Methods

We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence.

This paper studies the relationship between group size and informal risk sharing. It shows that under limited commitment with coalitional deviations, this relationship is theoretically ambiguous.

Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment eects can be identied in a more general class of models that allows for multidimensional unobserved heterogeneity.

This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to the sample size.

The paper deals with panel co-operation in a cross-national, fully harmonized face to-face survey. Our outcome of interest is panel co-operation in the fourth wave of the Survey of Health, Ageing and Retirement in Europe.

We show that the identification results of finite mixture and misclassification models are equivalent in a widely-used scenario except an extra ordering assumption. In the misclassification model, an ordering condition is imposed to pin down the precise values of the latent variable, which are also of researchers' interests and need to be identified.

In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects.

Many questions in econometrics can be cast as inference on a parameter selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data.

Better governance, better access: practising responsible data sharing in the METADAC governance infrastructure

We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative with respect to first step nonparametric estimation is zero and equivalently first step estimation has no effect on the influence function.

The multinomial logit model with random coefficients is widely used in applied research. This paper is concerned with estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters.

This paper characterizes and proposes a method to correct for errors-in-variables biases in the estimation of rank correlation coeffcients (Spearman's ρ and Kendall's τ).

This paper studies the properties of the wild bootstrap-based test proposed in Cameron et al. (2008) in settings with clustered data.

This paper explores the effects of simulated moments on the performance of inference methods based on moment inequalities.

We propose simultaneous mean-variance regression for the linear estimation and approximation of conditional mean functions.

We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

This paper studies the properties of generalised empirical likelihood (GEL) methods for the estimation of and inference on partially identified parameters in models specified by unconditional moment inequality constraints.

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.

In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing the continuity of the density of the running variable at the cut-off, e.g., McCrary (2008). In this paper we propose a new test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under a novel asymptotic framework.