pwc-archive/papers-with-abstracts huggingface dataset:
Assessing Omitted Variable Bias when the Controls are Endogenous
Authors: Paul Diegert, Matthew A. Masten, Alexandre Poirier
Date: 2022-06-06 00:00:00
Tasks: Sensitivity

Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods assess the impact of omitted variables on empirical conclusions by comparing measures of selection on observables with measures of selection on unobservables. The recent literature has discussed various limitations of these existing methods, however. This includes a companion paper of ours which explains issues that arise when the omitted variables are endogenous, meaning that they are correlated with the included controls. In the present paper, we develop a new approach to sensitivity analysis that avoids those limitations, while still allowing researchers to calibrate sensitivity parameters by comparing the magnitude of selection on observables with the magnitude of selection on unobservables as in previous methods. We illustrate our results in an empirical study of the effect of historical American frontier life on modern cultural beliefs. Finally, we implement these methods in the companion Stata module regsensitivity for easy use in practice.
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan
Authors: Yasunobu Wakashiro
Date: 2022-06-05 00:00:00
Tasks: Time Series Analysis

As of May 2022, the coronavirus disease 2019 (COVID-19) still has a severe global impact on people's lives. Previous studies have reported that COVID-19 decreased the electricity demand in early 2020. However, our study found that the electricity demand increased in summer and winter even when the infection was widespread. The fact that the event has continued over two years suggests that it is essential to introduce the method which can estimate the impact of the event for long period considering seasonal fluctuations. We employed the Bayesian structural time-series model to estimate the causal impact of COVID-19 on electricity demand in Japan. The results indicate that behavioral restrictions due to COVID-19 decreased the daily electricity demand (-5.1% in weekdays, -6.1% in holidays) in April and May 2020 as indicated by previous studies. However, even in 2020, the results show that the demand increases in the hot summer and cold winter (the increasing rate is +14% in the period from 1st August to 15th September 2020, and +7.6% from 16th December 2020 to 15th January 2021). This study shows that the significant decrease in electricity demand for the business sector exceeded the increase in demand for the household sector in April and May 2020; however, the increase in demand for the households exceeded the decrease in demand for the business in hot summer and cold winter periods. Our result also implies that it is possible to run out of electricity when people's behavior changes even if they are less active.
Vicious Cycle of Poverty in Haor Region of Bangladesh- Impact of Formal and Informal Credits
Authors: Nazrul Islam
Date: 2022-06-06 00:00:00

This research attempts to explore the key research questions about what are the different microcredit programs in Haor area in Bangladesh? And do microcredit programs have a positive impact on livelihoods of the clients in terms of selected social indicators viz. income, consumption, assets, net worth, education, access to finance, social capacity, food security and handling socks etc. in Haor area in Bangladesh? Utilizing difference-in-difference and factor analysis, we explore the nature and terms of conditions of available formal and informal micro-creditss in Haor region of Bangladesh; and investigate the impact of micro-creditss on the poverty condition of Haor people in Bangladesh. The findings showed that total income of borrowers has been increased over non-borrowers (z=6.75) significantly. Among the components of income, non-agricultural income has been increased significantly on the other hand income from labor sale has been decreased significantly. Total consumption expenditure with its heads of food and non-food consumption of both formal borrowers and informal borrowers have been increased over the period 2016-2019 significantly. Most of the key informants agreed that the findings are very much consistent with prevailing condition of micro-credits in Haor region. However, some of them raised question about the impacts of micro-credits. They argued that there is no straightforward positive impact of micro-credits on poverty condition of the households.
Assessment of Consumers Awareness of Nutrition, Food Safety and Hygiene in the Life Cycle for Sustainable Development of Bangladesh- A Study on Sylhet City Corporation
Authors: Nazrul Islam
Date: 2022-06-06 00:00:00
Tasks: Nutrition

