Browse Items (8286 total)

For randomized controlled trials (RCTs) with a single intervention’s impact being measured on multiple outcomes, researchers often apply a multiple testing procedure (such
as Bonferroni or Benjamini-Hochberg) to adjust p values. Such an adjustment…

Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models. Empirical likelihoodbased tests are asymptotically pivotal and thus avoid explicit studentization.…

This article introduces the Python package gcimpute for missing data imputation.
Package gcimpute can impute missing data with many different variable types, including
continuous, binary, ordinal, count, and truncated values, by modeling data as…

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
It provides functionalities to estimate parameters in causal models based on machine…

Market models constitute a significant cornerstone of empirical applications in business, industrial organization, and policymaking macroeconomics. The econometric literature proposes various estimation methods for markets in equilibrium, which…

The tipsae package implements a set of small area estimation tools for mapping proportions and indicators defined on the unit interval. It provides for small area models
defined at area level, including the classical beta regression, zero- and/or…

The existence of latent clusters with different responses to a treatment is a major
concern in scientific research, as latent effect heterogeneity often emerges due to latent
or unobserved features – e.g., genetic characteristics, personality…

This paper introduces software packages for efficiently imputing missing data using
deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement
a recently developed approach to multiple imputation known as MIDAS, which…

Despite the huge availability of software to estimate cross-sectional spatial models,
there are only few functions to estimate models dealing with spatial limited dependent
variable. This paper fills this gap introducing the new R package spldv.…

We develop an R package panelView and a Stata package panelview for panel data
visualization. They are designed to assist causal analysis with panel data and have three
main functionalities: (1) They plot the treatment status and missing values in…