Event-study imputation estimator (module-name entry point)
Source:R/causdidev_native.R
morie_causdidev.RdEntry point onto morie_boryis; see there for the
three-step imputation procedure of Borusyak, Jaravel and Spiess
(2024), Theorem 2.
Arguments
- y
Outcome, long format.
- D
Binary 0/1 absorbing treatment, long format.
- unit, time
Panel identifiers.
- X
Optional time-varying covariates (vector or matrix, one column per covariate), long format.
- weights
Target weights:
NULLor"equal"(default) averages treated cells equally;"cohort"weights by cohort size; or supply your own per-observation weights. All three routes are available, since the paper's Theorem 2 imputes identically whatever the estimand.
Value
The list returned by morie_boryis.
References
Borusyak, K., Jaravel, X. and Spiess, J. (2024). Revisiting event study designs: robust and efficient estimation. Review of Economic Studies, 91(6), 3253-3285.
Examples
set.seed(1)
nu <- 6; T <- 5
unit <- rep(1:nu, each = T)
time <- rep(1:T, nu)
ft <- rep(c(Inf, Inf, Inf, 4, 4, 3), each = T)
D <- as.integer(time >= ft)
y <- rnorm(nu * T) + D * 1.5
morie_causdidev(y, D, unit, time)
#> $estimate
#> [1] 1.012478
#>
#> $se
#> [1] 0.6437863
#>
#> $ci
#> [1] -0.2493203 2.2742757
#>
#> $tau_it
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] -0.7066393 -0.02792423 -0.7649001 1.5813997 -0.08193605
#> [2,] -0.9065197 0.26999573 0.8031875 0.5560344 -0.72269798
#> [3,] 1.5227426 0.26942261 -0.4593651 -2.1374341 0.80463402
#> [4,] -0.3456817 -0.44832042 0.7940022 2.0867774 1.46189490
#> [5,] 0.3205738 0.05235070 -0.3729245 -1.0214509 1.19016388
#> [6,] 0.1155244 -0.11552440 0.3518148 1.2598075 1.75833640
#>
#> $imputed_y0
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.08018553 0.21156755 -0.07072856 0.01388114 0.4114438
#> [2,] 0.08605130 0.21743332 -0.06486279 0.01974691 0.4173096
#> [3,] -0.01096140 0.12042063 -0.16187548 -0.07726578 0.3202969
#> [4,] 0.30074813 0.43213016 0.14983405 0.23444375 0.6320064
#> [5,] 0.59840357 0.72978560 0.44748949 0.53209919 0.9296619
#> [6,] -0.17165314 -0.04027111 -0.32256722 -0.23795752 0.1596052
#>
#> $weights
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.11904762 0.11904762 0.04761905 -0.1428571 -0.1428571
#> [2,] 0.11904762 0.11904762 0.04761905 -0.1428571 -0.1428571
#> [3,] 0.11904762 0.11904762 0.04761905 -0.1428571 -0.1428571
#> [4,] -0.07142857 -0.07142857 -0.14285714 0.1428571 0.1428571
#> [5,] -0.07142857 -0.07142857 -0.14285714 0.1428571 0.1428571
#> [6,] -0.21428571 -0.21428571 0.14285714 0.1428571 0.1428571
#>
#> $linearity_residual
#> [1] 1.088019e-14
#>
#> $se_note
#> [1] "clustered on unit using the estimator's exact linear weights; treated cells contribute their deviation from the average effect, which is the conservative choice"
#>
#> $event
#> $event$`0`
#> [1] 0.4723805
#>
#> $event$`1`
#> [1] 1.303955
#>
#> $event$`2`
#> [1] 1.758336
#>
#>
#> $cohort_att
#> $cohort_att$`2`
#> [1] 1.12332
#>
#> $cohort_att$`3`
#> [1] 0.9293463
#>
#>
#> $pretrend_by_rel
#> $pretrend_by_rel$`-3`
#> [1] -0.01255397
#>
#> $pretrend_by_rel$`-2`
#> [1] -0.09348177
#>
#> $pretrend_by_rel$`-1`
#> [1] 0.1018511
#>
#>
#> $pretrend_max_abs
#> [1] 0.1018511
#>
#> $pretrend_note
#> [1] "the imputation uses EVERY untreated period, so parallel trends is assumed throughout the pre-period, not only just before adoption; these residuals are zero in expectation under that assumption"
#>
#> $unit_effects
#> [1] 0.11621089 0.12207667 0.02506397 0.33677350 0.63442894 -0.13562777
#>
#> $period_effects
#> [1] -0.03602537 0.09535666 -0.18693945 -0.10232975 0.29523293
#>
#> $covariate_coef
#> NULL
#>
#> $n_treated_cells
#> [1] 7
#>
#> $n_untreated_cells
#> [1] 23
#>
#> $n_units
#> [1] 6
#>
#> $n_periods
#> [1] 5
#>
#> $no_forbidden_comparisons
#> [1] "no treated observation enters the fit, so an already-treated unit cannot act as a control"
#>
#> $method
#> [1] "Borusyak-Jaravel-Spiess (2024) imputation estimator"
#>