Irene Botosaru
Associate Professor
Canada Research Chair (Tier 2)
Department of Economics
McMaster University
Research field: Econometrics
Curriculum Vitae: [Link]
Women in Econometrics Conference
[2022]
[2024]
[2026]
Canadian Econometrics Study Group
[2023]
Research
My research develops econometric methods for panel data, with a focus on nonlinear models and program evaluation.
A unifying theme of my work is how time variation and cross-sectional heterogeneity affect the identification and
estimation of counterfactuals and treatment effects.
Publications
Partial Effects in Time-Varying Linear Transformation Panel Models with Endogeneity (2025, with
Chris Muris and
Senay Sokullu)
Accepted at Journal of Business & Economic Statistics
[Abstract]
[link]
This paper develops a new estimator for an average partial effect in nonlinear panel models, where outcomes are
time-varying monotonic transformations of latent variables that include fixed effects and endogenous regressors. The
partial effect can be time-varying and the counterfactual shift is scale invariant -- key advantages over the linear
model. We exploit a conditional moment restriction and address the ill-posed nature of recovering the transformation
functions using nonparametric instrumental variable techniques. Our estimator for the partial effect satisfies root-$n$
asymptotic normality. We apply our method to data from the Trends in International Mathematics and Science Study, and
find evidence that traditional instruction is strongly associated with improved achievement in both mathematics and
science.
Identification of Time-Varying Counterfactual Parameters in Nonlinear Panel Models (2024, with
Chris Muris)
Accepted at Journal of Econometrics
[Abstract]
[Paper]
[arXiv]
We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric
panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed
effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our
results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our main
results focus on static models, with a set of results applying to models without any exogeneity conditions. We show that
the survival distribution of counterfactual outcomes is identified (point or partial) in this class of models. This
parameter is a building block for most partial and marginal effects of interest in applied practice that are based on the
average structural function as defined by Blundell and Powell (2003, 2004). To the best of our knowledge, ours are the
first results on average partial and marginal effects for binary choice and ordered choice models with two-way fixed
effects and non-logistic errors.
Time-Varying Unobserved Heterogeneity in Earnings Shocks (2023)
Journal of Econometrics, 235(2): 1378-1393.
[Abstract]
[Paper]
This paper considers the transitory-permanent model for the earnings process, and allows for time-varying
individual-specific unobserved heterogeneity in each shock. The cross-sectional heterogeneity in each shock is drawn from
an unknown distribution at each time period. Sufficient conditions for the nonparametric identification of the cross-
sectional density functions of the heterogeneity are provided, under different assumptions on the time series behavior of
the transitory shock. The method proposed is then applied to earnings data to document a high degree of cross-sectional
heterogeneity in each shock.
Identification of Time-Varying Transformation Models with Fixed Effects, with an Application to Unobserved Heterogeneity in Resource Shares
(2023, with Chris Muris and
Krishna Pendakur)
Journal of Econometrics, 232(2): 576-597.
[Abstract]
[Paper]
We provide new results showing identification of a large class of fixed-T panel models, where the response variable is
an unknown, weakly monotone, time-varying transformation of a latent linear index of fixed effects, regressors, and an
error term drawn from an unknown stationary distribution. Our results identify the transformation, the coefficient on
regressors, and features of the distribution of the fixed effects. We then develop a full-commitment intertemporal
collective household model, where the implied quantity demand equations are time-varying functions of a linear index.
The fixed effects in this index equal logged resource shares, defined as the fractions of household expenditure enjoyed
by each household member. Using Bangladeshi data, we show that women’s resource shares decline with household budgets
and that half of the variation in women’s resource shares is due to unobserved household-level heterogeneity.
Nonparametric Analysis of a Duration Model with Stochastic Unobserved Heterogeneity (2020).
Journal of Econometrics, 217(1): 112-139.
