pwc-archive/papers-with-abstracts huggingface dataset:
Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period
Authors: Clément de Chaisemartin, Xavier D'Haultfœuille, Félix Pasquier, Doulo Sow, Gonzalo Vazquez-Bare
Date: 2022-01-18 00:00:00
Tasks: valid

We propose difference-in-differences (DID) estimators in designs where the treatment is continuously distributed in every period, as is often the case when one studies the effects of taxes, tariffs, or prices. We assume that between consecutive periods, the treatment of some units, the switchers, changes, while the treatment of other units, the stayers, remains constant. We show that under a parallel-trends assumption, weighted averages of the slopes of switchers' potential outcomes are nonparametrically identified by difference-in-differences estimands comparing the outcome evolutions of switchers and stayers with the same baseline treatment. Controlling for the baseline treatment ensures that our estimands remain valid if the treatment's effect changes over time. We highlight two possible ways of weighting switcher's slopes, and discuss their respective advantages. For each weighted average of slopes, we propose a doubly-robust, nonparametric, $\sqrt{n}$-consistent, and asymptotically normal estimator. We generalize our results to the instrumental-variable case. Finally, we apply our method to estimate the price-elasticity of gasoline consumption.
Homophily in preferences or meetings? Identifying and estimating an iterative network formation model
Authors: Luis Alvarez, Cristine Pinto, Vladimir Ponczek
Date: 2022-01-18 00:00:00
Tasks: counterfactual

Is homophily in social and economic networks driven by a taste for homogeneity (preferences) or by a higher probability of meeting individuals with similar attributes (opportunity)? This paper studies identification and estimation of an iterative network game that distinguishes between these two mechanisms. Our approach enables us to assess the counterfactual effects of changing the meeting protocol between agents. As an application, we study the role of preferences and meetings in shaping classroom friendship networks in Brazil. In a network structure in which homophily due to preferences is stronger than homophily due to meeting opportunities, tracking students may improve welfare. Still, the relative benefit of this policy diminishes over the school year.
An Entropy-Based Approach for Nonparametrically Testing Simple Probability Distribution Hypotheses
Authors: Ron Mittelhammer, George Judge, Miguel Henry
Date: 2022-01-17 00:00:00

In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the population probability distribution being tested and is attractive in that, regardless of the null hypothesis being tested, it provides a unified framework for conducting such tests. The testing procedure is also computationally tractable and relatively straightforward to implement. In contrast to some alternative test statistics, the proposed entropy test is free from user-specified kernel and bandwidth choices, idiosyncratic and complex regularity conditions, and/or choices of evaluation grids. Several simulation exercises were performed to document the empirical performance of our proposed test, including a regression example that is illustrative of how, in some contexts, the approach can be applied to composite hypothesis-testing situations via data transformations. Overall, the testing procedure exhibits notable promise, exhibiting appreciable increasing power as sample size increases for a number of alternative distributions when contrasted with hypothesized null distributions. Possible general extensions of the approach to composite hypothesis-testing contexts, and directions for future work are also discussed.
Nonparametric Identification of Random Coefficients in Endogenous and Heterogeneous Aggregate Demand Models
Authors: Fabian Dunker, Stefan Hoderlein, Hiroaki Kaido
Date: 2022-01-16 00:00:00

This paper studies nonparametric identification in market level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as welfare measures, is identified. A key finding is that two leading models, the BLP-model (Berry, Levinsohn, and Pakes, 1995) and the pure characteristics model (Berry and Pakes, 2007), require considerably different conditions on the support of the product characteristics.
Measuring Changes in Disparity Gaps: An Application to Health Insurance
Authors: Paul Goldsmith-Pinkham, Karen Jiang, Zirui Song, Jacob Wallace
Date: 2022-01-14 00:00:00

We propose a method for reporting how program evaluations reduce gaps between groups, such as the gender or Black-white gap. We first show that the reduction in disparities between groups can be written as the difference in conditional average treatment effects (CATE) for each group. Then, using a Kitagawa-Oaxaca-Blinder-style decomposition, we highlight how these CATE can be decomposed into unexplained differences in CATE in other observables versus differences in composition across other observables (e.g. the "endowment"). Finally, we apply this approach to study the impact of Medicare on American's access to health insurance.
Volatility in the Relative Standard Deviation of Target Fulfilment as Key Performance Indicator (KPI)
Authors: Andreas Bauer, Jasna Omeragic
Date: 2022-01-17 00:00:00

