Methods

Likelihood inference and the role of initial conditions for the dynamic panel data model

Inference in linear regression models with many covariates and heteroskedasticity

A bootstrap method for constructing pointwise and uniform confidence bands for conditional quantile functions

Identifying preferences in networks with bounded degree

We consider a class of nonparametric time series regression models in which the regressor takes values in a sequence space and the data are stationary and weakly dependent.

‘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.

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.

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

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.

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

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.

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

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.