cox s regression model for counting processes a large sample study the cox regression model for censored survival data specifies that covariates have a proportional effect on the hazard function of the life time distribution of an individual in this paper we discuss how this model can be extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process this permits a statistical regression analysis of the intensity of a recurrent event allowing for complicated censoring patterns and time dependent covariates furthermore this formulation gives rise to proofs with very simple structure using martingale techniques for the asymptotic properties of the estimators from such a model finally an example of a statistical analysis is included

nonparametric estimation of distributional policy effects this paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest in contrast to other methods we do not restrict attention to the effect on the mean in particular our method can be used to conduct inference on the change of the distribution function as a whole its moments and quantiles inequality measures such as the lorenz curve or gini coefficient and to test for stochastic dominance the practical applicability of our procedure is illustrated via a simulation study and an empirical example

parametric links for binary choice models a fisherian bayesian colloquy abstract the familiar logit and probit models provide convenient settings for many binary response applications but a larger class of link functions may be occasionally desirable two parametric families of link functions are investigated the gosset link based on the student t latent variable model with the degrees of freedom parameter controlling the tail behavior and the pregibon link based on the generalized tukey family with two shape parameters controlling skewness and tail behavior both bayesian and maximum likelihood methods for estimation and inference are explored compared and contrasted in applications like the propensity score matching problem discussed below where it is critical to have accurate estimates of the conditional probabilities we find that misspecification of the link function can create serious bias bayesian point estimation via mcmc performs quite competitively with mle methods however nominal coverage of bayes credible regions is somewhat more problematic

inference on the quantile regression process tests based on the quantile regression process can be formulated like the classical kolmogorov smirnov and cramer von mises tests of goodness of fit employing the theory of bessel processes as in kiefer 1959 however it is frequently desirable to formulate hypotheses involving unknown nuisance parameters thereby jeopardizing the distribution free character of these tests we characterize this situation as the durbin problem since it was posed in durbin 1973 for parametric empirical processes in this paper we consider an approach to the durbin problem involving a martingale transformation of the parametric empirical process suggested by khmaladze 1981 and show that it can be adapted to a wide variety of inference problems involving the quantile regression process in particular we suggest new tests of the location shift and location scale shift models that underlie much of classical econometric inference the methods are illustrated with a reanalysis of data on unemployment durations from the pennsylvania reemployment bonus experiments the pennsylvania experiments conducted in 1988 89 were designed to test the efficacy of cash bonuses paid for early reemployment in shortening the duration of insured unemployment spells

uniform central limit theorems for kernel density estimators let mathbb p n ast k h n x n 1 h n d sum i 1 n k left x x i h n right be the classical kernel density estimator based on a kernel k and n independent random vectors x i each distributed according to an absolutely continuous law mathbb p on mathbb r d it is shown that the processes f longmapsto sqrt n int fd mathbb p n ast k h n mathbb p f in mathcal f converge in law in the banach space ell infty mathcal f for many interesting classes mathcal f of functions or sets some mathbb p donsker some just mathbb p pregaussian the conditions allow for the classical bandwidths h n that simultaneously ensure optimal rates of convergence of the kernel density estimator in mean integrated squared error thus showing that subject to some natural conditions kernel density estimators are plug in estimators in the sense of bickel and ritov ann statist 31 1033 1053 2003 some new results on the uniform central limit theorem for smoothed empirical processes needed in the proofs are also included

consistent tests for stochastic dominance methods are proposed for testing stochastic dominance of any pre specified order with primary interest in the distributions of income we consider consistent tests that are similar to kolmogorov smirnov tests of the complete set of restrictions that relate to the various forms of stochastic dominance for such tests in the case of tests for stochastic dominance beyond first order we propose and justify a variety of approaches to inference based on simulation and the bootstrap we compare these approaches to one another and to alternative approaches based on multiple comparisons in the context of a monte carlo experiment and an empirical example

