Archive Seminars and Workshops Quantitative Economics
Autumn 2024
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
Tuesday 15 Oktober 2024 - 13.00-14.00 PM TS53 C.1-05
Speakers: Kristof Bosmans (joint work with Koen Decancq (Universitity of Antwerp), Erwin Ooghe (KU Leuven))
Title: Multidimensional welfare and inequality: we need to talk about efficiency
Abstract:
We strengthen the theory of multidimensional welfare and inequality measurement by explicitly acknowledging considerations of allocative efficiency. Our approach combines the axiomatic rigor of more recent studies (Tsui, 1995, 1999, Gajdos and Weymark, 2005, Weymark, 2006, Seth, 2013) with the utility-based foundations of earlier studies (Kolm, 1977, Atkinson and Bourguignon, 1982, 1987, Maasoumi, 1986). Our axioms sharply distinguish efficiency considerations, captured by the standard Pareto axiom, from inequality considerations, represented by a new transfer axiom that only considers commodity transfers neutral in their effect on efficiency. These axioms reappear, suitably adapted, in our characterizations of welfare, inequality, and efficiency criteria. The analysis yields new characterizations of a well-established class of welfare criteria, a “dissident” class of inequality criteria, and new efficiency criteria.
Tuesday 12 November 2024 - 13:00-14:00 PM - TS53 A0.24
Author: Jana Gieselmann (Duesseldorf Institute for Competition Economics)
Title: (Mis-)Matchmaker
Abstract:
On matching platforms, users (implicitly) pay for the platform's services, but the platform makes money as long as it does not match them. This paper analyzes the matching rule of a profit-maximizing monopoly platform when the incentives between users and the platform are misaligned --- search is costly for users, but the platform either commits to display advertisements or charges a search fee each period. I demonstrate that frequently studied matching rules, such as random matching, are strictly suboptimal for the platform. Instead, the platform strategically lowers match quality to prolong search time and increase revenue, leading to unnecessary delay and potentially inefficient matches. Finally, I provide two explanations for why platforms adopt business models with misaligned incentives: targeted advertising and the presence of overconfident users.
Jana Gieselmann (Duesseldorf Institute for Competition Economics) is on the job market this year and will present her work at the MLSE Seminar on (Mis-)Matchmaker. She is primarily interested in behavioral economics, industrial organization and microeconomic theory
You can find more information about Jana here: https://www.jana-gieselmann.com/
Tuesday 26 November 2024 - 13.00 -14.00 PM TS 53 A1.23
Author: Rastislav Rehák (joint with Maxim Senkov)
Title: Persuasive Pooling
Abstract:
We analyze a quadratic Bayesian persuasion model in which the sender and receiver have arbitrary misalignment regarding their bliss actions as functions of the state. Our focus is on the structure of the optimal signal, particularly how states are pooled in the supports of the induced posterior distributions. We apply our findings to settings where the sender and receiver share a common preference over the ordering of bliss actions but differ in the sensitivity of their responses to state changes. This framework captures scenarios such as an advisor and a CEO with aligned priorities but divergent risk attitudes.
Tuesday 10 December 2024 - 13:15-14:15, TS53 A1.23
Authors: Hannes Rusch (with Maximilian Schmitt, Gewei Cao and Thomas Meissner)
Title: On the Microeconomics of Exploitation
Abstract:
According to the WalkFree Foundation, 50 million people are currently victims of modern slavery, the ILO estimates that the annual profit from forced labor is $236 billion, and two of the UN's Sustainable Development Goals directly address the need to end exploitation. Surprisingly, despite the fact that exploitation has existed across time and space, the incentive structures and strategic logic of exploitative interactions remain poorly understood. Our research uses a simple principal-agent approach to examine exploitation from a microeconomic perspective. Comparing exploitative interactions to a ‘free labor’ benchmark, we show that exploitation is harmful for victims and society at large, while being beneficial for the exploiter. Contrary to the (sparse) existing literature, comparative statics with respect to victims’ outside options imply that victims with better alternatives will be coercived more. Correspondingly, the exploiter's profits decrease in victims’ outside options, making it more profitable to exploit poor victims. Against the backdrop of our model, finally, we examine some preliminary evidence drawn from accounts of trafficking victims in the United States and around the world.
