David Rügamer:
Semi-Structured Regression: Current Advances and Challenges

Neural networks enable learning from various data modalities, such as images and text. This concept has been integrated into statistical modeling through semi-structured regression, which additively combines structured predictors with unstructured effects from arbitrary data modalities by embedding the statistical model into the neural network. This approach opens new opportunities for advancing regression models but also poses challenges, such as increased uncertainty, implicit regularization, and complexities in statistical inference. This presentation will introduce semi-structured regression, discuss recent advances, and highlight the challenges of embedding regression models into neural networks.

Various related papers: Paper 1, Paper 2, Paper 3, Paper 4, Paper 5

Damir Filipovic:
Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels

We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consistency results and finite-sample guarantees, ensuring its reliability for empirical applications in Finance. We apply it to an extensive panel of monthly US stock excess returns from 1962 to 2021, using macroeconomic and firm-specific covariates as conditioning variables. The estimator effectively captures time-varying cross-sectional dependencies, demonstrating robust statistical and economic performance. We find that idiosyncratic risk explains, on average, more than 75% of the cross-sectional variance.

Ulrike Schneider:
Understanding the Adaptive LASSO in Predictive Regressions

Several articles have looked at LASSO and adaptive LASSO methods in the context of predictive and cointegrating regressions in the econometrics literature in recent years. The results shown in this literature are derived in a so-called fixed-parameter framework which is known to come with certain limitations regarding the interpretability of the results for the actual performance of the estimator.
To better understand the behavior of the adaptive LASSO, we carry out a full analysis of the estimator in a predictive regression model with only one (endogenous) regressor. We provide insights to the model selection properties in connection with the tuning parameter sequence and extract the two different possible regimes, consistent and conservative model selection. For each regime, we look at convergence rates and asymptotic distributions in a so-called moving-parameter framework, and also work out the local-to-zero rates which still can be detected by the estimator. We find that in the consistently tuned case, the existing results from a fixed-parameter framework do not paint a realistic picture as overall convergence and local-to-zero rates are actually slower and limiting distributions differ. The discrepancies are less pronounced when looking at the conservatively tuned case. We finish our analysis with a simulation study examining the distribution of the estimator in finite samples.

Joint work in progress with Karsten Reichold (TU Wien).

Sara Wade:
Understanding Uncertainty in Bayesian Cluster Analysis

The Bayesian approach to clustering is often appreciated for its ability to shed light on uncertainty in the partition structure. However, summarizing the posterior distribution on the partition space can be challenging. In previous work, we proposed to summarize the posterior samples using a single optimal clustering estimate, which minimizes the expected posterior Variation of Information (VI).  In instances where the posterior distribution is multimodal, it can be beneficial to summarize the posterior samples using multiple clustering estimates, each corresponding to a different part of the space of partitions that receives substantial posterior mass. In this work, we propose to find such clustering estimates by approximating the posterior distribution in a VI-based Wasserstein distance sense. An interesting byproduct is that this problem can be seen as using the k-mediods algorithm to divide the posterior samples into different groups, each represented by one of the clustering estimates. Using both synthetic and real datasets, we show that our proposal helps to improve the understanding of uncertainty, particularly when the data clusters are not well separated, or when the employed model is misspecified. 

Work in progress with Cecilia Balocchi (Edinburgh), building on previous work (see linked papers). 

Sascha Desmettre:
Equilibrium Control Theory for Kihlstrom-Mirman Preferences in Continuous Time

Introduced in 1974, Kihlstrom-Mirman preferences represent a multi-attribute generalization of the standard (univariate) expected utility theory. The main appeal of this class of utilities is that, by separating the choice of the elasticity of intertemporal substitution and risk aversion, they allow to disentangle attitudes towards time and risk. We discuss in detail how this approach differs from other classes of preferences exhibiting a similar feature (for instance, Epstein-Zin-Weil recursive utility). However, when solving intertemporal choice problems, the peculiar construction of Kihlstrom-Mirman preferences induces dynamic inconsistency - that is, the dynamic programming principle fails to hold. Our main contribution is therefore to address this challenge. In doing so, we provide a formal template to address the time-inconsistency of Kihlstrom-Mirman preferences in Markovian settings by means of equilibrium control theory. As an application, we study a consumption-investment problem for an agent with constant relative risk aversion and constant elasticity of substitution.