pwc-archive/papers-with-abstracts huggingface dataset:
Unit Averaging for Heterogeneous Panels
Authors: Christian Brownlees, Vladislav Morozov
Date: 2022-10-25 00:00:00

In this work we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion we derive. We analyze the properties of the resulting minimum MSE unit averaging estimator in a local heterogeneity framework inspired by the literature on frequentist model averaging, and we derive the local asymptotic distribution of the estimator and the corresponding weights. The benefits of the procedure are showcased with an application to forecasting unemployment rates for a panel of German regions.
GLS under Monotone Heteroskedasticity
Authors: Yoichi Arai, Taisuke Otsu, Mengshan Xu
Date: 2022-10-25 00:00:00
Tasks: regression

The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requires a restrictive functional form assumption for parametric estimation or smoothing parameters for nonparametric estimation. In this paper, we propose an alternative approach to estimate the conditional variance function under nonparametric monotonicity constraints by utilizing the isotonic regression method. Our GLS estimator is shown to be asymptotically equivalent to the infeasible GLS estimator with knowledge of the conditional error variance, and involves only some tuning to trim boundary observations, not only for point estimation but also for interval estimation or hypothesis testing. Our analysis extends the scope of the isotonic regression method by showing that the isotonic estimates, possibly with generated variables, can be employed as first stage estimates to be plugged in for semiparametric objects. Simulation studies illustrate excellent finite sample performances of the proposed method. As an empirical example, we revisit Acemoglu and Restrepo's (2017) study on the relationship between an aging population and economic growth to illustrate how our GLS estimator effectively reduces estimation errors.
Prediction intervals for economic fixed-event forecasts
Authors: Fabian Krüger, Hendrik Plett
Date: 2022-10-24 00:00:00
Tasks: Prediction, Prediction Intervals, regression

The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same (`fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct such a measure, we consider forecast postprocessing techniques tailored to the fixed-event case. We develop regression methods that impose constraints motivated by the problem at hand, and use these methods to construct prediction intervals for gross domestic product (GDP) growth in Germany and the US.
Measuring Transition Risk in Investment Funds
Authors: Ricardo Crisostomo
Date: 2022-10-27 00:00:00

We develop a comprehensive framework to measure the impact of the climate transition on investment portfolios. Our analysis is enriched by including geographical, sectoral, company and ISIN-level data to assess transition risk. We find that investment funds suffer a moderate 5.7% loss upon materialization of a high transition risk scenario. However, the risk distribution is significantly left-skewed, with the worst 1% funds experiencing an average loss of 21.3%. In terms of asset classes, equities are the worst performers (-12.7%), followed by corporate bonds (-5.6%) and government bonds (-4.8%). We discriminate among financial instruments by considering the carbon footprint of specific counterparties and the credit rating, duration, convexity and volatility of individual exposures. We find that sustainable funds are less exposed to transition risk and perform better than the overall fund sector in the low-carbon transition, validating their choice as green investments.
Model of work motivation based on happiness: pandemic related study
Authors: Joanna Nieżurawska, Radosław A. Kycia, Iveta Ludviga, Agnieszka Niemczynowicz
Date: 2022-10-26 00:00:00
Tasks: Survey

This study aims to enrich the current literature by providing a new approach to motivating Generation Z employees in Poland. Employees need to be motivated in order to be efficient at doing a particular task at the workplace. As young people born between 1995 and 2004 called Generation Z, enter the labour market it, is essential to consider how employees' motivation might be affected. Traditionally identified motivators are known, but many reports indicate that the motivation continues decreasing. This situation causes some perturbations in business and fluctuations of staff. In order to prevent this situation, the employers are looking for new solutions to motivate the employees. A quantitative approach was used to collect new evidence from 200 Polish respondents completing an online survey. The research were conducted before and during pandemic time. We report and analyse the survey results conducted in Poland among representatives of Generation Z, who were employed for at least 6 months. We developed and validated a new approach to motivation using methodologies called Factor Analysis. Based on empirical verification, we found a new tool that connects employee motivation and selected areas of the Hygge concept called Hygge star model, which has the same semantics before and during Covid-19 pandemic.
The Influence of Payment Method: Do Consumers Pay More with Mobile Payment?
Authors: Yizhao Jiang
Date: 2022-10-26 00:00:00