identification and estimation of triangular simultaneous equations models without additivity this paper uses control variables to identify and estimate models with nonseparable multidimensional disturbances triangular simultaneous equations models are considered with instruments and disturbances that are independent and a reduced form that is strictly monotonic in a scalar disturbance here it is shown that the conditional cumulative distribution function of the endogenous variable given the instruments is a control variable also for any control variable identification results are given for quantile average and policy effects bounds are given when a common support assumption is not satisfied estimators of identified objects and bounds are provided and a demand analysis empirical example is given

instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings this paper reports estimates of the effects of jtpa training programs on the distribution of earnings the estimation uses a new instrumental variable iv method that measures program impacts on quantiles the quantile treatment effects qte estimator reduces to quantile regression when selection for treatment is exogenously determined qte can be computed as the solution to a convex linear programming problem although this requires first step estimation of a nuisance function we develop distribution theory for the case where the first step is estimated nonparametrically for women the empirical results show that the jtpa program had the largest proportional impact at low quantiles perhaps surprisingly however jtpa training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution

a boundary element algorithm using compatible boundary displacements and tractions with the aid of muskhelishvili s complex plane elasticity solution representation compatible displacement and stress fields are constructed the complex functions in these formulas are represented by cauchy integrals which are discretized along the boundary with the aid of complex shape functions for each boundary element the constructed displacement and stress functions satisfy the navier equations and the equilibrium equations respectively the use of fifth order complex basis functions with continuous second complex derivatives gives numerical results of high accuracy

an elementary approach to weak convergence for quantile processes with applications to censored survival data abstract let be a continuously differentiable function with positive derivative and let n be a sequence of right continuous increasing processes we show that if n 1 2 n w where w is continuous then n 1 2 1 n 1 w 1 1 this result is applied to classical processes such as the empirical distribution function the kaplan meier estimator and some other situations we also prove an analogous result for the bootstrapped version of n 1 2 n and show how this allows one to obtain confidence bands for the quantile function 1 based on the bootstrap several examples are given

inference on counterfactual distributions counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics in this article we develop modeling and inference tools for counterfactual distributions based on regression methods the counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates for either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression based estimators of the status quo and counterfactual outcome distributions these results allow us to construct simultaneous confidence sets for function valued effects of the counterfactual changes including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals these confidence sets can be used to test functional hypotheses such as no effect positive effect or stochastic dominance our theory applies to general counterfactual changes and covers the main regression methods including classical quantile duration and distribution regressions we illustrate the results with an empirical application to wage decompositions using data for the united states as a part of developing the main results we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution we show that distribution regression encompasses the cox duration regression and represents a useful alternative to quantile regression we establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals publication abstract

the sorted effects method discovering heterogeneous effects beyond their averages the partial ceteris paribus effects of interest in nonlinear and interactive linear models are heterogeneous as they can vary dramatically with the underlying observed or unobserved covariates despite the apparent importance of heterogeneity a common practice in modern empirical work is to largely ignore it by reporting average partial effects or at best average effects for some groups while average effects provide very convenient scalar summaries of typical effects by definition they fail to reflect the entire variety of the heterogeneous effects in order to discover these effects much more fully we propose to estimate and report sorted effects a collection of estimated partial effects sorted in increasing order and indexed by percentiles by construction the sorted effect curves completely represent and help visualize the range of the heterogeneous effects in one plot they are as convenient and easy to report in practice as the conventional average partial effects they also serve as a basis for classification analysis where we divide the observational units into most or least affected groups and summarize their characteristics we provide a quantification of uncertainty standard errors and confidence bands for the estimated sorted effects and related classification analysis and provide confidence sets for the most and least affected groups the derived statistical results rely on establishing key new mathematical results on hadamard differentiability of a multivariate sorting operator and a related classification operator which are of independent interest we apply the sorted effects method and classification analysis to demonstrate several striking patterns in the gender wage gap we find that this gap is particularly strong for married women ranging from 60 to 0 between the 2 and 98 percentiles as a function of observed and unobserved characteristics while the gap for never married women ranges from 40 to 20 the most adversely affected women tend to be married do not have college degrees work in sales and have high levels of potential experience