The objective of the study is to explore the knowledge of consumers on nutrition, food safety and hygiene of the sampled respondents in the Sylhet City Corporation in Bangladesh in terms of different demographic characteristics. This study includes the different types of consumers in the life cycle viz. baby, child, adolescent, young and old so that an overall awareness level can be measured in the urban area of Bangladesh. The study is confined to SCC area in which all types of respondents has been included and findings from this study will be used generally for Bangladesh in making policy In conducting the study the population has been divided into six group as, Baby, child, adolescent, parental, unmarried adult young and married adult matured. We find that the average score of awareness of food nutrition and hygiene of unmarried adult is higher than that of married adults. The study suggested it is needed to increase awareness in of the parents for feeding babies. The average awareness of parents to their childs eating behavior between 5 and 9 years is 3.36 out of 5. The awareness is around 67 percent so we should be more careful in this regard. The average awareness adolescent food habit is 1.89 on three points scales which about 63 percent only. Therefore, the consciousness of adolescent has been to increase in taking food. The average feeding styles of parents is 4.24 out of 5 to their children up to 9 years and in percentage it is 84 percent.
Making heads or tails of systemic risk measures
Authors: Aleksy Leeuwenkamp
Date: 2022-06-06 00:00:00

This paper shows that the CoVaR,$\Delta$-CoVaR,CoES,$\Delta$-CoES and MES systemic risk measures can be represented in terms of the univariate risk measure evaluated at a quantile determined by the copula. The result is applied to derive empirically relevant properties of these measures concerning their sensitivity to power-law tails, outliers and their properties under aggregation. Furthermore, a novel empirical estimator for the CoES is proposed. The power-law result is applied to derive a novel empirical estimator for the power-law coefficient which depends on $\Delta\text{-CoVaR}/\Delta\text{-CoES}$. To show empirical performance simulations and an application of the methods to a large dataset of financial institutions are used. This paper finds that the MES is not suitable for measuring extreme risks. Also, the ES-based measures are more sensitive to power-law tails and large losses. This makes these measures more useful for measuring network risk but less so for systemic risk. The robustness analysis also shows that all $\Delta$ measures can underestimate due to the occurrence of intermediate losses. Lastly, it is found that the power-law tail coefficient estimator can be used as an early-warning indicator of systemic risk.
Car-Sharing Subscription Preferences: The Case of Copenhagen, Munich, and Tel Aviv-Yafo
Authors: Mayara Moraes Monteiro, Carlos M. Lima Azevedo, Maria Kamargianni, Yoram Shiftan, Ayelet Gal-Tzur, Sharon Shoshany Tavory, Constantinos Antoniou, Guido Cantelmo
Date: 2022-06-06 00:00:00

Car-sharing services have been providing short-term car access to their users, contributing to sustainable urban mobility and generating positive societal and often environmental impacts. As car-sharing business models vary, it is important to understand what features drive the attraction and retention of its members in different contexts. For that, it is essential to examine individuals preferences for subscriptions to different business models and what they perceive as most relevant, as well as understand what could be attractive incentives. This study aims precisely to examine individuals preferences for the subscription of different car-sharing services in different cities. We designed a stated preference experiment and collected data from three different urban car-sharing settings, namely Copenhagen, Munich, and Tel Aviv-Yafo. Then a mixed logit model was estimated to uncover car-sharing plan subscription and incentives preferences. The results improve our understanding of how both the features of the car-sharing business model and the provision of incentives can maintain and attract members to the system. The achieved insights pave the road for the actual design of car-sharing business models and incentives that can be offered by existing and future car-sharing companies in the studied or similar cities.
Stability of shares in the Proof of Stake Protocol -- Concentration and Phase Transitions
Authors: Wenpin Tang
Date: 2022-06-05 00:00:00
Tasks: POS

This paper is concerned with the stability of shares in a cryptocurrency where the new coins are issued according to the Proof of Stake protocol. We identify large, medium and small investors under various rewarding schemes, and show that the limiting behaviors of these investors are different -- for large investors their shares are stable, while for medium to small investors their shares may be volatile or even shrink to zero. For instance, with a geometric reward there is chaotic centralization, where all the shares will eventually concentrate on one investor in a random manner. This leads to the phase transition phenomenon, and the thresholds for stability are characterized. In response to the increasing activities in blockchain networks, we also propose and analyze a dynamical population model for the PoS protocol, which allows the number of investors to grow over the time. Numerical experiments are provided to corroborate our theory.
The Effect of External Debt on Greenhouse Gas Emissions
Authors: Jorge Carrera, Pablo de la Vega
Date: 2022-06-03 00:00:00

