Work in Progress
Some of the programs related to my papers are available on the page “Software Programs”
Estimating the Gains (and Losses) of Revenue Management
We compare actual practice of revenue management with optimal static and dynamic pricing strategies, in the context of French railway transportation. The identification of demand is complicated by censoring and the absence of exogenous price variations. Using an original empirical strategy combining, we show losses of up to 16.2% compared to the optimal (dynamic) pricing strategy. We also highlight the key role of revenue management in acquiring information when demand is uncertain.
Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period
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 only rely on parallel trends assumptions, unlike commonly used TWFE regressions that also rely on homogeneous treatment effect assumptions.
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.