a resampling method based on pivotal estimating functions summary suppose that under a semiparametric model setting one is interested in drawing inferences about a finite dimensi onal parameter vector based on an estimating function generally a consistent point estimator j for 0 the true value for j can be easily obtained by finding a root of the corresponding estimating equation to estimate the variance of ft however may involve complicated and subjective nonparametric functional estimates in this paper a general and simple resampling method for inferences about js0 based on pivotal estimating functions is proposed the new procedure is illustrated with the quantile and rank regression models for both cases our proposal can be easily and efficiently implemented with existing statistical software

nonparametric policy analysis abstract this article considers the problem of predicting the mean effect of a change in the distribution of certain policy related variables on a dependent variable y this is conventionally done using a parametric model if however the conditional expectation of y given policy and nonpolicy variables x is unaltered by the policy intervention and if the support of x after the policy intervention lies within the support of x before the intervention then this analysis can be performed nonparametrically the proposed nonparametric estimator is developed for the model yj g xj a dj uj where g is a continuous unknown function of the continuous variables x dj is an m vector of dummy variables a is an m vector of unknown parameters representing fixed cell specific effects and uj is an error term with e uj xj dj 0 the estimand is b eg xj eg xj where x and x respectively denote the values of x before and after the policy intervention a nonparametric estimator bn is propo

censored regression quantiles abstract the object of this paper is to demonstrate how the lad estimation method for the censored regression model can be extended to more general quantiles in this paper the form of the conditional quantiles for the censored regression models is heuristically derived and discussed the resulting estimators of the regression coefficients which include the censored lad estimator as a special case are shown to be consistent and asymptotically normally distributed under appropriately translated versions of the corresponding assumptions for the former approach consistent estimation of the asymptotic covariance matrix when the error terms are i i d is also treated the paper discussed how several quantile estimators might be combined to improve efficiency when the error terms are identically distributed and how tests of homoskedasticity and symmetry of the error distribution can be constructed using particular differences of the estimated coefficients

bounds on distributional treatment effect parameters using panel data with an application on job displacement abstract this paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned i show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time i provide more identifying power for distributional treatment effect parameters than existing bounds and ii provide a more plausible set of conditions than existing methods that obtain point identification i apply these bounds to study heterogeneity in the effect of job displacement during the great recession using standard techniques i find that workers who were displaced during the great recession lost on average 34 of their earnings relative to their counterfactual earnings had they not been displaced using the methods developed in the current paper i also show that the average effect masks substantial heterogeneity across workers

tests for parameter instability and structural change with unknown change point this paper considers tests for parameter instability and structural change with unknown change point the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures the asymptotic distributions of the test statistics considered here are nonstandard because the change point parameter only appears under the alternative hypothesis and not under the null the tests considered here are shown to have nontrivial asymptotic local power against all alternatives for which the parameters are nonconstant the tests are found to perform quite well in a monte carlo experiment reported elsewhere copyright 1993 by the econometric society

treatment effects with censoring and endogeneity this article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework the article proposes a censored quantile treatment effects estimator derives its asymptotic distribution and illustrates its performance using monte carlo simulations even in the exogenous case the estimator performs better in finite samples than existing censored quantile regression estimators and performs nearly as well as maximum likelihood estimators in cases where their distributional assumptions hold an empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells especially at the right tail of the distribution

