Seminars , Courses and Events Quantitative Economics

MLSE is a (mostly) bi-weekly seminar to foster cooperation between the Department of Microeconomics and Public Economics and the Department of Quantitative Economics. It aims to give researchers the opportunity to present their ongoing work and to facilitate cooperation

Website of MLSE : https://www.maastrichtuniversity.nl/mlse-seminar

In case you want to follow the seminar online, please let us know. Also let us know whenever you know people that would like to receive these emails.

If you would like to present in this seminar series, please send an email to either @Bodicky, Michal (ALGEC) or @Triêu, Anh (KE).

Date and time: 18 November 18, 13:15-14:15 

Author: Jean-Jacques Herings, Christian Seel, Arkadi Predtetchinski

Room A1.22

Title: Random networks

Abstract: Akin to the literature on random games, we analyze pairwise stability (Jackson and Wolinsky, 1996) for two models of socioeconomic networks in which the utilities of each individual are drawn at random. In the case of network-dependent utilities, the utility is drawn at random for each possible network, whereas in the case of neighbor-dependent utilities, the utility is required to be the same whenever the individual has the same set of links.

For each model, as the network gets large, the probability that at least one pairwise stable network exists approaches one, but the expected fraction of pairwise stable networks approaches zero. We also obtain lower and upper bounds on the expected number of pairwise stable networks as the network gets large. For the case of network-dependent utilities, we provide additional results on the distribution of pairwise stable networks.

Go to Archive Seminar s and Workshops QE

Wednesday 19 November 12:30-13:30.

Room TS 53 A1.22

Speaker: Yordi van Kruchten

Title:

Optimizing Capacity Management for the Treatment Daycare Center for Oncology and Hematology: From Thesis Research to Practical Application

Abstract:

This seminar will present the development of a heuristic scheduling framework for the Treatment Daycare Center for Oncology and Hematology at Maastricht UMC+ (MUMC+), from the initial research phase to its ongoing application in practice. The primary goal of the research was to design an efficient scheduling template that optimizes chair allocation and nurse workload. However, before full implementation, efforts are focused on reducing variability at the front-end of the process to ensure smoother operations. This work involves aligning patient inflow from outpatient clinics with available treatment capacity, thus minimizing inefficiencies.

The talk will outline the methodological approach, including clustering, optimization techniques such as Variable Neighborhood Search (VNS), and simulation-based evaluations. Furthermore, I will discuss the ongoing work on real-time data visualizations in Power BI, which are being used to enhance decision-making and capacity planning at the center. The seminar will conclude with an overview of future steps, including the implementation of the template scheduling, once the current variability reduction efforts and other planned improvements are completed.

This presentation will be of interest to students and faculty involved in operations research, healthcare optimization, and data-driven decision-making.

Wednesday 26 November  12:30-13:30.

Room TS 53 A1.22

Speaker: Philipp Ketz

Title: Numerical analysis of test optimality

Abstract: In many testing problems, size control and implementability are primary concerns. As a result, researchers often rely on ad hoc tests—without known optimality properties a priori. Yet, it remains of interest whether a given ad hoc test is optimal in some sense.

The standard practice compares the power curve of the ad hoc test with a power envelope constructed as a sequence of point-optimal tests, but this comparison is inconclusive when a gap exists between the two: either the ad hoc test is inadmissible or the power envelope is unattainable by a single test. We propose an approximate power envelope based on a single test, which enables a conclusive numerical assessment of admissibility. We construct our approximate power envelope for the CLR test of Moreira (2003) in the linear IV model with fixed true parameters under distant null hypotheses. The test is found to be numerically admissible, revising the less optimistic conclusion of Andrews, Marmer, and Yu (2019), which was based on the pointwise power envelope of Andrews, Moreira, and Stock (2006).

Wednesday 3 December, 12:30-13:30.

Speaker: Stijn Vansteelandt

Title: Assumption-Lean (Causal) Modeling

Abstract: Traditional inference in (semi-)parametric models, such as generalized linear models, assumes that models are correctly specified and pre-determined. However, this approach is increasingly inadequate because models are often adaptively selected based on the data, introducing unacknowledged uncertainty. Furthermore, since models rarely represent a true underlying mechanism, standard inference is prone to bias from model misspecification; this is especially a concern in causal modeling, where even small degrees of misspecification in the range of the observed data can give rise to large biases. Recent advances in debiased machine learning and targeted learning have addressed these issues by reducing reliance on correct model specification. However, their model-free nature can limit their applicability and the insight they can deliver in complex settings.  

Assumption-lean modeling rethinks the trade-off between model correctness, parsimony, and interpretability. It begins with data-adaptive outcome predictions, which are then projected onto specific model parameters. This projection is designed to ensure that the parameters remain interpretable or meaningful, even under model misspecification. By incorporating debiased machine learning techniques, assumption-lean modeling minimizes bias, maximizes interpretability, and provides valid confidence intervals that account for both model uncertainty and model misspecification.  

In this talk, I will introduce the core principles of assumption-lean modeling, focusing on its application to generalized linear models for accessibility. The presentation will draw on the work of Vansteelandt and Dukes (2022) that was presented in a discussion paper for the Journal of the Royal Statistical Society: Series B. I will also showcase recent advancements aimed at balancing efficiency with interpretability.