The introduction of new payment methods has resulted in one of the most significant changes in the way we consume goods and services. In this paper, I present results of a field and a laboratory experiment designed to determine the effect of payment method (cash vs. mobile payment) on spending, and a meta-analysis of previous literature about payment method effect. In the field experiment, I collected cashier receipts from Chinese supermarkets. Compared to cash payment, mobile payments significantly increased the amount purchased and the average amount spent on each item. This effect was found to be particularly large for high price elasticity goods. In the laboratory experiment, participants were randomly assigned to one of four groups that varied with respect to the kind of payment and the kind of incentives, eliminating the potential endogeneity problem from the field experiment. I found that compared to cash, mobile payments lead to a significantly higher willingness to pay (WTP) for consumption. In contrast to others, I found that pain of paying does not moderate the payment method effect; however, other psychological factors were found to work as potential mechanisms for affecting WTP.
Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?
Authors: Zunian Luo
Date: 2022-10-26 00:00:00

Electric vehicle adoption is considered to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentives charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are often discussed as policy levers that can stimulate charging station supply and correct this market failure. Nonetheless, there is limited research examining the extent such subsidies are successful in promoting charging station supply. Using annual data on electric vehicle sales, charging station counts, and subsidy amounts from 57 California counties and a staggered difference-in-differences methodology, I find that charging station subsidies are highly effective: counties that adopt subsidies experience a 36% increase in charging station supply 2 years following subsidy adoption. This finding suggests that governmental intervention can help correct the market failure in the charging station market.
Reliability of Ideal Indexes
Authors: Gholamreza Hajargasht
Date: 2022-10-25 00:00:00

The Fisher and GEKS are celebrated as ideal bilateral and multilateral indexes due to their superior axiomatic and econ-theoretic properties. The Fisher index is the main index used for constructing CPI by statistical agencies and the GEKS is the index used for compiling PPPs in World Bank's International Comparison Program (ICP). Despite such a high status and the importance attached to these indexes, the stochastic approach to these indexes is not well-developed and no measures of reliability exist for these important economic statistics. The main objective of this paper is to fill this gap. We show how appropriate reliability measures for the Fisher and GEKS indexes, and other ideal price index numbers can be derived and how they should be interpreted and used in practice. In an application to 2017 ICP data on a sample of 173 countries, we estimate the Fisher and GEKS indexes along with their reliability measures, make comparisons with several other notable indexes and discuss the implications.
Optimal Mechanism Design for Agents with DSL Strategies: The Case of Sybil Attacks in Combinatorial Auctions
Authors: Yotam Gafni, Moshe Tennenholtz
Date: 2022-10-27 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

In robust decision making under uncertainty, a natural choice is to go with safety (aka security) level strategies. However, in many important cases, most notably auctions, there is a large multitude of safety level strategies, thus making the choice unclear. We consider two refined notions: (i) a term we call DSL (distinguishable safety level), and is based on the notion of ``discrimin'', which uses a pairwise comparison of actions while removing trivial equivalencies. This captures the fact that when comparing two actions an agent should not care about payoffs in situations where they lead to identical payoffs. (ii) The well-known Leximin notion from social choice theory, which we apply for robust decision-making. In particular, the leximin is always DSL but not vice-versa. We study the relations of these notions to other robust notions, and illustrate the results of their use in auctions and other settings. Economic design aims to maximize social welfare when facing self-motivated participants. In online environments, such as the Web, participants' incentives take a novel form originating from the lack of clear agent identity -- the ability to create Sybil attacks, i.e., the ability of each participant to act using multiple identities. It is well-known that Sybil attacks are a major obstacle for welfare-maximization. Our main result proves that when DSL attackers face uncertainty over the auction's bids, the celebrated VCG mechanism is welfare-maximizing even under Sybil attacks. Altogether, our work shows a successful fundamental synergy between robustness under uncertainty, economic design, and agents' strategic manipulations in online multi-agent systems.
Pricing and Electric Vehicle Charging Equilibria
Authors: Trivikram Dokka, Jorge Bruno, Sonali SenGupta, Chowdhury Mohammad Sakib Anwar
Date: 2022-10-26 00:00:00
Tasks: Management