quantile regression under misspecification with an application to the u s wage structure quantile regression qr fits a linear model for conditional quantiles just as ordinary least squares ols fits a linear model for conditional means an attractive feature of ols is that it gives the minimum mean squared error linear approximation to the conditional expectation function even when the linear model is misspecified empirical research using quantile regression with discrete covariates suggests that qr may have a similar property but the exact nature of the linear approximation has remained elusive in this paper we show that qr minimizes a weighted mean squared error loss function for specification error the weighting function is an average density of the dependent variable near the true conditional quantile the weighted least squares interpretation of qr is used to derive an omitted variables bias formula and a partial quantile regression concept similar to the relationship between partial regression and ols we also present asymptotic theory for the qr process under misspecification of the conditional quantile function the approximation properties of qr are illustrated using wage data from the u s census these results point to major changes in inequality from 1990 to 2000

efficient semiparametric estimation of quantile treatment effects this paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics identification is achieved without requiring computation of the conditional quantiles of the potential outcomes instead the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems root n consistency asymptotic normality and achievement of the semiparametric efficiency bound are shown for that estimator a consistent estimation procedure for the variance is also presented finally the method developed here is applied to evaluation of a job training program and to a monte carlo exercise results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates the monte carlo study shows that for a relatively small sample size the method produces estimates with good precision and low bias especially for middle quantiles

efficient estimation of average treatment effects using the estimated propensity score we are interested in estimating the average effect of a binary treatment on a scalar outcome if assignment to the treatment is exogenous or unconfounded that is independent of the potential outcomes given covariates biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates rosenbaum and rubin 1983 show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates although adjusting for differences in the propensity score removes all the bias this can come at the expense of efficiency as shown by hahn 1998 heckman ichimura and todd 1998 and robins mark and newey 1992 we show that weighting by the inverse of a nonparametric estimate of the propensity score rather than the true propensity score leads to an efficient estimate of the average treatment effect we provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score

the conditional distribution of excess returns an empirical analysis abstract in this article we describe the cumulative distribution function of excess returns conditional on a broad set of predictors that summarize the state of the economy we do so by estimating a sequence of conditional logit models over a grid of values of the response variable our method uncovers higher order multidimensional structure that cannot be found by modeling only the first two moments of the distribution we compare two approaches to modeling one based on a conventional linear logit model and the other based on an additive logit the second approach avoids the curse of dimensionality problem of fully nonparametric methods while retaining both interpretability and the ability to let the data determine the shape of the relationship between the response variable and the predictors we find that the additive logit fits better and reveals aspects of the data that remain undetected by the linear logit the additive model retains its superiority even in out of sample prediction and portfolio s

a quantile correlated random coefficients panel data model abstract we propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data specifically we extend the correlated random coefficients representation of linear quantile regression e g koenker 2005 section 2 6 we show that panel data allows the econometrician to i introduce dependence between the regressors and the random coefficients and ii weaken the assumption of comonotonicity across them i e to enrich the structure of allowable dependence between different coefficients we adopt a fixed effects approach leaving any dependence between the regressors and the random coefficients unmodeled we motivate different notions of quantile partial effects in our model and study their identification for the case of discretely valued covariates we present analog estimators and characterize their large sample properties when the number of time periods t exceeds the number of random coefficients p identification is regular and our estimates are n consistent when t p our identification results make special use of the subpopulation of stayers units whose regressor values change little over time in a way which builds on the approach of graham and powell 2012 in this just identified case we study asymptotic sequences which allow the frequency of stayers in the population to shrink with the sample size one purpose of these discrete bandwidth asymptotics is to approximate settings where covariates are continuously valued and as such there is only an infinitesimal fraction of exact stayers while keeping the convenience of an analysis based on discrete covariates when the mass of stayers shrinks with n identification is irregular and our estimates converge at a slower than n rate but continue to have limiting normal distributions we apply our methods to study the effects of collective bargaining coverage on earnings using the national longitudinal survey of youth 1979 nlsy79 consistent with prior work e g chamberlain 1982 vella and verbeek 1998 we find that using panel data to control for unobserved worker heterogeneity results in sharply lower estimates of union wage premia we estimate a median union wage premium of about 9 percent but with in a more novel finding substantial heterogeneity across workers the 0 1 quantile of union effects is insignificantly different from zero whereas the 0 9 quantile effect is of over 30 percent our empirical analysis further suggests that on net unions have an equalizing effect on the distribution of wages

