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https://github.com/xuyiqing/fect
fixed effect counterfactual estimators
https://github.com/xuyiqing/fect
Last synced: about 2 months ago
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fixed effect counterfactual estimators
- Host: GitHub
- URL: https://github.com/xuyiqing/fect
- Owner: xuyiqing
- License: other
- Created: 2018-10-30T06:52:31.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-24T18:29:49.000Z (3 months ago)
- Last Synced: 2024-06-24T20:15:07.437Z (3 months ago)
- Language: R
- Size: 16 MB
- Stars: 51
- Watchers: 5
- Forks: 18
- Open Issues: 15
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# fect
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://opensource.org/licenses/MIT)
**R** package for implementing counterfactual estimators in panel fixed-effect settings. It is suitable for panel/TSCS analysis with binary treatments under (hypothetically) baseline randomization. It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models---hence, a generalization of [**gsynth**](https://yiqingxu.org/packages/gsynth/index.html)---and the matrix completion method.
**Repo:** [GitHub](https://github.com/xuyiqing/fect) (1.0.0)
**Examples:** R code used in the [tutorial](https://yiqingxu.org/packages/fect/articles/tutorial.html) can be downloaded from [here](fect_examples.R).
**Reference:** Licheng Liu, Ye Wang, Yiqing Xu (2021). [A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data](https://yiqingxu.org/papers/english/2022_fect/LWX2022.pdf). *American Journal of Political Science*, conditionally accepted.
## Installation
You can install **fect** directly from CRAN by typing the following command in the **R** console:
```{r eval=FALSE}
install.packages('fect')
```
You can install the development version of **fect** from GitHub by typing the following commands:
```{r eval=FALSE}
devtools::install_github('xuyiqing/fect')
```
**panelview** for panel data visualization is also highly recommended:
```{r eval=FALSE}
devtools::install_github('xuyiqing/panelView')
```
**fect** depends on the following packages, which will be installed automatically when **fect** is being installed. You can also install them manually.
```{r message = FALSE, warning = FALSE}
## for processing C++ code
require(Rcpp)
## for plotting
require(ggplot2)
require(GGally)
require(grid)
require(gridExtra)
## for parallel computing
require(foreach)
require(future)
require(doParallel)
require(abind)
```
### Notes on installation failures
1. Mac users who have updated to MacOS BigSur or Monterey will likely encounter compilation problems. See [here](http://yiqingxu.org/public/BigSurError.pdf) for a potential solution.
2. Windows users please consider upgrading R to 4.0.0 or higher and installing the [latest Rtools](https://cran.r-project.org/bin/windows/Rtools/) to avoid C++17 complier errors when installing fastplm.
3. For Rcpp, RcppArmadillo and MacOS "-lgfortran" and "-lquadmath" error, click [here]( http://thecoatlessprofessor.com/programming/rcpp-rcpparmadillo-and-os-x-mavericks-lgfortran-and-lquadmath-error/) for details.
4. Installation failure related to OpenMP on MacOS, click [here](http://thecoatlessprofessor.com/programming/openmp-in-r-on-os-x/) for a solution.
5. To fix these issues, try installing gfortran from [here]( https://gcc.gnu.org/wiki/GFortranBinaries#MacOS clang4 R Binaries from https://github.com/coatless/r-macos-clang).
## Report bugs
Please report bugs to **yiqingxu [at] stanford.edu** with your sample code and data file. Much appreciated!