pwc-archive/papers-with-abstracts huggingface dataset:
Bundle Choice Model with Endogenous Regressors: An Application to Soda Tax
Authors: Tao Sun
Date: 2024-12-08 00:00:00

This paper proposes a Bayesian factor-augmented bundle choice model to estimate joint consumption as well as the substitutability and complementarity of multiple goods in the presence of endogenous regressors. The model extends the two primary treatments of endogeneity in existing bundle choice models: (1) endogenous market-level prices and (2) time-invariant unobserved individual heterogeneity. A Bayesian sparse factor approach is employed to capture high-dimensional error correlations that induce taste correlation and endogeneity. Time-varying factor loadings allow for more general individual-level and time-varying heterogeneity and endogeneity, while the sparsity induced by the shrinkage prior on loadings balances flexibility with parsimony. Applied to a soda tax in the context of complementarities, the new approach captures broader effects of the tax that were previously overlooked. Results suggest that a soda tax could yield additional health benefits by marginally decreasing the consumption of salty snacks along with sugary drinks, extending the health benefits beyond the reduction in sugar consumption alone.
Convolution Mode Regression
Authors: Eduardo Schirmer Finn, Eduardo Horta
Date: 2024-12-07 00:00:00
Tasks: quantile regression, regression

For highly skewed or fat-tailed distributions, mean or median-based methods often fail to capture the central tendencies in the data. Despite being a viable alternative, estimating the conditional mode given certain covariates (or mode regression) presents significant challenges. Nonparametric approaches suffer from the "curse of dimensionality", while semiparametric strategies often lead to non-convex optimization problems. In order to avoid these issues, we propose a novel mode regression estimator that relies on an intermediate step of inverting the conditional quantile density. In contrast to existing approaches, we employ a convolution-type smoothed variant of the quantile regression. Our estimator converges uniformly over the design points of the covariates and, unlike previous quantile-based mode regressions, is uniform with respect to the smoothing bandwidth. Additionally, the Convolution Mode Regression is dimension-free, carries no issues regarding optimization and preliminary simulations suggest the estimator is normally distributed in finite samples.
Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups
Authors: Minseog Oh, Donggyu Kim
Date: 2024-12-07 00:00:00

In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local group structure more effectively, this study introduces an inverse covariance matrix-based financial adjacency matrix (IFAM) that utilizes negative values of the inverse covariance matrix. We show that IFAM ensures that the edge density between different groups vanishes, while that within the same group remains non-vanishing. This reduces falsely detected connections and helps identify local group membership accurately. To estimate IFAM under the multi-level factor model, we introduce a factor-adjusted GLASSO estimator to address the prevalent global factor effect in the inverse covariance matrix. An empirical study using returns from international stocks across 20 financial markets demonstrates that incorporating IFAM effectively detects latent local groups, which helps improve the minimum variance portfolio allocation performance.
Minimum Sliced Distance Estimation in a Class of Nonregular Econometric Models
Authors: Yanqin Fan, Hyeonseok Park
Date: 2024-12-07 00:00:00

This paper proposes minimum sliced distance estimation in structural econometric models with possibly parameter-dependent supports. In contrast to likelihood-based estimation, we show that under mild regularity conditions, the minimum sliced distance estimator is asymptotically normally distributed leading to simple inference regardless of the presence/absence of parameter dependent supports. We illustrate the performance of our estimator on an auction model.
Delving into Youth Perspectives on In-game Gambling-like Elements: A Proof-of-Concept Study Utilising Large Language Models for Analysing User-Generated Text Data
Authors: Thomas Krause, Steffen Otterbach, Johannes Singer
Date: 2024-12-12 00:00:00

This report documents the development, test, and application of Large Language Models (LLMs) for automated text analysis, with a specific focus on gambling-like elements in digital games, such as lootboxes. The project aimed not only to analyse user opinions and attitudes towards these mechanics, but also to advance methodological research in text analysis. By employing prompting techniques and iterative prompt refinement processes, the study sought to test and improve the accuracy of LLM-based text analysis. The findings indicate that while LLMs can effectively identify relevant patterns and themes on par with human coders, there are still challenges in handling more complex tasks, underscoring the need for ongoing refinement in methodologies. The methodological advancements achieved through this study significantly enhance the application of LLMs in real-world text analysis. The research provides valuable insights into how these models can be better utilized to analyze complex, user-generated content.
Moving to the suburbs? Exploring the potential impact of work-from-home on suburbanization in Poland
Authors: Beata Woźniak-Jęchorek, Sławomir Kuźmar, David Bole
Date: 2024-12-10 00:00:00

