We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For...

We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variance of the measurement errors is a fraction of that of the mismeasured variables, which is typical for empirical...

A recent literature considers the identification of heterogeneous demand and supply models via "quasi-experimental'' variation, as from instrumental variables. In this paper we establish nonparametric identification of differentiated products demand when one has "micro data'' linking...

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in...

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variability of the EIV is a fraction of that of the mismeasured variables, which is typical for empirical applications. For...

Much of the analysis of panel data has been based on an assumption of strict exogeneity. Distributions are specified for outcome variables conditional on a latent individual effect and conditional on observed predictor variables at all dates, with the future values of the predictor variables...

Much of the analysis of panel data has been based on an assumption of strict exogeneity. Distributions are specified for outcome variables conditional on a latent individual effect and conditional on observed predictor variables at all dates, with the future values of the predictor variables...

We develop an estimator and tests of a discrete time mixed proportional hazard (MPH) model of duration with unobserved heterogeneity. We allow for competing risks, observable characteristics, and censoring, and we use linear GMM, making estimation and inference straightforward. With repeated...

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in...

This paper considers a semiparametric threshold regression model with two threshold variables,extending Chen et al. (2012) and Kourtellos et al. (2021). The proposed model allows the endogeneity for both threshold variables and the slope regressors. Under the diminishing thresholdeffects...

We consider shape constrained kernel-based probability density function (PDF) and probability mass function (PMF) estimation. Our approach is of widespread potential applicability and includes, separately or simultaneously, constraints on the PDF (PMF) function itself, its integral (sum), and...

In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse-selection models. Despite significant data limitations, we are able to derive informative bounds on demand...

A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts...

A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts...

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal parameters that can be identified as linear functionals of the conditional expectation function of the outcome. Such functionals encompass many of the traditional targets of...

USDA operates several food assistance programs aimed at alleviating food insecurity. We study whether participation in both SNAP and WIC alleviates food insecurity compared with participation in SNAP alone. We bound underlying causal effects by applying nonparametric treatment effect methods...

In a "common values" environment, some market participants have private information relevant to others' assessments of their own valuations or costs. Economic theory shows that this type of informational asymmetry can have important implications for market performance and market design. Yet even...

USDA operates several food assistance programs aimed at alleviating food insecurity. We study whether participation in both SNAP and WIC alleviates food insecurity compared with participation in SNAP alone. We bound underlying causal effects by applying nonparametric treatment effect methods...

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in...

This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise naturally in industrial organization and labor economics settings where data are collected using an "input-based sampling" strategy, e.g., if the sampling unit is one...

We develop an estimator and tests of a discrete time mixed proportional hazard (MPH) model of duration with unobserved heterogeneity. We allow for competing risks, observable characteristics, and censoring, and we use linear GMM, making estimation and inference straightforward. With repeated...

Unlike random sampling, selective sampling draws units based on the outcome values, such as over-sampling rare events in choice outcomes and extreme activities on continuous and count outcomes. Despite high cost effectiveness for marketing research, such endogenously selected samples must be...

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an...

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an...

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an...

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an...

We study the nonparametric identification of gross output production functions under the environment of the commonly employed proxy variable methods. We show that applying these methods to gross output requires additional sources of variation in the demand for flexible inputs (e.g., prices)....

This paper studies nonparametric estimation of the infinite order regression $E(Y_t^k|\mathcal{F}_{t-1}), k\in\mathbb{Z}$ with stationary and weakly dependent data. We propose a Nadaraya-Watson type estimator that operates with an infinite number of conditioning variables. We propose a bandwidth...

In this paper we introduce a non-parametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information...