This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a lower frequency (as a temporally aggregated or sequentially sampled variable),...

We investigate the consequences of discreteness in the assignment variable in regression-discontinuity designs for cases where the outcome variable is itself discrete. We find that constructing confidence intervals that have the correct level of coverage in these cases is sensitive to the...

We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average...

This paper is concerned with tests and confidence intervals for parameters that are not necessarily point identified and are defined by moment inequalities. In the literature, different test statistics, critical-value methods, and implementation methods (i.e., the asymptotic distribution versus...

This paper is concerned with tests and confidence intervals for partially-identified parameters that are defined by moment inequalities and equalities. In the literature, different test statistics, critical value methods, and implementation methods (i.e., asymptotic distribution versus the...

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global...

This paper presents a unified framework for inference on parameters of the form $\phi(\theta_0)$, where $\theta_0$ is unknown but can be estimated by $\hat\theta_n$, and $\phi$ is known with null first order derivative at $\theta_0$. We show the ``standard'' bootstrap is consistent if and only...

Indirect inference requires simulating realisations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of...

This paper introduces a new identification‐ and singularity‐robust conditional quasi‐likelihood ratio (SR‐CQLR) test and a new identification‐ and singularity‐robust Anderson and Rubin (1949) (SR‐AR) test for linear and nonlinear moment condition models. Both tests are very fast to...

We study nonparametric and regression estimators of treatment and spillover effects when interference is mediated by a network. Inference is nonstandard due to dependence induced by treatment spillovers and network correlated effects. We derive restrictions on the network degree distribution...