[Abstract]
[Paper]
This paper develops nonparametric identification and estimation results for a single-spell hazard model, where the
unobserved heterogeneity is specified as a Lévy subordinator. The identification approach solves a nonlinear Volterra
integral equation of the first kind with an unknown kernel function defined on a non-compact support. Both the kernel of
the integral operator, which models the distribution of the unobserved heterogeneity, and the functions that enter it
nonlinearly are identified given regularity conditions and minimal variation in the observed covariates. The paper
proposes a shape-constrained nonparametric two-step sieve minimum distance estimator. The second step estimates the
kernel of the integral operator, exploiting a monotonicity property. Rates of convergence are derived and Monte Carlo
experiments show the finite sample performance of the estimator.
On the Role of Covariates in the Synthetic Control Method (2019, with
Bruno Ferman).
The Econometrics Journal, 22(2): 117-130.
Included in the Virtual Issue: The Econometrics of Treatment Effects
[Abstract]
[Paper]
Abadie et al. (2010) derive bounds on the bias of the Synthetic Control estimator under a perfect balance assumption on
both observed covariates and pretreatment outcomes. In the absence of a perfect balance on covariates, we show that it is
still possible to derive such bounds, but at the expense of relying on stronger assumptions on the effects of observed
and unobserved covariates and of generating looser bounds. We also show that a perfect balance on pre-treatment outcomes
does not generally imply an approximate balance for all covariates, even when they are all relevant. Our results have
important implications for the implementation of the method.
Nonparametric Heteroskedasticity in Persistent Panel Processes: An Application to Earnings Dynamics (2018, with
Yuya Sasaki).
Journal of Econometrics, 203(2): 283-296.
[Abstract]
[Paper]
[Stata command]
This paper considers a dynamic panel model where a latent state variable follows a unit root process with nonparametric
heteroskedasticity. We develop constructive nonparametric identification and estimation of the skedastic function.
Applying this method to the Panel Survey of Income Dynamics (PSID) in the framework of earnings dynamics, we found that
workers with lower pre-recession permanent earnings had higher earnings risk during the three most recent recessions.
Difference-in-Differences When the Treatment Status is Observed in Only One Period (2018, with
Federico Gutierrez).
Journal of Applied Econometrics, 33(1): 73-90.
[Abstract]
[Paper]
This paper considers the difference‐in‐differences (DID) method when the data come from repeated cross‐sections and the
treatment status is observed either before or after the implementation of a program. We propose a new method that
point‐identifies the average treatment effect on the treated (ATT) via a DID method when there is at least one proxy
variable for the latent treatment. Key assumptions are the stationarity of the propensity score conditional on the proxy
and an exclusion restriction that the proxy must satisfy with respect to the change in average outcomes over time
conditional on the true treatment status. We propose a generalized method of moments estimator for the ATT and we show
that the associated overidentification test can be used to test our key assumptions. The method is used to evaluate
JUNTOS, a Peruvian conditional cash transfer program. We find that the program significantly increased the demand for
health inputs among children and women of reproductive age.
In Progress
Event Studies with Imperfect Adoption (with Akanksha Negi)
Identification and Estimation of Correlated Random Coefficient Distributions (in Panel Data)
(with Jim Powell)
An Adversarial Approach to Identification and Inference
(with Isaac Loh and
Chris Muris)
[Abstract]
[arXiv]
We introduce a novel framework to characterize identified sets of structural and counterfactual parameters in econometric
models. Our framework centers on a discrepancy function, which we construct using insights from convex analysis. The zeros
of the discrepancy function determine the identified set, which may be a singleton. The discrepancy function has an
adversarial game interpretation: a critic maximizes the discrepancy between data and model features, while a defender
minimizes it by adjusting the probability measure of the unobserved heterogeneity. Our approach enables fast computation
via linear programming. We use the sample analog of the discrepancy function as a test statistic, and show that it
provides asymptotically valid inference for the identified set. Applied to nonlinear panel models with fixed effects, it
offers a unified approach for identifying both structural and counterfactual parameters across exogeneity conditions,
including strict and sequential, without imposing parametric restrictions on the distribution of error terms or
functional form assumptions.