minimum distance from independence estimation of nonseparable instrumental variables models abstract i develop a semiparametric minimum distance from independence estimator for a nonseparable instrumental variables model an independence condition identifies the model for many types of discrete and continuous instruments the estimator is taken as the parameter value that most closely satisfies this independence condition implementing the estimator requires a quantile regression of the endogenous variables on the instrument so the procedure is two step with a finite or infinite dimensional nuisance parameter in the first step i prove consistency and establish asymptotic normality for a parametric but flexibly nonlinear outcome equation the consistency of the nonparametric bootstrap is also shown i illustrate the use of the estimator by estimating the returns to schooling using data from the 1979 national longitudinal survey

application of the cvbem to non uniform st venant torsion abstract the complex variable boundary element method or cvbem is used to approximate the stress distribution associated with non uniform st venant torsion problems by specifying either the normal or tangential force equilibrium equation in terms of the warping function or its conjugate a laplace equation results which is immediately tractable by using the cvbem a comparison of modeling results to known solutions indicates that the modeling technique is a useful approach for the estimation of interior stresses as well as for the evaluation of modeling error by means of an approximate boundary determined by the cvbem approximation function

universal donsker classes and metric entropy when mathfrak f is a universal donsker class then for independent indetically distributed i i d observation mathbf x 1 ldots mathbf x n with an unknown law p for any mathfrak f i in mathfrak f i 1 ldots m quad n 1 2 left mathfrak f 1 left mathbf x 1 right ldots mathfrak f i left mathbf x n right right 1 leq i leq m is asymptotically normal with mean vector n 1 2 left int mathfrak f i left mathbf x n right d mathbf p left x right right 1 leq i leq m and covariance matrix int mathfrak f i mathfrak f j d mathbf p int mathfrak f id mathbf p int mathfrak f jd mathbf p uniformly for mathfrak f i in mathfrak f then for certain statistics formed frome the mathfrak f i left mathbf x k right even where mathfrak f i may be chosen depending on the mathbf x k there will be asymptotic distribution as n rightarrow infty for example for mathbf x 2 statistics where f i are indicators of disjoint intervals depending suitably on mathbf x 1 ldots mathbf x n whose union is the real line mathbf x 2 quadratic forms have limiting distributions roy 1956 and watson 1958 which may however not be mathbf x 2 distributions and may depend on p chernoff and lehmann 1954 universal donsker classes of sets are up to mild measurability conditions just classes satisfying the vapnik cervonenkis comdinatorial conditions defined later in this section donsker the vapnik cervonenkis combinatorial conditions defined later in this section durst and dudley 1981 and dudley 1984 chapter 11 the use of such classes allows a variety of extensions of the roy watson results to general multidimensional sample spaces pollard 1979 and moore and subblebine 1981 vapnik and cervonenkis 1974 indicated application of their families of sets to classification pattern recognition problems more recently the classes have been applied to tree structured classifiacation breiman friedman olshen and stone 1984 chapter 12

confidence bands for the median survival time as a function of the covariates in the cox model abstract let p x be the pth quantile of the distribution of the life length of an individual with covariate vector x in the cox model we introduce an estimator p x of p x and develop several families of confidence bands for x as a function of x to construct one type of band we proceed as follows we show that as n where n is the number of individuals in the study converges weakly to a gaussian process w x with a complicated covariance structure we then estimate this covariance structure from the data and simulate many gaussian processes with this estimated covariance structure the critical constants required for the construction of the confidence bands are obtained from the simulated processes another type of bands is obtained by bootstrapping we obtain an asymptotic theory for both types of bands simulation studies are used to compare the two types of bands the methods are illustrated on the stanford heart transplant data

