Martin Weidner

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure).

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption.

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable.

This paper studies linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects.

Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models.

We study the incidental parameter problem for the “three-way” Poisson Pseudo-Maximum Likelihood (PPML) estimator recently recommended for identifying the effects of trade policies and in other panel data gravity settings.

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations.

We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data.

Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average partial effects in discrete choice models, and counterfactual simulations in structural models.

We propose a framework for estimation and inference when the model may be misspecified.

This paper builds on Bonhomme (2012) to develop a method to systematically construct moment conditions for dynamic panel data logit models with fixed effects.

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.

We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood "PPML" gravity models recently recommended for identifying the effects of trade policies.

This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data.

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable.

Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average effects in discrete choice models, or counterfactual simulations in structural models.