The main goal of this paper is to assess the likelihood of office workers relocating to the suburbs due to work-from-home opportunities and the key factors influencing these preferences. Our study focuses on Poland, a country with different cultural individualism at work, which can impact work-from-home preferences and, indirectly, home relocation desires. Given the methodological limitations of official data on remote work practices in Poland, we conducted an original survey, gathering primary data from a nationally representative sample of office workers living in cities with populations exceeding 100,000. To investigate the factors shaping employees' preferences for suburban relocation driven by remote work, we utilized logistic regression to analyze the effects of socio-economic and employment characteristics, commuting experiences, and reported changes in work productivity. Our findings reveal that age, mode of commuting, perceived changes in work productivity, and sector ownership are significant determinants, strongly affecting home relocation preferences in response to work-from-home opportunities. These results align with previous research, typically based on data from countries with different cultural frameworks and more developed work-from-home practices.
India's residential space cooling transition: Decarbonization ambitions since the turn of millennium
Authors: Ran Yan, Nan Zhou, Minda Ma, Chao Mao
Date: 2024-12-09 00:00:00

As an emerging emitter poised for significant growth in space cooling demand, India requires comprehensive insights into historical emission trends and decarbonization performance to shape future low-carbon cooling strategies. By integrating a bottom-up demand resource energy analysis model and a top-down decomposition method, this study is the first to conduct a state-level analysis of carbon emission trends and the corresponding decarbonization efforts for residential space cooling in urban and rural India from 2000 to 2022. The results indicate that (1) the carbon intensity of residential space cooling in India increased by 292.4% from 2000 to 2022, reaching 513.8 kilograms of carbon dioxide per household. The net state domestic product per capita, representing income, emerged as the primary positive contributor. (2) The increase in carbon emissions from space cooling can be primarily attributed to the use of fans. While fan-based space cooling has nearly saturated Indian urban households, it is anticipated to persist as the primary cooling method in rural households for decades. (3) States with higher decarbonization potential are concentrated in two categories: those with high household income and substantial cooling appliance ownership and those with pronounced unmet cooling demand but low household income and hot climates. Furthermore, it is believed that promoting energy-efficient building designs can be prioritized to achieve affordable space cooling. Overall, this study serves as an effective foundation for formulating and promoting India's future cooling action plan, addressing the country's rising residential cooling demands and striving toward its net-zero goal by 2070.
Measuring Consumer Sensitivity to Audio Advertising: A Long-Run Field Experiment on Pandora Internet Radio
Authors: Ali Goli, Jason Huang, David Reiley, Nickolai M. Riabov
Date: 2024-12-07 00:00:00
Tasks: Experimental Design, Sensitivity

A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital content provision. The experiment randomized listeners into nine treatment groups, each of which received a different level of audio advertising interrupting their music listening, with the highest treatment group receiving more than twice as many ads as the lowest treatment group. By maintaining consistent treatment assignment for 21 months, we measure long-run demand effects and find ad-load sensitivity three times greater than what we would have obtained from a month-long experiment. We show the negative impact on the number of hours listened, days listened, and probability of listening at all in the final month. Using an experimental design that separately varies the number of commercial interruptions per hour and the number of ads per commercial interruption, we find that listeners primarily respond to the total number of ads per hour, with a slight preference for more frequent but shorter ad breaks. Lastly, we find that increased ad load led to an increase in the number of paid ad-free subscriptions to Pandora. Importantly, we show that observational methods often lead to biased or even directionally incorrect estimates of these effects, highlighting the value of experimental data.
The Economics of Equilibrium with Indivisible Goods
Authors: Ravi Jagadeesan, Alexander Teytelboym
Date: 2024-12-10 00:00:00

This paper develops a theory of competitive equilibrium with indivisible goods based entirely on economic conditions on demand. The key idea is to analyze complementarity and substitutability between bundles of goods, rather than merely between goods themselves. This approach allows us to formulate sufficient, and essentially necessary, conditions for equilibrium existence, which unify settings with complements and settings with substitutes. Our analysis has implications for auction design.
Network and timing effects in social learning
Authors: Wade Hann-Caruthers, Minghao Pan, Omer Tamuz
Date: 2024-12-10 00:00:00

We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do.
Consumption Dependent Random Utility
Authors: Christopher Turansick
Date: 2024-12-06 00:00:00

