Browse Items (12 total)

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…

Certain events can make the structure of volatility of financial returns to change,
making it nonstationary. Models of time-varying conditional variance such as generalized
autoregressive conditional heteroscedasticity (GARCH) models usually assume…

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 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…

Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent,
and their analysis is needed in a variety of disciplines. FRK is an R package for spatial
and spatio-temporal modeling and prediction with very large data sets that,…

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…

Two common issues arise in regression modelling of bivariate count data: (i) dependence across outcomes, and (ii) excess zero counts (i.e., zero inflation). However, there
are currently few options to estimate bivariate zero-inflated count…

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.…

The library scikit-fda is a Python package for functional data analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in…