We estimate the causal effect of external debt on greenhouse gas emissions in a panel of 78 emerging market and developing economies over the 1990-2015 period. Unlike previous literature, we use external instruments to address the potential endogeneity in the relationship between external debt and greenhouse gas emissions. Specifically, we use international liquidity shocks as instrumental variables for external debt. We find that dealing with the potential endogeneity problem brings about a positive and statistically significant effect of external debt on greenhouse gas emissions: a 1 percentage point (pp.) rise in external debt causes, on average, a 0.5% increase in greenhouse gas emissions. One possible mechanism of action could be that, as external debt increases, governments are less able to enforce environmental regulations because their main priority is to increase the tax base to pay increasing debt services or because they are captured by the private sector who owns that debt and prevented from tightening such regulations.
A Robust Deep Learning Enabled Semantic Communication System for Text
Authors: Xiang Peng, Zhijin Qin, Danlan Huang, Xiaoming Tao, Jianhua Lu, Guangyi Liu, Chengkang Pan
Date: 2022-06-06 00:00:00
Tasks: Deep Learning, Semantic Communication

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.
Urban Road Safety Prediction: A Satellite Navigation Perspective
Authors: Halim Lee, Jiwon Seo, Zaher M. Kassas
Date: 2022-06-06 00:00:00
Tasks: Prediction

Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure safe driving of automated vehicles, the vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, etc.). Such information can be derived from the roads' physical properties, vehicle's capabilities, and weather conditions. From a GNSS-based navigation perspective, the reliability of GNSS signals in different locales, which is heavily dependent on the road layout within the surrounding environment, is crucial to ensure safe automated driving. An urban road environment surrounded by tall objects can significantly degrade the accuracy and availability of GNSS signals. This article proposes an approach to predict the reliability of GNSS-based navigation to ensure safe urban navigation. Satellite navigation reliability at a given location and time on a road is determined based on the probabilistic position error bound of the vehicle-mounted GNSS receiver. A metric for GNSS reliability for ground vehicles is suggested, and a method to predict the conservative probabilistic error bound of the GNSS navigation solution is proposed. A satellite navigation reliability map is generated for various navigation applications. As a case study, the reliability map is used in the proposed optimization problem formulation for automated ground vehicle safety-constrained path planning.
Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios
Authors: Cheng-Xiang Wang, Zhen Lv, Xiqi Gao, Xiaohu You, Yang Hao, Harald Haas
Date: 2022-06-06 00:00:00
Tasks: All

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability.
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Authors: Xiwen Wang, Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Date: 2022-06-06 00:00:00
Tasks: Portfolio Optimization

Since Markowitz's mean-variance framework, optimizing a portfolio that maximizes the profit and minimizes the risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the portfolio's return, a.k.a. the mean and variance, which are sufficient to characterize a Gaussian distribution. However, it is broadly believed that the first two moments are not enough to capture the characteristics of the returns' behavior, which have been recognized to be asymmetric and heavy-tailed. Although there is ample evidence that portfolio designs involving the third and fourth moments, i.e., skewness and kurtosis, will outperform the conventional mean-variance framework, they are non-trivial. Specifically, in the classical framework, the memory and computational cost of computing the skewness and kurtosis grow sharply with the number of assets. To alleviate the difficulty in high-dimensional problems, we consider an alternative expression for high-order moments based on parametric representations via a generalized hyperbolic skew-t distribution. Then, we reformulate the high-order portfolio optimization problem as a fixed-point problem and propose a robust fixed-point acceleration algorithm that solves the problem in an efficient and scalable manner. Empirical experiments also demonstrate that our proposed high-order portfolio optimization framework is of low complexity and significantly outperforms the state-of-the-art methods by 2 to 4 orders of magnitude.
Underdetermined 2D-DOD and 2D-DOA Estimation for Bistatic Coprime EMVS-MIMO Radar: From the Difference Coarray Perspective
Authors: Qianpeng Xie, Yihang Du, He Wang, Xiaoyi Pan, Feng Zhao
Date: 2022-06-06 00:00:00

