Econometrics and Statistics

Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approxim [...]

Negative probabilities arise primarily in physics, statistical quantum mechanics, and quantum computing. Negative probabilities arise as mixing distributions of unobserve [...]

This is the replication repository for the paper "Model averaging and double machine learning" by Achim Ahrens, Christian Hansen, Mark Schaffer and Thomas Wiemann (Journa [...]

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate struct [...]

Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompas [...]

Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free g [...]

Default options may provide a low-cost way of influencing behaviour without modifying incentives and constraining choices between alternatives. However, an improved under [...]

Many sentient beings suffer serious harms due to a lack of moral consideration. Importantly, such harms could also occur to a potentially astronomical number of morally c [...]

In this paper, we introduce a statistical method for examining and adjusting chemical-transport models. We illustrate the findings with total column ozone predictions, [...]

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular different [...]

We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method [...]

Covariate distribution shifts and adversarial perturbations present robustness challenges to the conventional statistical learning framework: mild shifts in the test cova [...]

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceasel [...]

In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are su [...]

Contains the code and datasets necessary to replicate all figures and tables in the paper.

Note: One dataset on the "Gaokao experiment" (Section 6.2 of main [...]

Measuring the effect of peers on individuals' outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and uno [...]

No abstract

The pystacked command implements stacked generalization (Wolpert, 1992, Neural Networks 5: 241–259) for regression and binary classification via Python’s scikit-learn [...]

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem betwe [...]

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation le [...]

This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstra [...]

A security price volatility estimator that is capable of accurately estimating price volatility in real-time or near real-time, and in low noise and high noise environmen [...]

Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodimen [...]

We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet (color, piece, square) as the input and [...]

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the co [...]