Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <arXiv:2103.06328>.

Hypothesis Testing in Cluster-Randomized Encouragement Designs
An implementation of randomization-based hypothesis
testing for three different estimands in a cluster-randomized
encouragement experiment. The three estimands include (1) testing
a cluster-level constant proportional treatment effect (Fisher's
sharp null hypothesis), (2) pooled effect ratio, and (3) average
cluster effect ratio. To test the third estimand, user needs to install
'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to
<https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.

Ensures Mutually Consistent Beliefs When Using IVs
c(
person("Frank", "DiTraglia", email = "francis.ditraglia@economics.ox.ac.uk", role = "aut"),
person("Mallick", "Hossain", email = "emallickhossain@gmail.com", role = c("aut", "cre")))
Uses data and researcher's beliefs on measurement error and
instrumental variable (IV) endogeneity to generate the space of consistent
beliefs across measurement error, instrument endogeneity, and instrumental
relevance for IV regressions.
Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
AER, coda, data.table, graphics, MASS, Rcpp (>= 0.11.6), rgl,
sandwich, stats
testthat, haven, MCMCpack, knitr, rmarkdown
Frank DiTraglia [aut],
Mallick Hossain [aut, cre]

Instrumental fixed effect panel data model
Fit an Instrumental least square dummy variable model

General to Specific Modeling and Indicator Saturation in 2SLS
Models
person("Kurle", "Jonas", email = "mail@jonaskurle.com",
role = c("aut","cre"), comment = c(ORCID = "0000-0003-2197-2012"))
Provides facilities of general to specific model selection for
exogenous regressors in 2SLS models. Furthermore, indicator saturation
methods can be used to detect outliers and structural breaks in the sample.