In this paper, the underdetermined 2D-DOD and 2D-DOA estimation for bistatic coprime EMVS-MIMO radar is considered. Firstly, a 5-D tensor model was constructed by using the multi-dimensional space-time characteristics of the received data. Then, an 8-D tensor has been obtained by using the auto-correlation calculation. To obtain the difference coarrays of transmit and receive EMVS, the de-coupling process between the spatial response of EMVS and the steering vector is inevitable. Thus, a new 6-D tensor can be constructed via the tensor permutation and the generalized tensorization of the canonical polyadic decomposition. {According} to the theory of the Tensor-Matrix Product operation, the duplicated elements in the difference coarrays can be removed by the utilization of two designed selection matrices. Due to the centrosymmetric geometry of the difference coarrays, two DFT beamspace matrices were subsequently designed to convert the complex steering matrices into the real-valued ones, whose advantage is to improve the estimation accuracy of the 2D-DODs and 2D-DOAs. Afterwards, a third-order tensor with the third-way fixed at 36 was constructed and the Parallel Factor algorithm was deployed, which can yield the closed-form automatically paired 2D-DOD and 2D-DOA estimation. The simulation results show that the proposed algorithm can exhibit superior estimation performance for the underdetermined 2D-DOD and 2D-DOA estimation.
Reconfigurable intelligent surfaces: Channel characterization and modeling
Authors: Jie Huang, Cheng-Xiang Wang, Yingzhuo Sun, Rui Feng, Jialing Huang, Bolun Guo, Zhimeng Zhong, Tie Jun Cui
Date: 2022-06-06 00:00:00

Reconfigurable intelligent surfaces (RISs) are two dimensional (2D) metasurfaces which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this paper, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. At first, the concept, principle, and potential applications of RIS are given. An overview of RIS based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS based channel characteristics are studied, including reflection and transmission, Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field and near field, etc. RIS based channel modeling works are investigated, including largescale path loss models and small-scale multipath fading models. At last, future research directions related to RIS-assisted channels are also discussed.
MICAL: Mutual Information-Based CNN-Aided Learned Factor
Authors: Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad
Date: 2022-06-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNNAided Learned factor graphs (MICAL) for detection of eclectic seizures from EEG signals. Our proposed method contains three main components: a neural mutual information (MI) estimator, 1D convolutional neural network (CNN), and factor graph inference. Since during seizure the electrical activity in one or more regions in the brain becomes correlated, we use neural MI estimators to measure inter-channel statistical dependence. We also design a 1D CNN to extract additional features from raw EEG signals. Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph. The resulting factor graphs allows structured inference which exploits the temporal correlation for further improving the detection performance. On public CHB-MIT database, We conduct three evaluation approaches using the public CHB-MIT database, including 6-fold leave-four-patients-out cross-validation, all patient training; and per patient training. Our evaluations systematically demonstrate the impact of each element in MICAL through a complete ablation study and measuring six performance metrics. It is shown that the proposed method obtains state-of-the-art performance specifically in 6-fold leave-four-patients-out cross-validation and all patient training, demonstrating a superior generalizability.
Differentiable Point Scattering Models for Efficient Radar Target Characterization
Authors: Zachary Chance, Adam Kern, Arianna Burch, Justin Goodwin
Date: 2022-06-05 00:00:00

Target characterization is an important step in many defense missions, often relying on fitting a known target model to observed data. Optimization of model parameters can be computationally expensive depending on the model complexity, thus having models that both describe the data well and that can be efficiently optimized is critical. This work introduces a class of radar models that can be used to represent the radar scattering response of a target at high frequencies while also enabling the use of gradient-based optimization.