data models with individual specific fixed effects. Estimation of these models is complicated since estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of all model parameters. We present a penalized objective function that reduces the bias in the resulting point estimates. The penalty function is simple to construct and requires no modification for…
Instrumental Variables Estimation With Flexible Distributions
Instrumental variables are often associated with low estimator precision. This article explores efficiency gains that might be achievable using moment conditions that are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demon…
Plausibly Exogenous
Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We present practical methods for performing inference while relaxing the exclusion restriction. We illustrate the approaches with empirical examples that examine t…
Inference on Treatment Effects after Selection among High-Dimensional Controls
We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlle…
High-Dimensional Methods and Inference on Structural and Treatment Effects
Data with a large number of variables relative to the sample size-"high-dimensional data"-are readily available and increasingly common in empirical economics. Highdimensional data arise through a combination of two phenomena. First, the data may be inherently high dimensional in that many different characteristics per observation are available. For example, the US Census collects information on hundreds of individual characteristics and scanner …
Inference in High-Dimensional Panel Models With an Application to Gun Control
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relativ…
Prevalence of intimate partner violence and abuse and associated factors among women enrolled into a cluster randomised trial in northwestern Tanzania
ClinicalTrials.gov - ID NCT02592252 , registered retrospectively on 13 August 2015
Double/debiased machine learning for treatment and structural parameters
We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) method…
Pre-Event Trends in the Panel Event-Study Design
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference …
Clutch & Grasp
Presenting medical volume data using augmented reality (AR) can facilitate the identification of anatomical structures, the perception of their spatial relations and the development of mental maps compared to more commonly used monitors. However, interaction methods explored in these conventional settings may not be applicable in AR environments, or perform differently. In terms of mode activation, gestural interaction was shown to be a viable, t…
Targeted Undersmoothing
This paper proposes a procedure for assessing the sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By…
Model Averaging and Double Machine Learning
This paper discusses pairing double/debiased machine learning (DDML) with stacking , a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: Short‐stacking exploits the cross‐fitting step of DDML to substantially reduce the computational burden, and pooled stacking enforces common stacking weights over cross‐f…
Inference for Dependent Data with Learned Clusters
This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the p…
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
We study a panel data model with heterogeneous effects, allowing slopes to vary across individuals and time. To reduce dimensionality, we assume these slopes follow a factor structure, so slope matrices can be estimated via low-rank regularized regression. We propose a multi-step estimation procedure incorporating sample splitting and partialing-out to enable valid inference after penalized estimation. We establish the asymptotic normality of the…
Computer Science (17 works) · Econometrics (15 works) · Mathematics (13 works) · Statistical Methods and Inference (12 works) · Statistics (11 works) · Inference (10 works) · Economics (9 works) · Instrumental variable (9 works) · Advanced Causal Inference Techniques (8 works) · Statistical Methods and Bayesian Inference (6 works)