arXiv subjects

Alexia Ventouri

Publications and source records attributed to Alexia Ventouri.

5 recordsLinked to original sources

arXiv2026

Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses

This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all covariates whose true coefficients are nonzero, and no other covariates, with probability tending to one. Furthermore, the procedure enjoys an oracle property, in the sense that the post-BMT maximum likelihood estimator of the parameters of the model is asymptotically equivalent to an oracle estimator that knows the correct sparse model in advance. Monte Carlo experiments demonstrate that BMT outperforms competing methods, delivering high covariate-selection accuracy and low parameter estimation error. An empirical example illustrates that BMT delivers a predictive model for the probability that U.S. inflation exceeds a given threshold over a 12-month horizon which has very good out-of-sample performance.

econ.EM↗

arXiv2026

Grow and Pollute but Invest and Clean: Dynamic Associations between Parent Firm Characteristics and Facility Toxic Releases

This paper examines how relationships between parent-firm characteristics and facility-level toxic releases evolve over time. Using 238,304 observations for 7,447 U.S. manufacturing facilities from 1992 to 2023, we link on-site releases from the Toxics Release Inventory to financial, managerial, and macroeconomic data. A time-varying mean-group estimator accommodates changes in average coefficients over time and heterogeneity across facilities. The main result is a persistent contrast between operating scale and investment-related adjustment: sales is positively associated with release growth, whereas investment intensity is negatively associated. This pattern remains in joint specifications, parsimonious models, and the main robustness exercises. Other financial and managerial associations are identified as well. These findings suggest that environmental policy evaluation may benefit from distinguishing release changes associated with production expansion from those associated with investment-related adjustment and from allowing for variation across periods and production settings. For corporate managers, the same distinction can inform how emissions considerations are incorporated into capital budgeting and production planning.

econ.EM↗

arXiv2026

Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model

We study spillover effects in corporate toxic emissions using a heterogeneous panel network of U.S. industrial facilities from 2000-2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high-dimensional network econometrics. Indirect effects transmitted through the estimated network account for about 28% of the total impact of key firm balance-sheet characteristics. By contrast, distance-based networks generate no statistically discernible spillovers, while a priori firm- or industry-based networks substantially overstate within-group spillins relative to the data-driven network. These findings show that who is linked to whom, and with what strength, matters critically for assessing systemic environmental risk and for designing targeted regulation. Methodologically, the paper provides a flexible framework for quantifying facility-level emissions spillovers and their consequences in financial and policy settings.

econ.GN↗

arXiv2026

Model Selection in High-Dimensional Linear Regression using Boosting with Multiple Testing

High-dimensional regression specification and analysis is a complex and active area of research in statistics, machine learning, and econometrics. This paper proposes a new approach, Boosting with Multiple Testing (BMT), which combines forward stepwise variable selection with the multiple testing framework of Chudik et al (2018). At each stage, the model is updated by adding only the most significant regressor conditional on those already included, while a family-wise multiple testing filter is applied to the remaining candidates. In this way, the method retains the strong screening properties of Chudik et al (2018) while operating in a less greedy manner with respect to proxy and noise variables. Using sharp probability inequalities for heterogeneous strongly mixing processes from Dendramis et al (2022), we show that BMT enjoys oracle type properties relative to an approximating model that includes all true signals and excludes pure noise variables: this model is selected with probability tending to one, and the resulting estimator achieves standard parametric rates for prediction error and coefficient estimation. Additional results establish conditions under which BMT recovers the exact true model and avoids selection of proxy signals. Monte Carlo experiments indicate that BMT performs very well relative to OCMT and Lasso type procedures, delivering higher model selection accuracy and smaller RMSE for the estimated coefficients, especially under strong multicollinearity of the regressors. Two empirical illustrations based on a large set of macro-financial indicators as covariates, show that BMT yields sparse, interpretable specifications with favourable out-of-sample performance.

econ.EM↗

arXiv2025

Unlocking the Regression Space

This paper introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the OLS and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The proposed setting facilitates the development of asymptotic theory and the estimation of robust standard errors. The robust confidence interval estimators accommodate substantial heterogeneity in both regressors and noise. The resulting robust standard error estimates coincide with White's (1980) heteroskedasticity-consistent estimator but are applicable to a broader range of conditions, including models with missing data. They are computationally simple and perform well in Monte Carlo simulations. Their robustness, generality, and ease of implementation make them highly suitable for empirical applications. Finally, the paper provides a brief empirical illustration.

econ.EM↗