References:
Vansteelandt, S. (2021). Statistical Modelling in the Age of Data Science. Observational Studies, 7(1), 217-228.
Vansteelandt, S and Dukes, O. (2022) Assumption-lean inference for generalised linear model parameters (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84(3), 657– 685.
Vansteelandt, S., Dukes, O., Van Lancker, K., & Martinussen, T. (2024). Assumption-lean Cox regression. Journal of the American Statistical Association, 119(545), 475-484.

workshop in Econometrics

Thursday  4 December 2025,15:00-17:00

Room: TS53, C-1.03

Speakers: Antonio Cosma (University of Bergamo), Geert Dhaene (KU Leuven), Benjamin Holcblat (University of Luxembourg), Gautam Tripathi (University of Luxembourg)
 

15:00-15:30: Antonio Cosma (University of Bergamo)
Title: Missing endogenous variables in conditional moment restriction models (with Andreï V. Kostyrka and Gautam Tripathi)
Abstract:
We consider the estimation of finite dimensional parameters identified via a system of conditional moment equalities when at least one of the endogenous variables (outcomes and/or explanatory variables) is missing at random for some individuals in the sample. We derive the semiparametric efficiency bound for estimating the parameters and use it to demonstrate that efficiency gains occur only if there exists at least one endogenous variable that is nonmissing, i.e., observed for all individuals in the sample. We show how to construct “doubly robust” estimators and propose an estimator that achieves the efficiency bound. A simulation study reveals that our estimator works well in medium-sized samples for point estimation as well as for inference. To see what insights our estimator can deliver in empirical applications with very large sample sizes, we revisit the female labor supply model of Angrist and Evans (1998) and show that if there is even medium missingness in female labor income (the outcome variable), then having more than 200,000 observations is not enough for a researcher using inverse propensity score weighted GMM to find a statistically significant negative effect of having a 3rd child (the endogenous explanatory variable) on labor income. In contrast, our semiparametrically efficient estimator can deliver point estimates of this effect that are comparable to the GMM estimates as well as being statistically significant.

15:30-16:00: Geert Dhaene (KU Leuven)
Title: Approximate functional differencing estimation of average effects in panel models (with Jad Beyhum, Cavit Pakel, and Martin Weidner)
Abstract:
Average effects in nonlinear panel models with unobserved heterogeneity (e.g., fixed effects) are often not point-identified when T, the number of time periods, is finite. Yet the identified set may be small and shrink rapidly to the true average effect as T increases. In such cases, when T is only moderately large, one may settle for an almost consistent, easy-to-compute point estimator. We show that this is possible via an approximate version of the functional differencing approach of Bonhomme (Econometrica 2012). Approximate functional differencing can be viewed as an iterative bias correction scheme, applied to an initially biased estimator and implemented with a finite or infinite number of iterations. The key ingredient at each iteration is to replace, in the exact bias expression, the unknown heterogeneity distribution with the posterior estimate thereof, with the posterior based on a possibly misspecified prior. In models for discrete outcome variables (e.g., in panel logit or probit models with fixed effects), the implementation requires only elementary matrix computations. We show that, under suitable regularity conditions, infinitely-iterated approximate functional differencing yields average-effect estimates whose bias shrinks exponentially fast in T.

16:00-16:30: Benjamin Holcblat (University of Luxembourg)
Title: Generalized ESP estimator (with Ali Atabaigi and Fallaw Sowell)
Abstract:
Several studies have documented the instability of moment-based estimators such as the generalized method of moments (GMM) estimators, and the generalized empirical likelihood (GEL) estimators. We introduce a novel class of moment-based estimators. We show they correspond to GEL estimators shrunk toward parameter values with lower implied estimated variance, so they are more stable. We call them generalized empirical saddlepoint (GESP) estimators because they are based on generalizations of the ESP approximation. We prove existence, consistency and asymptotic normality of GESP estimators, and derive test statistics.
16:30-17:00: Gautam Tripathi (University of Luxembourg)
Title: Estimating parameters and marginal effects in nonlinear panel data models
Abstract:
We present some new developments in the estimation of parameters and marginal effects in nonlinear panel data models with fixed effects.

Schedule Overview:

• 10/09: Marc Schröder (Maastricht University)
• 17/09: Garth Tarr (The University of Sydney)
• 24/09: Tom Demeulemeester (Maastricht University)
• 1/10: Daniele Girolimetto (University of Padova)
• 8/10: Dries Vermeulen (Maastricht University)
• 15/10: Frits Spieksma (TU Eindhoven)
• 22/10: Ties Bos (QE), Jip de Kok (MUMC+), Frank van Rosmalen (MUMC+
• 29/10: Shahrezad Fahmy  (Maastricht University)
• 5/11:  Etienne Wijler (Vrije Universiteit Amsterdam)
• 12/11: Xuanzhu Jin  (Maastricht University)
• 19/11: Yordi van Kruchten and Flip van Kasteren (MUMC+)
• 26/11: Philipp Ketz (Paris School of Economics)
• 3/12: Stijn Vansteelandt (Ghent University)
• 10/12: Heiko Röglin (University of Bonn)
• 17/12: TBA

Go to Archive Seminars and Workshops QE