pwc-archive/papers-with-abstracts huggingface dataset:
Serial-Dependence and Persistence Robust Inference in Predictive Regressions
Authors: Jean-Yves Pitarakis
Date: 2025-02-01 00:00:00

This paper introduces a new method for testing the statistical significance of estimated parameters in predictive regressions. The approach features a new family of test statistics that are robust to the degree of persistence of the predictors. Importantly, the method accounts for serial correlation and conditional heteroskedasticity without requiring any corrections or adjustments. This is achieved through a mechanism embedded within the test statistics that effectively decouples serial dependence present in the data. The limiting null distributions of these test statistics are shown to follow a chi-square distribution, and their asymptotic power under local alternatives is derived. A comprehensive set of simulation experiments illustrates their finite sample size and power properties.
Confidence intervals for intentionally biased estimators
Authors: David M. Kaplan, Xin Liu
Date: 2025-02-01 00:00:00

We propose and study three confidence intervals (CIs) centered at an estimator that is intentionally biased to reduce mean squared error. The first CI simply uses an unbiased estimator's standard error; compared to centering at the unbiased estimator, this CI has higher coverage probability for confidence levels above 91.7%, even if the biased and unbiased estimators have equal mean squared error. The second CI trades some of this "excess" coverage for shorter length. The third CI is centered at a convex combination of the two estimators to further reduce length. Practically, these CIs apply broadly and are simple to compute.
Innovative activities of Activision Blizzard: A patent network analysis
Authors: Artur F. Tomeczek
Date: 2025-02-04 00:00:00
Tasks: Patent classification, Starcraft

Microsoft's acquisition of Activision Blizzard valued at $68.7 billion has drastically altered the landscape of the video game industry. At the time of the takeover, the intellectual properties of Activision Blizzard included World of Warcraft, Diablo, Hearthstone, StarCraft, Overwatch, Battle.net, Candy Crush Saga, and Call of Duty. This article aims to explore the patenting activity of Activision Blizzard between 2008 (the original merger) and 2023 (the Microsoft acquisition). Four IPC code co-occurrence networks (co-classification maps) are constructed and analyzed based on the patent data downloaded from the WIPO Patentscope database. International Patent Classification (IPC) codes are a language agnostic system for the classification of patents. When multiple IPC codes co-occur in a patent, it shows that the technologies are connected. These relationships can be used for patent mapping. The analysis identifies the prolific and bridging technologies of Activision Blizzard and explores its synergistic role as a subsidiary of Microsoft Corporation.
The Weitzman Premium on the Social Cost of Carbon
Authors: Jinchi Dong, Richard S. J. Tol, Fangzhi Wang
Date: 2025-02-03 00:00:00

Preference heterogeneity massively increases the social cost of carbon. We call this the Weitzman premium. Uncertainty about an exponential discount rate implies a hyperbolic discount rate, which in the near term is equal to the average discount rate but in the long term falls to the minimum discount rate. We calibrate the pure rate of time preference and the inverse of the elasticity of intertemporal substitution of 79,273 individuals from 76 countries and compute the corresponding social cost of carbon. Compared to the social cost of carbon for average time preferences, the average social cost of carbon is 6 times as large in the base calibration, and up to 200 times as large in sensitivity analyses.
The Market Design for Formulary Positions
Authors: Lawrence W. Abrams
Date: 2025-02-03 00:00:00
Tasks: Position

The exchange of rebates for formulary positions is conceptualized as a multi-round combinatorial position auction. This paper develops a linear assignment model of the winners' determination equation of this auction where the bases are net unit prices after unit rebates and expected demand.
Can the Nexus of Scaling Laws Coupled with Constant or Variable Elasticity of Substitution Predict AI and Other Technology Adoption?
Authors: Rajesh P. Narayanan, R. Kelley Pace
Date: 2025-02-02 00:00:00

Emergent technologies such as solar power, electric vehicles, and artificial intelligence (AI) often exhibit exponential or power function price declines and various ``S-curves'' of adoption. We show that under CES and VES utility, such price and adoption curves are functionally linked. When price declines follow Moore's, Wright's and AI scaling "Laws,'' the S-curve of adoption is Logistic or Log-Logistic whose slope depends on the interaction between an experience parameter and the elasticity of substitution between the incumbent and emergent good. These functional relations can serve as a building block for more complex models and guide empirical specifications of technology adoption.
Dynamic Structures of Knowledge Production: Citation Rates in Hydrogen Technologies
Authors: David Dekker, Dimitirs Christopoulos, Heather McGregor
Date: 2025-02-02 00:00:00

We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production," reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in $H_2$ pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing.
Testing Capacity-Constrained Learning
Authors: Andrew Caplin, Daniel Martin, Philip Marx, Anastasiia Morozova, Leshan Xu
Date: 2025-01-31 00:00:00

We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). We show that choice data are consistent with capacity-constrained learning if and only if they satisfy a No Improving (Action or Attention) Switches (NIS) condition. Based on existing experiments in which the incentives for being correct are varied, we find strong evidence that participants fail NIS for a wide range of standard perceptual tasks: identifying the proportion of ball colors, recognizing shapes, and counting the number of balls. However, we find that this is not true for all existing perceptual tasks in the literature, which offers insights into settings where we do or do not expect incentives to impact the extent of attention.
A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment
Authors: Viet Trinh
Date: 2025-01-31 00:00:00
Tasks: Algorithmic Trading, Decision Making, Management, Portfolio Optimization

Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.
Study on the impact of trade policy uncertainty on the performance of enterprise ESG performance
Authors: Hanqin Chen, Ye Lu, Huaqin Huang
Date: 2025-01-17 00:00:00

Trade policy uncertainty has become a significant feature of today's global economy. While its impact on free trade is evident, its microeconomic effects remain open to debate. This study explores the influence of trade policy uncertainty on corporate ESG performance and its underlying mechanisms, using data from A-share listed companies in China from 2010 to 2020. The findings reveal that increased trade policy uncertainty significantly and robustly enhances corporate ESG performance. Heterogeneity analysis indicates that high-tech enterprises are better equipped to improve their ESG performance in response to trade policy uncertainty. Furthermore, strengthening internal controls and appointing CEOs with environmental backgrounds also help firms seize the opportunities arising from trade policy uncertainty. In terms of mechanisms, trade policy uncertainty intensifies industry competition, compelling firms to enhance their ESG performance to gain market share. Additionally, it stimulates green technological innovation, further optimizing ESG outcomes. Therefore, efforts should focus on improving the ESG standards system, establishing ESG incentive policies, increasing the transparency and predictability of trade policies, and promoting corporate green development to advance national sustainable development goals.
Signaling Design
Authors: Matteo Camboni, Mingzi Niu, Mallesh M. Pai, Rakesh Vohra
Date: 2025-02-04 00:00:00
Tasks: Informativeness

We revisit the classic job-market signaling model of \cite{spence1973job}, introducing profit-seeking schools as intermediaries that design the mapping from candidates' efforts to job-market signals. Each school commits to an attendance fee and a monitoring policy. We show that, in equilibrium, a monopolist school captures the entire social surplus by committing to low information signals and charging fees that extract students' surplus from being hired. In contrast, competition shifts surplus to students, with schools vying to attract high-ability students, enabling them to distinguish themselves from their lower-ability peers. However, this increased signal informativeness leads to more wasteful effort in equilibrium, contrasting with the usual argument that competition enhances social efficiency. This result may be reversed if schools face binding fee caps or students are credit-constrained.
Inertial Updating with General Information
Authors: Adam Dominiak, Matthew Kovach, Gerelt Tserenjigmid
Date: 2025-02-02 00:00:00

We study belief revision when information is represented by a set of probability distributions, or general information. General information extends the standard event notion while including qualitative information (A is more likely than B), interval information (A has a ten-to-twenty percent chance), and more. We behaviorally characterize Inertial Updating: the decision maker's posterior is of minimal subjective distance from her prior, given the information constraint. Further, we introduce and characterize a notion of Bayesian updating for general information and show that Bayesian agents may disagree. We also behaviorally characterize f-divergences, the class of distances consistent with Bayesian updating.
A monotone solution to the problem of bargaining with transfers
Authors: Gregorio Curello, Sam Jindani
Date: 2025-02-01 00:00:00

We propose a solution to the problem of bargaining with transfers, along with an axiomatisation of the solution. Each player receives a weighted average of her contributions to each coalition's claim, where a coalition's claim is determined by its best allocation in the bargaining set. The key axioms are additivity and a property we call marginal monotonicity, which states that increasing a player's payoff in an allocation of the bargaining set, either outright or by means of transfers from other players, must benefit that player.
Frame-dependent Random Utility
Authors: Paul H. Y. Cheung, Yusufcan Masatlioglu
Date: 2025-01-31 00:00:00
Tasks: Decision Making

We explore the influence of framing on decision-making, where some products are framed (e.g., displayed, recommended, endorsed, or labeled). We introduce a novel choice function that captures observed variations in framed alternatives. Building on this, we conduct a comprehensive revealed preference analysis, employing the concept of frame-dependent utility using both deterministic and probabilistic data. We demonstrate that simple and intuitive behavioral principles characterize our frame-dependent random utility model (FRUM), which offers testable conditions even with limited data. Finally, we introduce a parametric model to increase the tractability of FRUM. We also discuss how to recover the choice types in our framework.
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study
Authors: Mattson Ogg
Date: 2025-02-04 00:00:00
Tasks: Self-Supervised Learning, Specificity, Transfer Learning