We study a dynamic random utility model that allows for consumption dependence. We axiomatically analyze this model and find insights that allow us to distinguish between behavior that arises due to consumption dependence and behavior that arises due to state dependence. As part of our analysis, we show that it is impossible to distinguish between myopic and dynamically sophisticated agents when there are well defined marginal choices in each period. Building on our axiomatic analysis, we develop a revealed preference test for consumption dependent random utility. Our test can be implemented with real data, and we show that our test offers computational improvements over the natural extension of Kitamura and Stoye (2018) to our environment.
CSSinger: End-to-End Chunkwise Streaming Singing Voice Synthesis System Based on Conditional Variational Autoencoder
Authors: Jianwei Cui, Yu Gu, Shihao Chen, Jie Zhang, Liping Chen, LiRong Dai
Date: 2024-12-12 00:00:00
Tasks: Audio Synthesis, Singing Voice Synthesis, text-to-speech, Text to Speech

Singing Voice Synthesis (SVS) aims to generate singing voices of high fidelity and expressiveness. Conventional SVS systems usually utilize an acoustic model to transform a music score into acoustic features, followed by a vocoder to reconstruct the singing voice. It was recently shown that end-to-end modeling is effective in the fields of SVS and Text to Speech (TTS). In this work, we thus present a fully end-to-end SVS method together with a chunkwise streaming inference to address the latency issue for practical usages. Note that this is the first attempt to fully implement end-to-end streaming audio synthesis using latent representations in VAE. We have made specific improvements to enhance the performance of streaming SVS using latent representations. Experimental results demonstrate that the proposed method achieves synthesized audio with high expressiveness and pitch accuracy in both streaming SVS and TTS tasks.
Spatio-temporal Latent Representations for the Analysis of Acoustic Scenes in-the-wild
Authors: Claudia Montero-Ramírez, Esther Rituerto-González, Carmen Peláez-Moreno
Date: 2024-12-10 00:00:00
Tasks: Information Retrieval, Self-Supervised Learning

In the field of acoustic scene analysis, this paper presents a novel approach to find spatio-temporal latent representations from in-the-wild audio data. By using WE-LIVE, an in-house collected dataset that includes audio recordings in diverse real-world environments together with sparse GPS coordinates, self-annotated emotional and situational labels, we tackle the challenging task of associating each audio segment with its corresponding location as a pretext task, with the final aim of acoustically detecting violent (anomalous) contexts, left as further work. By generating acoustic embeddings and using the self-supervised learning paradigm, we aim to use the model-generated latent space to acoustically characterize the spatio-temporal context. We use YAMNet, an acoustic events classifier trained in AudioSet to temporally locate and identify acoustic events in WE-LIVE. In order to transform the discrete acoustic events into embeddings, we compare the information-retrieval-based TF-IDF algorithm and Node2Vec as an analogy to Natural Language Processing techniques. A VAE is then trained to provide a further adapted latent space. The analysis was carried out by measuring the cosine distance and visualizing data distribution via t-Distributed Stochastic Neighbor Embedding, revealing distinct acoustic scenes. Specifically, we discern variations between indoor and subway environments. Notably, these distinctions emerge within the latent space of the VAE, a stark contrast to the random distribution of data points before encoding. In summary, our research contributes a pioneering approach for extracting spatio-temporal latent representations from in-the-wild audio data.
Versatile Volumetric Medical Image Coding for Human-Machine Vision
Authors: Jietao Chen, WeiJie Chen, Qianjian Xing, Feng Yu
Date: 2024-12-12 00:00:00
Tasks: Image Compression, Image Reconstruction

Neural image compression (NIC) has received considerable attention due to its significant advantages in feature representation and data optimization. However, most existing NIC methods for volumetric medical images focus solely on improving human-oriented perception. For these methods, data need to be decoded back to pixels for downstream machine learning analytics, which is a process that lowers the efficiency of diagnosis and treatment in modern digital healthcare scenarios. In this paper, we propose a Versatile Volumetric Medical Image Coding (VVMIC) framework for both human and machine vision, enabling various analytics of coded representations directly without decoding them into pixels. Considering the specific three-dimensional structure distinguished from natural frame images, a Versatile Volumetric Autoencoder (VVAE) module is crafted to learn the inter-slice latent representations to enhance the expressiveness of the current-slice latent representations, and to produce intermediate decoding features for downstream reconstruction and segmentation tasks. To further improve coding performance, a multi-dimensional context model is assembled by aggregating the inter-slice latent context with the spatial-channel context and the hierarchical hypercontext. Experimental results show that our VVMIC framework maintains high-quality image reconstruction for human vision while achieving accurate segmentation results for machine-vision tasks compared to a number of reported traditional and neural methods.
Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm
Authors: Marien Renaud, Julien Hermant, Nicolas Papadakis
Date: 2024-12-11 00:00:00
Tasks: Denoising, Image Restoration

Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to recover a clean image from a degraded observation. These algorithms are known to be flexible to changes of degradation and to perform state-of-the-art restoration. Recently, significant efforts have been made to explore new stochastic algorithms based on the Plug-and-Play or REgularization by Denoising (RED) frameworks, such as SNORE, which is a convergent stochastic gradient descent algorithm. A variant of this algorithm, named SNORE Prox, reaches state-of-the-art performances, especially for inpainting tasks. However, the convergence of SNORE Prox, that can be seen as a stochastic proximal gradient descent, has not been analyzed so far. In this paper, we prove the convergence of SNORE Prox under non convex assumptions.
Coarse-to-Fine: A Dual-Phase Channel-Adaptive Method for Wireless Image Transmission
Authors: Hanlei Li, Guangyi Zhang, Kequan Zhou, Yunlong Cai, Guanding Yu
Date: 2024-12-11 00:00:00
Tasks: Reinforcement Learning (RL)

Developing channel-adaptive deep joint source-channel coding (JSCC) systems is a critical challenge in wireless image transmission. While recent advancements have been made, most existing approaches are designed for static channel environments, limiting their ability to capture the dynamics of channel environments. As a result, their performance may degrade significantly in practical systems. In this paper, we consider time-varying block fading channels, where the transmission of a single image can experience multiple fading events. We propose a novel coarse-to-fine channel-adaptive JSCC framework (CFA-JSCC) that is designed to handle both significant fluctuations and rapid changes in wireless channels. Specifically, in the coarse-grained phase, CFA-JSCC utilizes the average signal-to-noise ratio (SNR) to adjust the encoding strategy, providing a preliminary adaptation to the prevailing channel conditions. Subsequently, in the fine-grained phase, CFA-JSCC leverages instantaneous SNR to dynamically refine the encoding strategy. This refinement is achieved by re-encoding the remaining channel symbols whenever the channel conditions change. Additionally, to reduce the overhead for SNR feedback, we utilize a limited set of channel quality indicators (CQIs) to represent the channel SNR and further propose a reinforcement learning (RL)-based CQI selection strategy to learn this mapping. This strategy incorporates a novel reward shaping scheme that provides intermediate rewards to facilitate the training process. Experimental results demonstrate that our CFA-JSCC provides enhanced flexibility in capturing channel variations and improved robustness in time-varying channel environments.
A Structurally Coherent Spatial Phase Estimate
Authors: Brian Knight, Naoki Saito
Date: 2024-12-11 00:00:00

The monogenic signal (MS) was introduced by Felsberg and Sommer, and independently by Larkin under the name vortex operator. It is a two-dimensional (2D) analog of the well-known analytic signal, and allows for direct amplitude and phase demodulation of (amplitude and phase) modulated images so long as the signal is intrinsically one-dimensional (i1D). Felsberg's PhD dissertation also introduced the structure multivector (SMV), a model allowing for intrinsically 2D (i2D) structure. While the monogenic signal has become a well-known tool in the image processing community, the SMV is little used, although even in the case of i1D signals it provides a more robust orientation estimation than the MS. We argue the SMV is more suitable in standard i1D image feature extraction due to the this improvement, and extend the steerable wavelet frames of Held et al. to accommodate the additional features of the SMV. We then propose a novel quality map based on local orientation variance which values structurally coherent patches. This yields a multiscale phase estimate which performs well even when signal to noise ratio (SNR) is $\le$ 1. The performance is evaluated on several synthetic phase estimation tasks as well as on a fine-scale fingerprint registration task related to the 2D phase demodulation problem.
Closed-Form Approximation of the Total Variation Proximal Operator
Authors: Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2024-12-10 00:00:00
Tasks: Computed Tomography (CT), Denoising, Form, Image Denoising, Image Reconstruction

Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using proximal methods, but the way in which they are applied is constrained by the absence of a closed-form expression for the proximal operator of the TV function. A closed-form approximation of the TV proximal operator has previously been proposed, but its accuracy was not theoretically explored in detail. We address this gap by making several new theoretical contributions, proving that the approximation leads to a proximal operator of some convex function, that it always decreases the TV function, and that its error can be fully characterized and controlled with its scaling parameter. We experimentally validate our theoretical results on image denoising and sparse-view computed tomography (CT) image reconstruction.