We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfaction cost in to account, contrary to popular belief, herding only happens at lower EV uptake. Moreover, this is true for both linear and logistic dissatisfaction functions. We study the question of existence of price profiles to induce a desired equilibrium. We define two types of equilibria, distributed and non-distributed equilibria, and show that under logistic dissatisfaction, only non-distributed equilibria are possible by feasibly setting prices. In linear case, both type of equilibria are possible but price discrimination is necessary to induce distributed equilibria. Finally, we show that in the case of symmetric EV users, mediation cannot improve upon Nash equilibria.
The Economy's Potential: Duality and Equilibrium
Authors: Jacob K Goeree
Date: 2022-10-26 00:00:00

I introduce a concave function of allocations and prices -- the economy's potential -- which measures the difference between utilitarian social welfare and its dual. I show that Walrasian equilibria correspond to roots of the potential: allocations maximize weighted utility and prices minimize weighted indirect utility. Walrasian prices are "utility clearing" in the sense that the utilities consumers expect at Walrasian prices are just feasible. I discuss the implications of this simple duality for equilibrium existence, the welfare theorems, and the interpretation of Walrasian prices.
Revealed Preferences of One-Sided Matching
Authors: Andrew Tai
Date: 2022-10-25 00:00:00
Tasks: Combinatorial Optimization

Consider the object allocation (one-sided matching) model of Shapley and Scarf (1974). When final allocations are observed but agents' preferences are unknown, when might the allocation be in the core? This is a one-sided analogue of the model in Echenique, Lee, Shum, and Yenmez (2013). I build a model in which the strict core is testable -- an allocation is "rationalizable" if there is a preference profile putting it in the core. In this manner, I develop a theory of the revealed preferences of one-sided matching. I study rationalizability in both non-transferrable and transferrable utility settings. In the non-transferrable utility setting, an allocation is rationalizable if and only if: whenever agents with the same preferences are in the same potential trading cycle, they receive the same allocation. In the transferrable utility setting, an allocation is rationalizable if and only if: there exists a price vector supporting the allocation as a competitive equilibrium; or equivalently, it satisfies a cyclic monotonicity condition. The proofs leverage simple graph theory and combinatorial optimization and tie together classic theories of consumer demand revealed preferences and competitive equilibrium.
Jointly Resampling and Reconstructing Corrupted Images for Image Classification using Frequency-Selective Mesh-to-Grid Resampling
Authors: Viktoria Heimann, Andreas Spruck, André Kaup
Date: 2022-10-27 00:00:00
Tasks: Classification, image-classification, Image Classification

Neural networks became the standard technique for image classification throughout the last years. They are extracting image features from a large number of images in a training phase. In a following test phase, the network is applied to the problem it was trained for and its performance is measured. In this paper, we focus on image classification. The amount of visual data that is interpreted by neural networks grows with the increasing usage of neural networks. Mostly, the visual data is transmitted from the application side to a central server where the interpretation is conducted. If the transmission is disturbed, losses occur in the transmitted images. These losses have to be reconstructed using postprocessing. In this paper, we incorporate the widely applied bilinear and bicubic interpolation and the high-quality reconstruction Frequency-Selective Reconstruction (FSR) for the reconstruction of corrupted images. However, we propose to use Frequency-Selective Mesh-to-Grid Resampling (FSMR) for the joint reconstruction and resizing of corrupted images. The performance in terms of classification accuracy of EfficientNetB0, DenseNet121, DenseNet201, ResNet50 and ResNet152 is examined. Results show that the reconstruction with FSMR leads to the highest classification accuracy for most networks. Average improvements of up to 6.7 percentage points are possible for DenseNet121.
Optimising Different Feature Types for Inpainting-based Image Representations
Authors: Ferdinand Jost, Vassillen Chizhov, Joachim Weickert
Date: 2022-10-26 00:00:00
Tasks: Image Compression

