Work in Progress
Some of the programs related to my papers are available on the page “Software Programs”
Asymptotic Properties of Empirical Quantile-Based Estimators
We derive, under weak conditions the asymptotic normality of estimators of parameters related to the nonlinear DID estimator of the ATT (see Athey and Imbens, 2006). We also develop a new estimator of its asymptotic variance and show its consistency.
Analytic inference with two-way clustering
The usual analytic variance estimator for two-way clustering may not be positive and is invalid for some DGPs. We propose a simple fix that solves both issues. When the estimator of the parameter of interest is asymptotically Gaussian, the corresponding tests are asymptotically exact and equivalent to usual ones. Otherwise, the new tests are asymptotically conservative.
Price Discrimination and Online Sales in the Automobile Industry
We investigate the consequences of introducing an online distribution channel in the French car industry, where currently most of the sales take place in person through car dealers relying on third-degree price discrimination. To this end, we develop a structural model of demand with unobserved third-degree price discrimination and transportation costs related to visiting car dealers.
Linear Regressions with Combined Data
We consider linear regressions when the outcome is not observed in the same dataset as some of the covariates. Without further restriction, we show that the coefficients are partially identified and the bounds take a very simple form. We also show their asymptotic normality.
Is Inference Conditional on Not Rejecting a Pre-test Less Reliable than Unconditional Inference?
The common practice of conducting inference on a parameter of interest conditional on not rejecting a specification test is valid (though generally conservative) if the tested conditions hold, under general conditions. In particular, this requires no assumption on the asymptotic dependence between the estimator of the parameter of interest and the specification test. Conditional inference may also have better properties than unconditional inference under local alternatives.
Difference-in-Differences Estimators When No Unit Remains Untreated
We consider treatment-effect estimation in designs in which no unit is treated initially, and then units simultaneously receive heterogeneous and strictly positive treatment doses. Under a parallel-trends assumption and quasi-untreated units, a causal effect is identified by an estimand mixing DID and RDD. We also consider designs without quasi-untreated units and propose a test of the homogeneous-effect assumption underlying TWFE regressions.
Instrument-Free Demand Estimation Using Relative Prices Variation, with an Application to Railway Transportation
We develop a new identification strategy for demand estimation when cost shifters may not be available and there are substantial variations in demand over time. This approaches relies on a kind of nonlinear difference-in-differences, in which price elasticities are identified by relating changes over time in relative purchases between two goods to changes in their relative prices. We apply this strategy to the context of French railway transportation
Difference-in-Differences for Continuous Treatments and Instruments with Stayers
We propose new DID estimators for continuous treatments. We also assume that from two periods, the treatment of some units, the movers, changes, while the treatment of other units, the stayers, does not change. Then, our estimators compare the outcome evolution of movers and stayers with the same initial value of the treatment. Our estimators is robust to heterogeneous treatment effects. We also extend this framework to an IV setup.
Identification and Estimation of average marginal effects in fixed effect logit models
We show that the sharp bounds on the average marginal and treatment effects (AME and ATE) can be obtained very simply in fixed effect logit models, without any optimization. We consider two related estimation methods. The second is very simple but yields asymptotically to larger confidence intervals, though it performs very well in finite samples.
Empirical MSE Minimization to Estimate a Scalar Parameter
We consider the estimation of a scalar parameter, when two estimators are available. The first is always consistent. The second is inconsistent in general, but has a smaller asymptotic variance than the first, and may be consistent if an assumption is satisfied. We propose to use the weighted sum of the two estimators with the lowest estimated mean-squared error (MSE). This third estimator dominates the other two from a minimax-regret perspective.
Faut-il pondérer ? Ou l’éternelle questin de l’économètre confronté à des données de sondage
A note in French with on whether we should use survey weights in econometrics. The note is complete but most plausibly will never be published.