arXiv subjects

David Benatia

Publications and source records attributed to David Benatia.

4 recordsLinked to original sources

arXiv2026

What Drives Energy Use? Prices, Efficiency Policies, and the Demand Frontier

What drives cross-state differences in U.S. energy consumption? We combine LMDI decomposition, stochastic frontier analysis, and variable-importance methods on a panel of 50 states plus DC over the 2006--2022 period. The observed 12.8% decline in per capita energy use is driven almost entirely by intensity improvements. A variance decomposition attributes 63% of cross-state variation in log energy use to the demand frontier, 34\% to inefficiency above it, and 3% to noise. Within the frontier, energy prices account for roughly 26% of cross-state variation and state efficiency policies for about 13%, while GDP and climate together explain only around 10\%. Efficiency policies also operate through a second channel by reducing inefficiency, adding a further 6 percentage points to their total contribution. The results suggest that pricing and regulation are the primary drivers of cross-state energy use differences.

econ.GN↗

arXiv2023

Large Banks and Systemic Risk: Insights from a Mean-Field Game Model

This paper presents a dynamic game framework to analyze the role of large banks in interbank markets. By extending existing models, we incorporate a large bank as a dynamic decision-maker interacting with multiple small banks. Using the mean-field game methodology and convex analysis, best-response trading strategies are derived, leading to an approximate equilibrium for the interbank market. We investigate the influence of the large bank on the market stability by examining individual default probabilities and systemic risk, through the use of Monte Carlo simulations. Our findings reveal that, when the size of the major bank is not excessively large, it can positively contribute to market stability. However, there is also the potential for negative spillover effects in the event of default, leading to an increase in systemic risk. The magnitude of this impact is further influenced by the size and trading rate of the major bank. Overall, this study provides valuable insights into the management of systemic risk in interbank markets.

q-fin.MF↗

arXiv2023

The Chained Difference-in-Differences

This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels. Our estimator accommodates (1) multiple time periods, (2) variation in treatment timing, (3) treatment effect heterogeneity, (4) general missing data patterns, and (5) sample selection on observables. We establish the asymptotic properties of the proposed estimator and discuss identification and efficiency gains in comparison to existing methods. Finally, we illustrate its relevance through (i) numerical simulations, and (ii) an application about the effects of an innovation policy in France.

econ.EM↗

arXiv2022

Dealing with Logs and Zeros in Regression Models

The log transformation is widely used in linear regression, mainly because coefficients are interpretable as proportional effects. Yet this practice has fundamental limitations, most notably that the log is undefined at zero, creating an identification problem. We propose a new estimator, iterated OLS (iOLS), which targets the normalized average treatment effect, preserving the percentage-change interpretation while addressing these limitations. Our procedure is the theoretically justified analogue of the ad-hoc log(1+Y) transformation and delivers a consistent and asymptotically normal estimator of the parameters of the exponential conditional mean model. iOLS is computationally efficient, globally convergent, and free of the incidental-parameter bias, while extending naturally to endogenous regressors through iterated 2SLS. We illustrate the methods with simulations and revisit three influential publications.

econ.EM↗