arXiv subjects

Adhen Benlahlou

Publications and source records attributed to Adhen Benlahlou.

3 recordsLinked to original sources

arXiv2026

Gradient Boosting for Spatial Panel Models with Random and Fixed Effects

Due to the increase in data availability in urban and regional studies, various spatial panel models have emerged to model spatial panel data, which exhibit spatial patterns and spatial dependencies between observations across time. Although estimation is usually based on maximum likelihood or generalized method of moments, these methods may fail to yield unique solutions if researchers are faced with high-dimensional settings. This article proposes a model-based gradient boosting algorithm, which enables estimation with interpretable results that is feasible in low- and high-dimensional settings. Due to its modular nature, the flexible model-based gradient boosting algorithm is suitable for a variety of spatial panel models, which can include random and fixed effects. The general framework also enables data-driven model and variable selection as well as implicit regularization where the bias-variance trade-off is controlled for, thereby enhancing accuracy of prediction on out-of-sample spatial panel data. Monte Carlo experiments concerned with the performance of estimation and variable selection confirm proper functionality in low- and high-dimensional settings while real-world applications including non-life insurance in Italian districts, rice production in Indonesian farms and life expectancy in German districts illustrate the potential application.

stat.ME↗

arXiv2026

Strategic Interactions in Science and Technology Networks: Substitutes or Complements?

This paper develops a theory of scientific and technological peer effects to study how individuals' productivity responds to the behavior and network positions of their collaborators across both scientific and inventive activities. Building on a simultaneous equation network framework, the model predicts that productivity in each activity increases in a variation of the Katz-Bonacich centrality that captures within-activity and cross-activity strategic complementarities. To test these predictions, we assemble the universe of cancer-related publications and patents and construct coauthorship and coinventorship networks that jointly map the collaboration structure of researchers active in both spheres. Using an instrumental-variables approach based on predicted link formation from exogenous dyadic characteristics, and incorporating community fixed effects to address endogenous network formation, we show that both authors' and inventors' outputs rise with their network centrality, consistent with the theory. Moreover, scientific productivity significantly enhances technological productivity, while technological output does not exert a detectable reciprocal effect on scientific production, highlighting an asymmetric linkage aligned with a science-driven model of innovation. These findings provide the first empirical evidence on the joint dynamics of scientific and inventive peer effects, underscore the micro-foundations of the co-evolution of science and technology, and reveal how collaboration structures can be leveraged to design policies that enhance collective knowledge creation and downstream innovation.

econ.GN↗

arXiv2024

Mitigating Consequences of Prestige in Citations of Publications

For many public research organizations, funding creation of science and maximizing scientific output is of central interest. Typically, when evaluating scientific production for funding, citations are utilized as a proxy, although these are severely influenced by factors beyond scientific impact. This study aims to mitigate the consequences of the Matthew effect in citations, where prominent authors and prestigious journals receive more citations regardless of the scientific content of the publications. To this end, the study presents an approach to predicting citations of papers based solely on observable characteristics available at the submission stage of a double-blind peer-review process. Combining classical linear models, generalized linear models and utilizing large-scale data sets on biomedical papers based on the PubMed database, the results demonstrate that it is possible to make fairly accurate predictions of citations using only observable characteristics of papers excluding information on authors and journals, thereby mitigating the Matthew effect. Thus, the outcomes have important implications for the field of scientometrics, providing a more objective method for citation prediction by relying on pre-publication variables that are immune to manipulation by authors and journals, thereby enhancing the objectivity of the evaluation process. Our approach is thus important for government agencies responsible for funding the creation of high-quality scientific content rather than perpetuating prestige.

cs.DL↗