In this study, we identify the relative standard deviation volatility (RSD volatility) in the individual target time fulfilment of the complete set of comparables (e.g., all individuals in the same organisational structure) as a possible key performance indicator (KPI) for predicting employee job performance. KPIs are a well-established, measurable benchmark of an organisation's critical success metrics; thus, in this paper, we attempt to identify employees experiencing a transition in their RSD towards a higher per cent deviation, indicating emerging inadequate work conditions. We believe RSD volatility can be utilised as an additional assessment factor, particularly in profiling.
The Resilience of FDI to Natural Disasters through Industrial Linkages
Authors: Hayato Kato, Toshihiro Okubo
Date: 2022-01-17 00:00:00

When do multinationals show resilience during natural disasters? To answer this, we develop a simple model in which foreign multinationals and local firms in the host country are interacted through input-output linkages. When natural disasters seriously hit local firms and thus increase the cost of sourcing local intermediate inputs, most multinationals may leave the host country. However, they are likely to stay if they are tightly linked with local suppliers and face low trade costs of importing foreign intermediates. We further provide a number of extensions of the basic model to incorporate, for example, multinationals with heterogeneous productivity and disaster reconstruction.
Intertemporal Consumption and Debt Aversion: A Replication and Extension
Authors: Steffen Ahrens, Ciril Bosch-Rosa, Thomas Meissner
Date: 2022-01-16 00:00:00

We replicate Meissner (2016), where debt aversion was reported for the first time in an intertemporal consumption and saving problem. While Meissner (2016) uses a German sample, our participants are US undergraduate students. All of the original study's main findings replicate with similar effect sizes. Additionally, we extend the original analysis by introducing a new individual index of debt aversion, which we use to compare debt aversion across countries. Interestingly, we find no significant differences in debt aversion between the original German and the new US sample. We then test whether debt aversion correlates with individual characteristics such as gender, cognitive reflection ability, and risk aversion. Overall, this paper confirms the importance of debt aversion in intertemporal consumption and saving problems and validates the approach of Meissner (2016).
How easy is it for investment managers to deploy their talent in green and brown stocks?
Authors: David Ardia, Keven Bluteau, Thien Duy Tran
Date: 2022-01-14 00:00:00

We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt (2018). Focusing on S&P 500 index firms over 2014-2020 and defining peer groups in terms of firms' greenhouse gas emission levels, we find that, on average, about 20% of the stocks differentiate themselves from their peers in terms of future performance. We see a much higher time-variation in this opportunity set within brown stocks. Furthermore, the performance heterogeneity has decreased over time, especially for green stocks, implying that it is now more difficult for investment managers to deploy their skills when choosing among low-GHG intensity stocks.
Examining the Relations between Household Saving Rate of Rural Areas and Migration
Authors: Fuhao Lou
Date: 2022-01-13 00:00:00

China has been developing very fast since the beginning of the 21st century. The net income of households has been increased a lot as well. Nonetheless, migration from rural areas to urban sectors tends to keep a high saving rate instead of consumption. This essay tries to use the conventional Ordinary Least Square regression, along with the method of Instrument Variable to test the problem of endogeneity, to discover the relationship between the saving rates of rural households and labor migration, controlling for other characteristic variables including having insurance, marital status, education, having children, health conditions. The assumption is that migration contributes positively to the dependent variable, meaning that migration could increase the household save rates. However, the conclusion is that it is negatively with the household save rates. All the other variables regarding education, health conditions, marital status, insurance, and number of children are negatively related with the household saving rates.
Pandemic Recession and Helicopter Money: Venice, 1629--1631
Authors: Charles Goodhart, Donato Masciandaro, Stefano Ugolini
Date: 2022-01-12 00:00:00