chi square diagnostic tests for econometric models theory this paper extends the pearson chi square testing method to nondynamic parametric econometric models in particular to models with covariates the paper establishes the asymptotic distribution of the test statistic under the null and local alternatives when the test statistic is based on data dependent random cells of a general form and on an arbitrary asymptotically normal estimator these results are attained by extending recent probabilistic results for the weak convergence of empirical processes indexed by sets the chi square test that is introduced can be used to test goodness of fit of a parametric model as well as to test particular aspects of the parametric model that are of interest

quantile and probability curves without crossing this paper proposes a method to address the longstanding problem of lack of monotonicity in estimation of conditional and structural quantile functions also known as the quantile crossing problem bassett and koenker 1982 the method consists in sorting or monotone rearranging the original estimated non monotone curve into a monotone rearranged curve we show that the rearranged curve is closer to the true quantile curve than the original curve in finite samples establish a functional delta method for rearrangement related operators and derive functional limit theory for the entire rearranged curve and its functionals we also establish validity of the bootstrap for estimating the limit law of the entire rearranged curve and its functionals our limit results are generic in that they apply to every estimator of a monotone function provided that the estimator satisfies a functional central limit theorem and the function satisfies some smoothness conditions consequently our results apply to estimation of other econometric functions with monotonicity restrictions such as demand production distribution and structural distribution functions we illustrate the results with an application to estimation of structural distribution and quantile functions using data on vietnam veteran status and earnings

identification in nonseparable models weak nonparametric restrictions are developed sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable these derivatives can exhibit stochastic variation in a microeconometric context this allows the impact of a policy intervention as measured by the value of a structural derivative to vary across people who are identical as measured by covariates when the restrictions are satisfied quantiles of the distribution of a policy impact across people can be identified the identification restrictions are local in the sense that they are specific to the values of the covariates and the specific quantiles of latent variables at which identification is sought the conditions do not include the commonly required independence of latent variables and covariates they include local versions of the classical rank and order conditions and local quantile insensitivity conditions values of structural derivatives are identified by functionals of quantile regression functions and can be estimated using the same functionals applied to estimated quantile regression functions

quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods abstract this paper shows that the conditional quantile treatment effect on the treated is identified under i a conditional distributional difference in differences assumption and ii a new assumption that the dependence the copula between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group we consider estimation and inference with discrete covariates and propose a uniform inference procedure based on the exchangeable bootstrap finally we estimate the effect of increasing the minimum wage on the distribution of earnings for subgroups defined by race gender and education

estimating derivatives of function valued parameters in a class of moment condition models abstract we develop a general approach to estimating the derivative of a function valued parameter o u that is identified for every value of u as the solution to a moment condition this setup in particular covers interesting models for conditional distributions such as quantile regression or distribution regression exploiting that o u solves a moment condition we obtain an explicit expression for its derivative from the implicit function theorem and then estimate the components of this expression by suitable sample analogues the last step generally involves local linear smoothing of the empirical moment condition our estimators can then be used for a variety of purposes including the estimation of conditional density functions quantile partial effects and the distribution of bidders valuations in structural auction models

network and panel quantile effects via distribution regression abstract this paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two way effects strictly exogenous covariates and possibly discrete outcome variables the method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators these fixed effects estimators are debiased to deal with the incidental parameter problem under asymptotic sequences where both dimensions of the data set grow at the same rate the confidence bands for the quantile functions and effects have correct joint coverage in large samples an empirical application to gravity models of trade illustrates the applicability of the methods to network data

