Séminaire PAS

Décembre 2024

• Jeudi 05 décembre 2024 - Arthur Stephanovic
  

Novembre 2024

• Jeudi 21 novembre 2024 - Jordan Serre
  

• Jeudi 14 novembre 2024 - Valentin Gillet (Université de Lille)
  

Octobre 2024

• Jeudi 03 octobre 2024 - Harouna Kinda

  Natural Resource Revenues and Double Taxation Treaties in Developing Countries: Evidence from A Neural Network Approach

  This paper investigates the effects of double taxation treaties on resource revenue mobilization in 83 resource-rich countries from 2000 to 2019 by applying standard panel fixed effects and methods-of-moments approaches. We calculate countries’ centrality indices by year based on their importance in the tax treaty network and show that centrality indices have a negative relationship with resource revenue mobilization—findings that are robust to alternative centrality indices and government revenue aggregates. We also use the betweenness centrality index to identify countries characterized as intermediate jurisdictions (countries -classified as investment or tax hubs based on their betweenness centrality index, which is above the median), arguing that multinational companies structure their investments to benefit from the low withholding tax rates in these countries. Applying the entropy balancing method, we find evidence of a negative effect on resource revenue mobilization dueto signing tax treaties with country-classified investment or tax hubs

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Septembre 2024

• Jeudi 26 septembre 2024 - Wiliam Kengne

  Robust deep learning from weakly dependent data

  We consider robust deep learning from weakly dependent observations, with unbounded loss function and unbounded input/output. It is only assumed that the output variable has a finite r order moment, with r >1.
  Non asymptotic bounds for the expected excess risk of the deep neural network estimator are established under strong mixing assumptions on the observations.
  We derive a relationship between these bounds and r, and when the data have moments of any order (that is r=\infty), the convergence rate is close to some well-known results.
  When the target predictor belongs to the class of H\"older smooth functions with sufficiently large smoothness index, the rate of the expected excess risk for exponentially strongly mixing data is close to that obtained with i.i.d. samples.
  Application to robust nonparametric regression with heavy-tailed errors shows that, robust estimators with absolute loss and Huber loss outperform the least squares method.

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