We analyse the money-financed fiscal stimulus implemented in Venice during the famine and plague of 1629--31, which was equivalent to a 'net-worth helicopter money' strategy -- a monetary expansion generating losses to the issuer. We argue that the strategy aimed at reconciling the need to subsidize inhabitants suffering from containment policies with the desire to prevent an increase in long-term government debt, but it generated much monetary instability and had to be quickly reversed. This episode highlights the redistributive implications of the design of macroeconomic policies and the role of political economy factors in determining such designs.
Agenda-manipulation in ranking
Authors: Gregorio Curello, Ludvig Sinander
Date: 2020-01-30 00:00:00

We study the susceptibility of committee governance (e.g. by boards of directors), modelled as the collective determination of a ranking of a set of alternatives, to manipulation of the order in which pairs of alternatives are voted on -- agenda-manipulation. We exhibit an agenda strategy called insertion sort that allows a self-interested committee chair with no knowledge of how votes will be cast to do as well as if she had complete knowledge. Strategies with this 'regret-freeness' property are characterised by their efficiency, and by their avoidance of two intuitive errors. What distinguishes regret-free strategies from each other is how they prioritise among alternatives; insertion sort prioritises lexicographically.
Tit for Tattling: Cooperation, communication, and how each could stabilize the other
Authors: Victor Vikram Odouard, Michael Holton Price
Date: 2022-01-18 00:00:00
Tasks: Diversity

Indirect reciprocity is a mechanism by which individuals cooperate with those who have cooperated with others. This creates a regime in which repeated interactions are not necessary to incent cooperation (as would be required for direct reciprocity). However, indirect reciprocity creates a new problem: how do agents know who has cooperated with others? To know this, agents would need to access some form of reputation information. Perhaps there is a communication system to disseminate reputation information, but how does it remain truthful and informative? Most papers assume the existence of a truthful, forthcoming, and informative communication system; in this paper, we seek to explain how such a communication system could remain evolutionarily stable in the absence of exogenous pressures. Specifically, we present three conditions that together maintain both the truthfulness of the communication system and the prevalence of cooperation: individuals (1) use a norm that rewards the behaviors that it prescribes (an aligned norm), (2) can signal not only about the actions of other agents, but also about their truthfulness (by acting as third party observers to an interaction), and (3) make occasional mistakes, demonstrating how error can create stability by introducing diversity.
Referral Hiring and Social Network Structure
Authors: Yoshitaka Ogisu
Date: 2022-01-16 00:00:00

It is well known that differences in the average number of friends among social groups can cause inequality in the average wage and/or unemployment rate. However, the impact of social network structure on inequality is not evident. In this paper, we show that not only the average number of friends but also the heterogeneity of degree distribution can affect inter-group inequality. A worker group with a scale-free network tends to be disadvantaged in the labor market compared to a group with an Erd\H{o}s-R\'{e}nyi network structure. This feature becomes strengthened as the skewness of the degree distribution increases in scale-free networks. We show that the government's policy of discouraging referral hiring worsens social welfare and can exacerbate inequality.
Sampling color and geometry point clouds from ShapeNet dataset
Authors: Davi Lazzarotto, Touradj Ebrahimi
Date: 2022-01-18 00:00:00

The popularisation of acquisition devices capable of capturing volumetric information such as LiDAR scans and depth cameras has lead to an increased interest in point clouds as an imaging modality. Due to the high amount of data needed for their representation, efficient compression solutions are needed to enable practical applications. Among the many techniques that have been proposed in the last years, learning-based methods are receiving large attention due to their high performance and potential for improvement. Such algorithms depend on large and diverse training sets to achieve good compression performance. ShapeNet is a large-scale dataset composed of CAD models with texture and constitute and effective option for training such compression methods. This dataset is entirely composed of meshes, which must go through a sampling process in order to obtain point clouds with geometry and texture information. Although many existing software libraries are able to sample geometry from meshes through simple functions, obtaining an output point cloud with geometry and color of the external faces of the mesh models is not a straightforward process for the ShapeNet dataset. The main difficulty associated with this dataset is that its models are often defined with duplicated faces sharing the same vertices, but with different color values. This document describes a script for sampling the meshes from ShapeNet that circumvent this issue by excluding the internal faces of the mesh models prior to the sampling. The script can be accessed from the following link: https://github.com/mmspg/mesh-sampling.
Efficient demodulation scheme for multilevel modulation based optical camera communication
Authors: ZiHao Zhou, Maolin Li, Weipeng Guan
Date: 2022-01-15 00:00:00

