| Title: | Instrumented Difference-in-Differences Decomposition |
| Version: | 0.1.0 |
| Description: | Implements a decomposition of the two-way fixed effects instrumental variable estimator into all possible Wald difference-in-differences estimators. Provides functions to summarize the contribution of different cohort comparisons to the overall two-way fixed effects instrumental variable estimate, with or without controls. The method is described in Miyaji (2024) <doi:10.48550/arXiv.2405.16467>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| URL: | https://github.com/shomiyaji/twfeiv-decomp |
| BugReports: | https://github.com/shomiyaji/twfeiv-decomp/issues |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Depends: | R (≥ 3.5) |
| LazyData: | true |
| Imports: | dplyr, Formula, AER, stats, magrittr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-04 20:07:31 UTC; shomi |
| Author: | Sho Miyaji [aut, cre] |
| Maintainer: | Sho Miyaji <sho.miyaji@yale.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-22 11:50:02 UTC |
Print the summary.
Description
Print the summary.
Usage
print_summary(data, return_df = FALSE)
Arguments
data |
A data.frame. |
return_df |
Logical. If TRUE, returns the summary data.frame. |
Value
Invisibly prints the summary to console. Returns a data.frame if return_df = TRUE.
Example simulation data
Description
A toy dataset included in the package to illustrate the use of the twfeiv_decomp() function. This is artificial data and does not represent real observations.
Usage
simulation_data
Format
A data frame with 60 rows and 6 variables:
- id
Individual identifier (1–10)
- time
Time period (2000–2005)
- instrument
Binary instrumental variable
- treatment
Treatment variable
- outcome
Outcome variable
- control1
Control variable 1
- control2
Control variable 2
Examples
data(simulation_data)
head(simulation_data)
DID-IV decomposition
Description
twfeiv_decomp() is a function that decomposes the TWFEIV estimator into all possible Wald-DID estimators.
Usage
twfeiv_decomp(formula, data, id_var, time_var, summary_output = FALSE)
Arguments
formula |
A formula object of the form
|
data |
A data frame containing all variables used in the formula, as well as the variables specified by id_var and time_var. |
id_var |
The name of id variable. |
time_var |
The name of time variable. |
summary_output |
Logical. If TRUE, prints a summary table showing, for each design type, the total weight and the weighted average of the Wald-DID estimates. If FALSE (the default), no summary is printed. |
Value
If no control variables are included in the formula, the function returns a data frame named exposed_unexposed_combinations which contains the Wald-DID estimates and corresponding weights for each exposed/unexposed cohort pair.
If control variables are included, the function returns a list named decomposition_list containing:
- within_IV_coefficient
Numeric. The coefficient from the within-IV regression.
- between_IV_coefficient
Numeric. The coefficient from the between-IV regression.
- Omega
Numeric. The weight on the within-IV coefficient in the TWFEIV estimator, such that
TWFEIV = \Omega \times \text{within} + (1 - \Omega) \times \text{between}.- exposed_unexposed_combinations
A data.frame with the between-IV coefficients and corresponding weights for each exposed/unexposed cohort pair.
Examples
# Load example dataset
data(simulation_data)
head(simulation_data)
# Example without controls
decomposition_result_without_controls <- twfeiv_decomp(outcome ~ treatment | instrument,
data = simulation_data,
id_var = "id",
time_var = "time")
# Example with controls
decomposition_result_with_controls <- twfeiv_decomp(
outcome ~ treatment + control1 + control2 |control1 + control2 + instrument,
data = simulation_data,
id_var = "id",
time_var = "time"
)