Time-Varying Heterogeneous Treatment Effects in Event Studies • tvhte

Time-varying heterogeneous treatment effects in event studies, following Botosaru & Liu (2025, 2026).

What this package does

Standard event-study regressions (TWFE with event-time dummies) implicitly assume that the unit and time fixed effects absorb all serial correlation in the residual. When outcomes are genuinely persistent — earnings, employment, consumption, anything with habit or adjustment costs — the event-time dummies pick up persistence on top of the causal effect, producing spurious pre-trends and biased post-treatment estimates.

tvhte implements Botosaru & Liu’s two-step semiparametric estimator:

Marginal-likelihood QMLE for common parameters (ρ_Y, ρ_δ, σ_U, σ_ε) and the latent prior, integrating out unit-level random coefficients (α_i, δ_{i0}) under a Gaussian working assumption. Consistent under misspecification of the prior.
Tweedie / Gaussian-conjugate empirical Bayes for posterior means of the unit-specific treatment trajectories {δ_{i,j}}, achieving asymptotic ratio optimality.

The dimensionality reduction comes from imposing an AR(1) on event-time effects, δ_{ij} = ρ_δ δ_{i,j-1} + ε_{ij}, with δ_{i0} free.

Status

This is Phase 1 (linear-Gaussian, common adoption timing, no covariates). Forthcoming:

Phase 2: strictly exogenous covariates X_{it}'β.
Phase 3: staggered adoption with cohort-specific conditioning.
Phase 4: full MC validation suite.
Phase 5: feedback / endogenous covariate adjustment (Botosaru-Liu 2026).
Phase 6: Great Recession county-unemployment replication vignette.

Installation

# install.packages("remotes")
remotes::install_github("xiangao/tvhte")

Minimal example

library(tvhte)

# Simulate from the model
sim <- simulate_tvhte(N = 800, T = 6, t0 = 3, J = 3,
                      rho_Y = 0.5, rho_delta = 0.7,
                      sigma_alpha = 0.5, sigma_delta0 = 0.4,
                      cor_alpha_delta = 0.3, seed = 1)

# Fit
fit <- tvhte(sim$Y, sim$Y0, t0 = sim$t0, J = sim$J)
print(fit)
# rho_Y      = 0.5112
# rho_delta  = 0.7122
# sigma_U    = 1.007
# ...

Documentation & vignettes

Full documentation: https://xiangao.github.io/tvhte/

Page	Description
Home	Overview, install, motivation
Illustrative vignette	End-to-end walk-through: simulate, fit, posterior trajectories, counterfactual direct/indirect decomposition, with plots
`tvhte()`	Main estimator (QMLE + Tweedie EB). Runnable examples: common adoption, with covariate, staggered with never-treated
`simulate_tvhte()`	Data generator (vanilla DGP and feedback DGP)
`fit_feedback()`	Botosaru-Liu 2026 homogeneous-feedback estimator
`simulate_counterfactual()`	Algorithm 1 of BL 2026: joint counterfactual `(Y, X)`
Reference index	All functions on one page

A Julia port is available at TVHTE.jl (docs).

References

Botosaru, Irene and Laura Liu (2025). “Time-Varying Heterogeneous Treatment Effects in Event Studies.” arXiv:2509.13698.
Botosaru, Irene and Laura Liu (2026). “Event Studies with Feedback.” AEA Papers and Proceedings 116: 70–74.

License

MIT