Martin Weidner

This paper considers inference on fixed effects in a linear regression model estimated from network data.

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 propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, when the model may be misspecified.

This paper considers inference on fixed effects in a linear regression model estimated from network data.

Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications.

This article reviews recent advances in fixed effect estimation of panel data models for long panels, where the number of time periods is relatively large.

This paper provides a method to construct simultaneous condfience 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 consider a situation where a distribution is being estimated by the empirical distribution of noisy measurements.

This article reviews recent advances in fixed effect estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time effects, where the distribution of the outcome variable conditional on covariates and unobserved effects is specified parametrically, while the distribution of the unobserved effects is left unrestricted.

This paper studies 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, which is a workhorse method in the analysis of matched data sets. Networks are typically quite sparse and it is difficult to see how the data carry information about certain parameters. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the structure of the network. These bounds depend on the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and on the degree structure of the network. The Laplacian is a matrix that describes the network and its smallest non-zero eigenvalue is a measure of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent estimation and convergence rates, and allow to evaluate the accuracy of first-order approximations to the variance of the fixed-effect estimator. The bounds are also used to assess the bias and variance of estimators of moments of the fixed effects.

Estimation of random coefficients logit demand models with interactive fixed effects

This paper studies inference on fixed effects in a linear regression model estimated from network data. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and of the degree structure of the network. The eigenvalue is a measure of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent estimation and convergence rates, and allow to evaluate the accuracy of first-order approximations to the variance of the fixed-effect estimator.

We derive fixed effects estimators of parameters and average partial effects in (possibly dynamic) nonlinear panel data models with individual and time effects.

This paper considers identifcation of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Because treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long run average treatment effects are not point identified. Instead we derive informative bounds on these average treatment effects. Our bounds do not impose (semi)parametric restrictions, as e.g. proportional hazards. We also explore various assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables.

In this paper, we study the least squares (LS) estimator in a linear panel regression model withunknown number of factors appearing as interactive fixed effects.

Fixed effects estimators of nonlinear panel data models can be severely biased because of the incidental parameter problem. In this paper, the authors develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects.

This paper considers identification of treatment effects on conditional transition probabilities.

This paper analyzes linear panel regression models with interactive fixed effects and predetermined regressors, e.g. lagged-dependent variables.