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 of economic outcomes in order to address this problem we develop a model of quantile treatment effects qte in the presence of endogeneity and obtain conditions for identification of the qte without functional form assumptions the principal feature of the model is the imposition of conditions that restrict the evolution of ranks across treatment states this feature allows us to overcome the endogeneity problem and recover the true qte through the use of instrumental variables the proposed model can also be equivalently viewed as a structural simultaneous equation model with nonadditive errors where qte can be interpreted as the structural quantile effects sqe

partial distributional policy effects in this paper we propose a method to evaluate the effect of a counterfactual change in the unconditional distribution of a single covariate on the unconditional distribution of an outcome variable of interest both fixed and infinitesimal changes are considered we show that such effects are point identified under general conditions if the covariate affected by the counterfactual change is continuously distributed but are typically only partially identified if its distribution is discrete for the latter case we derive informa tive bounds making use of the available information we also discuss estimation and inference

efficient propensity score regression estimators of multivalued treatment effects for the treated abstract when a multivalued treatment is randomly assigned conditional on observables valid causal comparisons for a subpopulation treated at a particular treatment level require two propensity scores one for the treated level and one for the counterfactual level we contribute efficient propensity score regression estimators to a class of treatment effects for the treated under the cases when the propensity scores are unknown and when they are known our efficient estimator matches on a normalized propensity score that combines the true propensity score and the nonparametric estimate our asymptotic theory takes into account that the propensity scores are nonparametrically or parametrically estimated as generated regressors

instrumental quantile regression inference for structural and treatment effect models we introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference these methods can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes we describe an estimator of the instrumental variable quantile regression process and the set of inference procedures derived from it we focus our discussion of inference on tests of distributional equality constancy of effects conditional dominance and exogeneity we apply the procedures to characterize the returns to schooling in the u s

three step censored quantile regression and extramarital affairs this article suggests very simple three step estimators for censored quantile regression models with a separation restriction on the censoring probability the estimators are theoretically attractive i e asymptotically as efficient as the celebrated powell s censored least absolute deviation estimator at the same time they are conceptually simple and have trivial computational expenses they are especially useful in samples of small size or models with many regressors with desirable finite sample properties and small bias the separation restriction costs a small reduction of generality relative to the canonical censored regression quantile model yet its main plausible features remain intact the estimator can also be used to estimate a large class of traditional models including the normal amemiya tobin model and many accelerated failure and proportional hazard models we illustrate the approach with an extramarital affairs example and contrast our findings with those of fair

methods for estimating a conditional distribution function abstract motivated by the problem of setting prediction intervals in time series analysis we suggest two new methods for conditional distribution estimation the first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation it produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1 the second method involves an adjusted form of the nadaraya watson estimator it preserves the bias and variance properties of a class of second order estimators introduced by yu and jones but has the added advantage of always being a distribution itself our methods also have application outside the time series setting for example to quantile estimation for independent data this problem motivated the work of yu and jones

inference on inequality from household survey data abstract i develop a theory of asymptotic inference for the lorenz curve and the gini coefficient for testing economic inequality when the data come from stratified and clustered household surveys with large number of clusters per stratum using the asymptotic framework of bhattacharya asymptotic inference from multi stage surveys journal of econometrics 126 1 145 171 i derive a weak convergence result for the continuously indexed lorenz process even when the underlying density is not uniformly bounded away from zero i provide analytical formulae for the asymptotic covariance functions that are corrected for both stratification and clustering and develop consistent tests for lorenz dominance inference on the gini coefficient follows as a corollary the methods are applied to per capita household expenditure data from the complexly designed indian national sample survey to test for changes in inequality before and after the reforms of the early 1990s ignoring the survey design is seen to produce qualitatively different results especially in the urban sector where the population sorts more completely into rich and poor neighborhoods

boundary element discretization of plane elasticity and plate bending problems particular attention is paid to the efficiency of the interpolation used in approximating the boundary quantities and to the precision and computational convenience in evaluating the boundary integrals the proposed discretization model is based on the use of a quadratic b spline approximation to represent the boundary variables and on the results from the analytical integration to compute the boundary coefficients