We proposed and experimentally demonstrated a new hybrid code structure based on the overlapping of two light sources to produce the effect of multi-voltage amplitudes. And we also proposed an efficient polarity reversal threshold (RRT) algorithm for multilevel based OCC system. Then taking the issue of SPO into account, a novel adaptive sampling method (ASM) is proposed, which can effectively alleviate the problem of SPO and further enhance the performance of multilevel OCC. It is demonstrated that a data rate of 8.4 Kbit/s can be achieved by applying the proposed two algorithms.
Co-Pulsing FDA Radar
Authors: Wanghan Lv, Kumar Vijay Mishra, Shichao Chen
Date: 2022-01-18 00:00:00

Target localization based on frequency diverse array (FDA) radar has lately garnered significant research interest. A linear frequency offset (FO) across FDA antennas yields a range-angle dependent beampattern that allows for joint estimation of range and direction-of-arrival (DoA). Prior works on FDA largely focus on the one-dimensional linear array to estimate only azimuth angle and range while ignoring the elevation and Doppler velocity. However, in many applications, the latter two parameters are also essential for target localization. Further, there is also an interest in radar systems that employ fewer measurements in temporal, Doppler, or spatial signal domains. We address these multiple challenges by proposing a co-prime L-shaped FDA, wherein co-prime FOs are applied across the elements of L-shaped co-prime array and each element transmits at a non-uniform co-prime pulse repetition interval (C$^3$ or C-Cube). This co-pulsing FDA yields significantly large degrees-of-freedom (DoFs) for target localization in the range-azimuth-elevation-Doppler domain while also reducing the time-on-target and transmit spectral usage. By exploiting these DoFs, we develop C-Cube auto-pairing (CCing) algorithm, in which all the parameters are ipso facto paired during a joint estimation. We show that C-Cube FDA requires at least $2\sqrt{Q+1}-1$ antenna elements and $2\sqrt{Q+1}-1$ pulses to guarantee perfect recovery of $Q$ targets as against $Q+1$ elements and $Q+1$ pulses required by both L-shaped uniform linear array and L-shaped linear FO FDA with uniform pulsing. We derive Cram\'er-Rao bounds (CRBs) for joint angle-range-Doppler estimation errors for C-Cube FDA and provide the conditions under which the CRBs exist. Numerical experiments with our CCing algorithm show great performance improvements in parameter recovery.
Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features
Authors: Luca Varotto, Marco Fabris, Giulia Michieletto, Angelo Cenedese
Date: 2022-01-18 00:00:00
Tasks: model

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI
Authors: Zhen Gao, Minghui Wu, Chun Hu, Feifei Gao, Guanghui Wen, Dezhi Zheng, Jun Zhang
Date: 2022-01-18 00:00:00
Tasks: Quantization, Transfer Learning

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.
AI Augmented Digital Metal Component
Authors: Eunhyeok Seo, Hyokyung Sung, Hayeol Kim, Taekyeong Kim, Sangeun Park, Min Sik Lee, Seung Ki Moon, Jung Gi Kim, Hayoung Chung, Seong-Kyum Choi, Ji-hun Yu, Kyung Tae Kim, Seong Jin Park, Namhun Kim, Im Doo Jung
Date: 2022-01-18 00:00:00

The aim of this work is to propose a new paradigm that imparts intelligence to metal parts with the fusion of metal additive manufacturing and artificial intelligence (AI). Our digital metal part classifies the status with real time data processing with convolutional neural network (CNN). The training data for the CNN is collected from a strain gauge embedded in metal parts by laser powder bed fusion process. We implement this approach using additive manufacturing, demonstrate a self-cognitive metal part recognizing partial screw loosening, malfunctioning, and external impacting object. The results indicate that metal part can recognize subtle change of multiple fixation state under repetitive compression with 89.1% accuracy with test sets. The proposed strategy showed promising potential in contributing to the hyper-connectivity for next generation of digital metal based mechanical systems