Operations Research Seminars
Wednesday September 11 2024 - 13:00- 14.00 PM TS53 E3.09
Whiteboard talk by Leo Krull - UM. - "Minimizing Tardy Processing Time on a Single Machine in Near-Linear Time"
Abstract:
this work, we revisit the elementary scheduling problem $1||\sum p_j U_j$. The goal is to select, among $n$ jobs with processing times and due dates, a subset of jobs with maximum total processing time that can be scheduled in sequence without violating their due dates. This problem is NP-hard, but a classical algorithm by Lawler and Moore from the 60s solves this problem in pseudo-polynomial time $O(nP)$, where $P$ is the total processing time of all jobs. With the aim to develop best-possible pseudo-polynomial-time algorithms, a recent wave of results has improved Lawler and Moore's algorithm for $1||\sum p_j U_j$: First to time $\widetilde O(P^{7/4})$ [Bringmann, Fischer, Hermelin, Shabtay, Wellnitz; ICALP'20], then to time $\widetilde O(P^{5/3})$ [Klein, Polak, Rohwedder; SODA'23], and finally to time $\widetilde O(P^{7/5})$ [Schieber, Sitaraman; WADS'23]. It remained an exciting open question whether these works can be improved further.
In this work, we develop an algorithm in near-linear time $\widetilde O(P)$ for the $1||\sum p_j U_j$ problem. This running time not only significantly improves upon the previous results, but also matches conditional lower bounds based on the Strong Exponential Time Hypothesis or the Set Cover Hypothesis and is therefore likely optimal (up to subpolynomial factors). Our new algorithm also extends to the case of $m$ machines in time $\widetilde O(P^m)$. In contrast to the previous improvements, we take a different, more direct approach inspired by the recent reductions from Modular Subset Sum to dynamic string problems. We thereby arrive at a satisfyingly simple algorithm.
Joint work with Nick Fischer
Econometric Seminars
Thursday September 12 2024 - 16.00 -17.00 PM
Speaker: Enrico Wegner (UM) - “Transmission channel analysis in dynamic models”.
Abstract: We propose a framework for the analysis of transmission channels in a large class of dynamic models. To this end, we formulate our approach both using graph theory and potential outcomes, which we show to be equivalent. Our method, labelled Transmission Channel Analysis (TCA), allows for the decomposition of total effects captured by impulse response functions into the effects flowing along transmission channels, thereby providing a quantitative assessment of the strength of various transmission channels. We establish that this requires no additional identification assumptions beyond the identification of the structural shock whose effects the researcher wants to decompose. Additionally, we prove that impulse response functions are sufficient statistics for the computation of transmission effects. We demonstrate the empirical relevance of TCA for policy evaluation by decomposing the effects of policy shocks arising from a variety of popular macroeconomic models.
Link: https://doi.org/10.48550/arXiv.2405.18987
Wednesday 6 November 2024 - 11.00-12.00 PM TS53 room H0.06
Speaker: Lucas Harlaar (UM)
Title: Constrained Multivariate Unobserved Components Time Series Models for National Accounts Data
Abstract:
We develop a multivariate time series model for the complete National Accounts data set of a country. The dynamic features in the data are modeled by means of unobserved components that allow for stochastically evolving trend, cycle and seasonal effects. The components can be uniquely associated with a particular variable but can also impact multiple variables simultaneously. The model considers both expenditure and production variables and includes constraints for a synchronized and consistently-defined Gross Domestic Product variable. The model constraints are handled using restricted Kalman filtering and smoothing methods. Parameter estimation is based on exact maximum likelihood which we show is feasible despite the high-dimensional parameter vector. The proposed model-based framework can be used for synchronized nowcasting and forecasting of Gross Domestic Product, but also for its components on the expenditure and production sides. Given that the time series model includes seasonal components, we show that constraints in the model can be altered for the forecasting of seasonally adjusted variables in a model-consistent way. Finally, when the model includes a single cycle component that is shared by all variables in the National Accounts, we can naturally interpret it as a business cycle indicator. We provide evidence that the extracted business cycle is more accurate and more timely when compared to well-known filtering methods. We empirically illustrate our model-based methodology for National Accounts data from Germany, Italy and The Netherlands. We argue that our proposed analyses are of significance from both macroeconomic and statistical perspectives.