Self-supervised learning (SSL) algorithms have emerged as powerful tools that can leverage large quantities of unlabeled audio data to pre-train robust representations that support strong performance on diverse downstream tasks. Up to now these have mostly been developed separately for speech and non-speech applications. Here, we explored the domain specificity of a convolutional model's pre-training data relative to different downstream speech and non-speech tasks using a self-supervised pre-training approach (BYOL-A). We found that these pre-trained models (regardless of whether they were pre-trained on speech data, non-speech data or both) enabled good performance on nearly all downstream tasks, beating or nearly matching the performance of popular domain-specific models. Only small domain-specificity advantages were observed between the different pre-training datasets. The popular domain-specific models used as baselines performed very well in their target domains, but generally faltered outside of them. Together, these results demonstrate that SSL methods can be a powerful way to learn flexible representations for domain specific data without labels. These models can be a powerful resource for later transfer learning, fine-tuning or data exploration applications when the downstream data are similar, but also perhaps when there may be a domain mismatch.
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition
Authors: Martijn Bartelds, Ananjan Nandi, Moussa Koulako Bala Doumbouya, Dan Jurafsky, Tatsunori Hashimoto, Karen Livescu
Date: 2025-02-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Modern deep learning models often achieve high overall performance, but consistently fail on specific subgroups. Group distributionally robust optimization (group DRO) addresses this problem by minimizing the worst-group loss, but it fails when group losses misrepresent performance differences between groups. This is common in domains like speech, where the widely used connectionist temporal classification (CTC) loss scales with input length and varies with linguistic and acoustic properties, leading to spurious differences between group losses. We present CTC-DRO, which addresses the shortcomings of the group DRO objective by smoothing the group weight update to prevent overemphasis on consistently high-loss groups, while using input length-matched batching to mitigate CTC's scaling issues. We evaluate CTC-DRO on the task of multilingual automatic speech recognition (ASR) across five language sets from the ML-SUPERB 2.0 benchmark. CTC-DRO consistently outperforms group DRO and CTC-based baseline models, reducing the worst-language error by up to 47.1% and the average error by up to 32.9%. CTC-DRO can be applied to ASR with minimal computational costs, and offers the potential for reducing group disparities in other domains with similar challenges.
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis
Authors: Divya Bharti, Sriprabha Ramanarayanan, Sadhana S, Kishore Kumar M, Keerthi Ram, Harsh Agarwal, Ramesh Venkatesan, Mohanasankar Sivaprakasam
Date: 2025-02-04 00:00:00
Tasks: Anatomy, Generative Adversarial Network, Image Generation, SSIM

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue perfusion in abnormal lesions and radiological suggestions for biopsy. However, DCE-MRI involves the administration of a Gadolinium based (Gad) contrast agent, which is associated with a risk of toxicity in the body. Previous deep learning approaches that synthesize DCE-MR images employ unimodal non-contrast or low-dose contrast MRI images lacking focus on the local perfusion information within the anatomy of interest. We propose AAD-DCE, a generative adversarial network (GAN) with an aggregated attention discriminator module consisting of global and local discriminators. The discriminators provide a spatial embedded attention map to drive the generator to synthesize early and late response DCE-MRI images. Our method employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient (ADC), and T1 pre-contrast for image synthesis. Extensive comparative and ablation studies on the ProstateX dataset show that our model (i) is agnostic to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late response, and (ii) emphasize the importance of attention ensembling. Our code is available at https://github.com/bhartidivya/AAD-DCE.
The Skin Game: Revolutionizing Standards for AI Dermatology Model Comparison
Authors: Łukasz Miętkiewicz, Leon Ciechanowski, Dariusz Jemielniak
Date: 2025-02-04 00:00:00
Tasks: Classification, Data Augmentation, image-classification, Image Classification

Deep Learning approaches in dermatological image classification have shown promising results, yet the field faces significant methodological challenges that impede proper evaluation. This paper presents a dual contribution: first, a systematic analysis of current methodological practices in skin disease classification research, revealing substantial inconsistencies in data preparation, augmentation strategies, and performance reporting; second, a comprehensive training and evaluation framework demonstrated through experiments with the DINOv2-Large vision transformer across three benchmark datasets (HAM10000, DermNet, ISIC Atlas). The analysis identifies concerning patterns, including pre-split data augmentation and validation-based reporting, potentially leading to overestimated metrics, while highlighting the lack of unified methodology standards. The experimental results demonstrate DINOv2's performance in skin disease classification, achieving macro-averaged F1-scores of 0.85 (HAM10000), 0.71 (DermNet), and 0.84 (ISIC Atlas). Attention map analysis reveals critical patterns in the model's decision-making, showing sophisticated feature recognition in typical presentations but significant vulnerabilities with atypical cases and composite images. Our findings highlight the need for standardized evaluation protocols and careful implementation strategies in clinical settings. We propose comprehensive methodological recommendations for model development, evaluation, and clinical deployment, emphasizing rigorous data preparation, systematic error analysis, and specialized protocols for different image types. To promote reproducibility, we provide our implementation code through GitHub. This work establishes a foundation for rigorous evaluation standards in dermatological image classification and provides insights for responsible AI implementation in clinical dermatology.
Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer
Authors: Jeong Hoon Lee, Cynthia Xinran Li, Hassan Jahanandish, Indrani Bhattacharya, Sulaiman Vesal, LiChun Zhang, Shengtian Sang, Moon Hyung Choi, Simon John Christoph Soerensen, Steve Ran Zhou, Elijah Richard Sommer, Richard Fan, Pejman Ghanouni, Yuze Song, Tyler M. Seibert, Geoffrey A. Sonn, Mirabela Rusu
Date: 2025-02-01 00:00:00
Tasks: Contrastive Learning, Diagnostic, Specificity

Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways.