Rosalba Radice

Testing Exogeneity in the Bivariate Probit Model: A Monte Carlo Study

We conduct an extensive Monte Carlo experiment to examine the finite sample properties of maximum-likelihood-based inference in the bivariate probit model with an endogenous dummy. We analyse the relative performance of alternative exogeneity tests, the impact of distributional misspecification and the role of exclusion restrictions to achieve parameter identification in practice. The results allow us to infer important guidelines for applied econometric practice.

Penalised regression splines: theory and application to medical research

Generalised additive models (GAMs) allow for flexible functional dependence of a response variable on covariates. The aim of this article is to provide an accessible overview of GAMs based on the penalised likelihood approach with regression splines. In contrast to the classical backfitting, the penalised likelihood framework taken here provides researchers with an efficient computational method for automatic multiple smoothing parameter selection, which can determine the functional form of any relationship from the data. We illustrate through an example how the use of this methodology can help to gain insights into medical research.

Copula regression spline models for binary outcomes

We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. Non-linear dependence between treatment and outcome is dealt using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach. Related model fitting and inferential procedures are developed, and asymptotic arguments presented.

A penalized likelihood estimation approach to semiparametric sample selection binary response modeling

Sample selection models are employed when an outcome of interest is observed for a restricted non-randomly selected sample of the population. We consider the case in which the response is binary and continuous covariates have a nonlinear relationship to the outcome. We introduce two statistical methods for the estimation of two binary regression models involving semiparametric predictors in the presence of non-random sample selection. This is achieved using a multiple-stage procedure, and a newly developed simultaneous equation estimation scheme. Both approaches are based on the penalized likelihood estimation framework. The problems of identification and inference are also discussed. The empirical properties of the proposed approaches are studied through a simulation study. The methods are then illustrated using data from the American National Election Study where the aim is to quantify public support for school integration. If non-random sample selection is neglected then the predicted probability of giving, for instance, a supportive response may be biased, an issue that can be tackled using the proposed tools.

A Flexible Instrumental Variable Approach

Classical regression model literature has generally assumed that measured and unmeasured covariates are statistically independent. For many applications, this assumption is clearly tenuous. When unobservables are associated with included regressors and have an impact on the response, standard estimation methods will not be valid. This means that estimation results from observational studies, whose aim is to evaluate the impact of a treatment of interest on a response variable, will be biased and inconsistent in the presence of unmeasured confounders. One method for obtaining consistent estimates of treatment effects when dealing with linear models is the instrumental variable (IV) approach. Linear models have been extended to generalized linear models (GLMs) and generalized additive models (GAMs), and although IV methods have been proposed to deal with GLMs, fitting methods to carry out IV analysis within the GAM context have not been developed. We propose a simple but effective two-stage procedure for IV estimation when dealing with GAMs represented using any penalized regression spline approach and a correction procedure for confidence intervals. We explain under which conditions the proposed method works and illustrate its empirical validity through an extensive simulation experiment and a health study where unmeasured confounding is suspected to be present.

Estimation of a regression spline sample selection model

It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of two regressions: a binary selection equation determining whether a particular statistical unit will be available in the outcome equation. Classic sample selection models assume a priori that continuous regressors have a pre-specified linear or non-linear relationship to the outcome, which can lead to erroneous conclusions. In the case of continuous response, methods in which covariate effects are modeled flexibly have been previously proposed, the most recent being based on a Bayesian Markov chain Monte Carlo approach. A frequentist counterpart which has the advantage of being computationally fast is introduced. The proposed algorithm is based on the penalized likelihood estimation framework. The construction of confidence intervals is also discussed. The empirical properties of the existing and proposed methods are studied through a simulation study. The approaches are finally illustrated by analyzing data from the RAND Health Insurance Experiment on annual health expenditures.

Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach

Abstract Propensity score (Pscore) matching and inverse probability of treatment weighting (IPTW) can remove bias due to observed confounders, if the Pscore is correctly specified. Genetic Matching (GenMatch) matches on the Pscore and individual covariates using an automated search algorithm to balance covariates. This paper compares common ways of implementing Pscore matching and IPTW, with Genmatch for balancing time-constant baseline covariates}. The methods are considered when estimates of treatment effectiveness are required for patient subgroups, and the treatment allocation process differs by subgroup. We apply these methods in a prospective cohort study that estimates the effectiveness of Drotrecogin alfa activated, for subgroups of patients with severe sepsis. In a simulation study we compare the methods when the Pscore is correctly specified, and then misspecified by ignoring the subgroup-specific treatment allocation. The simulations also consider poor overlap in baseline covariates, and different sample sizes. In the case study, GenMatch reports better covariate balance than IPTW or Pscore matching. In the simulations with correctly specified Pscores, good overlap and reasonable sample sizes, all methods report minimal bias. When the Pscore is misspecified, GenMatch reports the least imbalance and bias. With small sample sizes, IPTW is the most efficient approach, but all methods report relatively high bias of treatment effects. This study shows that overall GenMatch achieves the best covariate balance for each subgroup, and is more robust to Pscore misspecification than common alternative Pscore approaches.

A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses

ABSTRACT Estimates of HIV prevalence are important for policy to establish the health status of a country’s population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using conventional approaches to deal with missing data, such as imputation-based methods, will be biased. We develop a Heckman-type simultaneous equation approach that accounts for nonignorable selection, but unlike previous implementations, allows for spatial dependence and does not impose a homogenous selection process on all respondents. In addition, our framework addresses the issue of separation, where for instance some factors are severely unbalanced and highly predictive of the response, which would ordinarily prevent model convergence. Estimation is carried out within a penalized likelihood framework where smoothing is achieved using a parameterization of the smoothing criterion, which makes estimation more stable and efficient. We provide the software for straightforward implementation of the proposed approach, and apply our methodology to estimating national and sub-national HIV prevalence in Swaziland, Zimbabwe, and Zambia. Supplementary materials for this article are available online.

Bivariate copula additive models for location, scale and shape

Rigby & Stasinopoulos (2005) introduced generalized additive models for location, scale and shape (GAMLSS) where the response distribution is not restricted to belong to the exponential family and its parameters can be specified as functions of additive predictors that allows for several types of covariate effects (e.g., linear, non-linear, random and spatial effects). In many empirical situations, however, modeling simultaneously two or more responses conditional on some covariates can be of considerable relevance. In this article, we extend the scope of GAMLSS by introducing a bivariate copula additive model with continuous margins for location, scale and shape. The framework permits the copula dependence and marginal distribution parameters to be estimated simultaneously and, like in GAMLSS, each parameter to be modeled using an additive predictor. Parameter estimation is achieved within a penalized likelihood framework using a trust region algorithm with integrated automatic multiple smoothing parameter selection. The proposed approach allows for straightforward inclusion of potentially any parametric continuous marginal distribution and copula function. The models can be easily used via the copulaReg() function in the R package SemiParBIVProbit. The usefulness of the proposal is illustrated on two case studies (which use electricity price and demand data, and birth records) and on simulated data.

Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching

Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maximum likelihood estimation is a double-robust method designed to reduce bias in the estimate of the parameter of interest. Bias-corrected matching reduces bias due to covariate imbalance between matched pairs by using regression predictions. We illustrate the methods in an evaluation of different types of hip prosthesis on the health-related quality of life of patients with osteoarthritis. We undertake a simulation study, grounded in the case study, to compare the relative bias, efficiency and confidence interval coverage of the methods. We consider data generating processes with non-linear functional form relationships, normal and non-normal endpoints. We find that across the circumstances considered, bias-corrected matching generally reported less bias, but higher variance than targeted maximum likelihood estimation. When either targeted maximum likelihood estimation or bias-corrected matching incorporated machine learning, bias was much reduced, compared to using misspecified parametric models.