Fixed Effects 2SLS for Linear Panel Models with Feedback (with
Chris Muris)
Time-Varying Heterogeneous Treatment Effects in Event Studies
(with Laura Liu)
[Abstract]
[arXiv]
This paper examines the identification and estimation of heterogeneous treatment effects in event studies, emphasizing
the importance of both lagged dependent variables and treatment effect heterogeneity. We show that omitting lagged
dependent variables can induce omitted variable bias in the estimated time-varying treatment effects. We develop a novel
semiparametric approach based on a short-T dynamic linear panel model with correlated random coefficients, where the
time-varying heterogeneous treatment effects can be modeled by a time-series process to reduce dimensionality. We
construct a two-step estimator employing quasi-maximum likelihood for common parameters and empirical Bayes for the
heterogeneous treatment effects. The procedure is flexible, easy to implement, and achieves ratio optimality
asymptotically. Our results also provide insights into common assumptions in the event study literature, such as no
anticipation, homogeneous treatment effects across treatment timing cohorts, and state dependence structure.
Forecasted Treatment Effects
(with Raffaella Giacomini and
Martin Weidner)
[Abstract]
[arXiv]
We consider estimation and inference of the effects of a policy in the absence of a control group. We obtain unbiased
estimators of individual (heterogeneous) treatment effects and a consistent and asymptotically normal estimator of the
average treatment effects, based on forecasting counterfactuals using a short time series of pre-treatment data. We show
that the focus should be on forecast unbiasedness rather than accuracy. Correct specification of the forecasting model is
not necessary to obtain unbiased estimates of individual treatment effects. Instead, simple basis function (e.g.,
polynomial time trends) regressions deliver unbiasedness under a broad class of data-generating processes for the
individual counterfactuals. Basing the forecasts on a model can introduce misspecification bias and does not necessarily
improve performance even under correct specification. Consistency and asymptotic normality of our Forecasted Average
Treatment effects (FAT) estimator are attained under an additional assumption that rules out common and unforecastable
shocks occurring between the treatment date and the date at which the effect is calculated.
Higher-Order Earnings Risks and Asymmetric Marginal Propensities to Consume
(with Silvia Sarpietro and
Yuya Sasaki)
Superseded by newer work
Time-varying linear transformation models with fixed effects and endogeneity for short panels (2022, with
Chris Muris and
Senay Sokullu)
[Cemmap Working Paper 06/22]
[McMaster Working Paper]
Binarization for Panel Models with Fixed Effects (2017, with
Chris Muris).
[Abstract]
[Cemmap Working Paper 31/17]
In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification
difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based,
impose time homogeneity, or restrict the distribution of unobserved heterogeneity. We provide new identification
results for the structural function and for partial effects in a large class of Fixed Effects Linear Transformation
(FELT) models with unknown, time-varying, weakly monotone transformation functions. Our results accommodate continuous
and discrete outcomes and covariates, require only two time periods, and impose no parametric distributional
assumptions. First, we provide a systematic solution to the incidental parameter problem in FELT. Second, we identify
the distribution of counterfactual outcomes and a menu of time-varying partial effects without any assumptions on the
distribution of unobserved heterogeneity. Third, we obtain new results for nonlinear difference-in-differences that
accomodate both discrete and censored outcomes, and for FELT with random coefficients. Finally, we propose rank- and
likelihood-based estimators that achieve square root-n rate of convergence.
Identifying Distributions in a Panel Model with Heteroskedasticity: An Application to Earnings Volatility (2017)
[Simon Fraser University Working Paper 17-11].
A Duration Model with Dynamic Unobserved Heterogeneity (2011)
[TSE Working Paper 11-262]
Dormant
Nonparametric Identification and Estimation of a Potential Hazard Model
Identification of a Duration Model with Time Deformed Unobserved Heterogeneity