Research methods

‘Randomisation bias’ in the medical literature: a review

This paper considers the micro-econometric analysis of patients' hospital choice for elective medical procedures when their choice set is pre-selected by a general practitioner (GP).

We propose a Kronecker product structure for large covariance or correlation matrices.

Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables.

Community-based interventions, particularly group-based ones, are considered to be a cost-effective way of delivering interventions in low-income settings. However, design features of these programs could also influence dimensions of household and community behaviour beyond those targeted by the intervention. This paper studies spillover effects of a participatory community health intervention in rural Malawi, implemented through a cluster randomised control trial, on an outcome not directly targeted by the intervention: household consumption smoothing after crop losses.

Dynamic portfolio choice has been a central and essential objective for investors in active asset management.

This paper models the use of statistical hypothesis testing in regulatory approval.

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well.

The ill-posedness of the inverse problem of recovering a regression function in a nonparametric instrumental variable (NPIV) model leads to estimators that may suffer from poor statistical performance. In this paper, we explore the possibility of imposing shape restrictions to improve the performance of the NPIV estimators.

In this paper, we derive a rate of convergence of the Lasso estimator when the penalty parameter  for the estimator is chosen using K-fold cross-validation

We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting.

The so-called leverage hypothesis is that negative shocks to prices/returns affect volatility more than equal positive shocks.

We propose an alternative Ratio Statistic for measuring predictability of stock prices. Our statistic is based on actual returns rather than logarithmic returns and is therefore better suited to capturing price predictability.

Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special case, for performing inference on the entire conditional quantile function and its linear functionals.

Modern construction of uniform confidence bands for nonpara-metric densities (and other functions) often relies on the classical Smirnov-Bickel-Rosenblatt (SBR) condition; see, for example, Giné and Nickl (2010). This condition requires the existence of a limit distribution of an extreme value type for the supremum of a studentized empirical process (equivalently, for the supremum of a Gaussian process with the same covariance function as that of the studentized empirical process). The principal contribution of this paper is to remove the need for this classical condition.

In this paper, we derive central limit and bootstrap theorems for probabilities that centered high-dimensional vector sums hit rectangles and sparsely convex sets.

This paper develops characterizations of identifi…ed sets of structures and structural features for complete and incomplete models involving continuous or discrete variables.

Slepian and Sudakov-Fernique type inequalities, which compare expectations of maxima of Gaussian random vectors under certain restrictions on the covariance matrices, play an important role in probability theory, especially in empirical process and extreme value theories. Here we give explicit comparisons of expectations of smooth functions and distribution functions of maxima of Gaussian random vectors without any restriction on the covariance matrices.

This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical processes in the sup-norm.