Network approaches to psychometric constructs, in which constructs are modeled in terms of
interactions between their constituent factors, have rapidly gained popularity in psychology …

We propose a procedure of iterating the HP filter to produce a smarter smoothing device,
called the boosted HP (bHP) filter, based on L2‐boosting in machine learning. Limit theory …

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary
autoregressions. This means that it estimates parameters consistently, selects the correct …

This paper concerns statistical inference for the components of a high-dimensional
regression parameter despite possible endogeneity of each regressor. Given a first-stage …

This article investigates the large sample properties of the variance, weights, and risk of high-
dimensional portfolios where the inverse of the covariance matrix of excess asset returns is …

We consider situations where a user feeds her attributes to a machine learning method that
tries to predict her best option based on a random sample of other users. The predictor is …

Evidence-based decision making often relies on meta-analyzing multiple studies, which
enables more precise estimation and investigation of generalizability. Integrative analysis of …

This paper studies high-dimensional vector autoregressions (VARs) augmented with
common factors that allow for strong cross-sectional dependence. Models of this type …

The global financial crisis and Covid‐19 recession have renewed discussion concerning
trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the …

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects
dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and …