Inpainting-based image compression is a promising alternative to classical transform-based lossy codecs. Typically it stores a carefully selected subset of all pixel locations and their colour values. In the decoding phase the missing information is reconstructed by an inpainting process such as homogeneous diffusion inpainting. Optimising the stored data is the key for achieving good performance. A few heuristic approaches also advocate alternative feature types such as derivative data and construct dedicated inpainting concepts. However, one still lacks a general approach that allows to optimise and inpaint the data simultaneously w.r.t. a collection of different feature types, their locations, and their values. Our paper closes this gap. We introduce a generalised inpainting process that can handle arbitrary features which can be expressed as linear equality constraints. This includes e.g. colour values and derivatives of any order. We propose a fully automatic algorithm that aims at finding the optimal features from a given collection as well as their locations and their function values within a specified total feature density. Its performance is demonstrated with a novel set of features that also includes local averages. Our experiments show that it clearly outperforms the popular inpainting with optimised colour data with the same density.
Multi-rate adaptive transform coding for video compression
Authors: Lyndon R. Duong, Bohan Li, Cheng Chen, Jingning Han
Date: 2022-10-25 00:00:00
Tasks: Data Compression, Quantization, Video Compression

Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive performance of end-to-end optimized compression with deep neural networks, the high computational and space demands of these models has prevented them from superseding the relatively simple transform coding found in conventional video codecs. In this study, we propose learned transforms and entropy coding that may either serve as (non)linear drop-in replacements, or enhancements for linear transforms in existing codecs. These transforms can be multi-rate, allowing a single model to operate along the entire rate-distortion curve. To demonstrate the utility of our framework, we augmented the DCT with learned quantization matrices and adaptive entropy coding to compress intra-frame AV1 block prediction residuals. We report substantial BD-rate and perceptual quality improvements over more complex nonlinear transforms at a fraction of the computational cost.
User Identification: A Key Enabler for Multi-User Vision-Aided Communications
Authors: Gouranga Charan, Ahmed Alkhateeb
Date: 2022-10-27 00:00:00
Tasks: Scene Understanding, User Identification

Vision-aided wireless communication is attracting increasing interest and finding new use cases in various wireless communication applications. These vision-aided communication frameworks leverage visual data captured, for example, by cameras installed at the infrastructure or mobile devices to construct some perception about the communication environment through the use of deep learning and advances in computer vision and visual scene understanding. Prior work has investigated various problems such as vision-aided beam, blockage, and hand-off prediction in millimeter wave (mmWave) systems and vision-aided covariance prediction in massive MIMO systems. This prior work, however, has focused on scenarios with a single object (user) in front of the camera. In this paper, we define the \textit{user identification} task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications. The objective of the user identification task is to identify the target communication user from the other candidate objects (distractors) in the visual scene. We develop machine learning models that process either one frame or a sequence of frames of visual and wireless data to efficiently identify the target user in the visual/communication environment. Using the large-scale multi-modal sense and communication dataset, DeepSense 6G, which is based on real-world measurements, we show that the developed approaches can successfully identify the target users with more than 97$\%$ accuracy in realistic settings. This paves the way for scaling the vision-aided wireless communication applications to real-world scenarios and practical deployments.
Simple Method for Perfect Reconstruction QMF Filter Design
Authors: Fernando Martin-Rodriguez, Fernando Isasi-de-Vicente, Monica Fernandez-Barciela
Date: 2022-10-27 00:00:00

The purpose of this work is the design of FIR QMF (Quadrature Mirror Filters) filters of perfect reconstruction and odd number of coefficients (even order). By design, these filters will have linear phase and integer delay. These filter pairs have many applications in wavelet transforms and other multi-frequency decompositions. Perfect reconstruction filters have been studied from long time ago but we still found publications about them.
Analysis of V2X Sidelink Positioning in sub-6 GHz
Authors: Yu Ge, Maximilian Stark, Musa Furkan Keskin, Frank Hofmann, Thomas Hansen, Henk Wymeersch
Date: 2022-10-27 00:00:00

Radio positioning is an important part of joint communication and sensing in beyond 5G communication systems. Existing works mainly focus on the mmWave bands and under-utilize the sub-6 GHz bands, even though it is promising for accurate positioning, especially when the multipath is uncomplicated, and meaningful in several important use cases. In this paper, we analyze V2X sidelink positioning and propose a new performance bound that can predict the positioning performance in the presence of severe multipath. Simulation results using ray-tracing data demonstrate the possibility of sidelink positioning, and the efficacy of the new performance bound and its relation with the complexity of the multipath.
Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks
Authors: Zhendong Li, Wen Chen, Ziwei Liu, Hongying Tang, Jianmin Lu
Date: 2022-10-27 00:00:00

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.