Tuesday 12 November 2024 - 12.00-13.00 PM TS53 room A0.23
Speaker: Dr. Edmondo Trentin, Dept. of Information Engineering and Mathematics, University of Siena
Dr. Trentin is visiting SBE also as a committee member for the PhD defense of Dewi Peerlings.
Title: Some Approaches to Density Estimation using Artificial Neural Networks
Abstract: The talk presents robust connectionist techniques for the empirical estimation of multivariate probability density functions (pdf) from unlabeled data samples (still an open yet crucial issue in pattern recognition and machine learning). Data may either be samples of random feature vectors or generalized random graphs. First, a soft-constrained unsupervised algorithm for training a (possibly deep) feed-forward neural net is discussed. A variant of the Metropolis--Hastings algorithm (exploiting the very probabilistic nature of the present deep network) is used to guarantee a model that satisfies numerically Kolmogorov's second axiom of probability. The approach overcomes the major limitations of the established statistical estimators. Graphical and quantitative experimental results show that the proposed technique can offer estimates that improve significantly over parametric and nonparametric approaches, regardless of (1) the complexity of the underlying pdf, (2) the dimensionality of the feature space, and (3) the amount of data available for training. Then, a hybrid machine (combining a graph neural network with a RBF-like network) is presented that can be trained via maximum-likelihood to estimate pdfs over structured (i.e, graphical) patterns.
Website: https://www3.diism.unisi.it/~trentin/HomePage.html
Wednesday 27 November 2024 - 15.00 -16.00 PM room A0.23
Speaker: Ivan Ricardo (UM)
Title: Reduced-Rank Matrix Autoregressive Models: A Medium N Approach
Abstract: Reduced-rank regressions are powerful tools used to identify co-movements within economic time series. However, this task becomes challenging when we observe matrix-valued time series, where each dimension may have a different co-movement structure. We propose reduced-rank regressions with a tensor structure for the coefficient matrix to provide new insights into co-movements within and between the dimensions of matrix-valued time series. Moreover, we relate the co-movement structures to two commonly used reduced-rank models, namely the serial correlation common feature and the index model. Two empirical applications involving U.S. states and economic indicators for the Eurozone and North American countries illustrate how our new tools identify co-movements.
Link to the paper: https://arxiv.org/abs/2407.07973
Thursday 5 December 2024 - 15.00 -16.00 PM TS 49A 0.008
Speaker: Luke Servat (UM)
Titel: Optimal Investment for Retirement with Intergenerational Benchmarking.
Abstract:
"The Law Future Pensions (Wet Toekomst Pensioenen), adopted in
2023, adds the Netherlands to the growing list of countries whose second pen-
sion pillar is changing to a defined contribution plan. Due to the cessation of
guarantees in this plan, pension funds have been given the novel task of finding
an optimal solidary strategy, that aims to prevent so-called ”fortunate” and
”unfortunate” generations. This paper investigates how differences in pensions
can be reduced by changing the investment strategy during the accumulation
phase. As the classic life-cycle does not have this solidarity feature, we adapt
the utility function such that a cohort not only looks at its own accumulated
wealth, but also compares this to the wealth of the preceding cohort. The op-
timal investment strategy that is found as a solution to this novel problem,
what we call the ”intergenerational” life-cycle, deviates significantly from the
classic life-cycle. We observe the ”intergenerational” strategy de-risks during
the periods in which consecutive cohorts are not both investing, but increases
exposure during periods in which they are both accumulating. Furthermore,
we observe that, due to the finite leverage and quasi-linearity, that our solution
bears a strong resemblance to the ”100 − age” rule, which is often used in prac-
tice in the Netherlands. Finally, we conduct multiple simulations which confirm
that, without any loss of the level of wealth, the pursued leveling effect of the
investment strategy is indeed realized."
Vectum Lectures
Tuesday 8 October 2024 - 19:00 - TS53 Room C-1.03.
Speaker: Prof Dr. Jan Christopher Kops.
Topic: (Mis)Interpreting the World
Abstract:
As humans, we do not perceive the world as it is. We interpret it, and even worse we often misinterpret it. There are many reasons why our interpretation can get distorted, ranging from logical mistakes to wrong convictions, measurement errors, language barriers, or even attempts to simplify reality. This research project takes a closer look at real-world instances of such (mis)interpretations as well as the mathematics behind it.