pwc-archive/papers-with-abstracts huggingface dataset:
Occasionally Misspecified
Authors: Jean-Jacques Forneron
Date: 2023-12-08 00:00:00

When fitting a particular Economic model on a sample of data, the model may turn out to be heavily misspecified for some observations. This can happen because of unmodelled idiosyncratic events, such as an abrupt but short-lived change in policy. These outliers can significantly alter estimates and inferences. A robust estimation is desirable to limit their influence. For skewed data, this induces another bias which can also invalidate the estimation and inferences. This paper proposes a robust GMM estimator with a simple bias correction that does not degrade robustness significantly. The paper provides finite-sample robustness bounds, and asymptotic uniform equivalence with an oracle that discards all outliers. Consistency and asymptotic normality ensue from that result. An application to the "Price-Puzzle," which finds inflation increases when monetary policy tightens, illustrates the concerns and the method. The proposed estimator finds the intuitive result: tighter monetary policy leads to a decline in inflation.
Power sector impacts of a simultaneous European heat pump rollout
Authors: Alexander Roth
Date: 2023-12-11 00:00:00

The decarbonization of buildings requires the phase-out of fossil fuel heating systems. Heat pumps are considered a crucial technology to supply a substantial part of heating energy for buildings. Yet, their introduction is not without challenges, as heat pumps generate additional electricity demand as well as peak loads. To better understand these challenges, an ambitious simultaneous heat pump rollout in several central European countries with an hourly-resolved capacity expansion model of the power sector is studied. I assess the structure of hours and periods of peak heat demands and their concurrence with hours and periods of peak residual load. In a 2030 scenario, I find that meeting 25% of total heat demand in buildings with heat pumps would be covered best with additional wind power generation capacities. I also identify the important role of small thermal energy storage that could reduce the need for additional firm generation capacity. Due to the co-occurrence of heat demand, interconnection between countries does not substantially reduce the additional generation capacities needed for heat pump deployment. Based on six different weather years, my analysis cautions against relying on results based on a single weather year.
Money, Time, and Grant Design
Authors: Kyle Myers, Wei Yang Tham
Date: 2023-12-11 00:00:00

The design of research grants has been hypothesized to be a useful tool for influencing researchers and their science. We test this by conducting two thought experiments in a nationally representative survey of academic researchers. First, we offer participants a hypothetical grant with randomized attributes and ask how the grant would influence their research strategy. Longer grants increase researchers' willingness to take risks, but only among tenured professors, which suggests that job security and grant duration are complements. Both longer and larger grants reduce researchers' focus on speed, which suggests a significant amount of racing in science is in pursuit of resources. But along these and other strategic dimensions, the effect of grant design is small. Second, we identify researchers' indifference between the two grant design parameters and find they are very unwilling to trade off the amount of funding a grant provides in order to extend the duration of the grant $\unicode{x2014}$ money is much more valuable than time. Heterogeneity in this preference can be explained with a straightforward model of researchers' utility. Overall, our results suggest that the design of research grants is more relevant to selection effects on the composition of researchers pursuing funding, as opposed to having large treatment effects on the strategies of researchers that receive funding.
Human capital in the sustainable economic development of the energy sector
Authors: Evgeny Kuzmin, Maksim Vlasov, Wadim Strielkowski, Marina Faminskaya, Konstantin Kharchenko
Date: 2023-12-11 00:00:00

This study examines the role of human capital investment in driving sustainable socio-economic growth within the energy industry. The fuel and energy sector undeniably forms the backbone of contemporary economies, supplying vital resources that underpin industrial activities, transportation, and broader societal operations. In the context of the global shift toward sustainability, it is crucial to focus not just on technological innovation but also on cultivating human capital within this sector. This is particularly relevant considering the recent shift towards green and renewable energy solutions. In this study, we utilize bibliometric analysis, drawing from a dataset of 1933 documents (represented by research papers, conference proceedings, and book chapters) indexed in the Web of Science (WoS) database. We conduct a network cluster analysis of the textual and bibliometric data using VOSViewer software. The findings stemming from our analysis indicate that investments in human capital are perceived as important in achieving long-term sustainable economic growth in the energy companies both in Russia and worldwide. In addition, it appears that the role of human capital in the energy sector is gaining more popularity both among Russian and international researchers and academics.
A unified repository for pre-processed climate data weighted by gridded economic activity
Authors: Marco Gortan, Lorenzo Testa, Giorgio Fagiolo, Francesco Lamperti
Date: 2023-12-10 00:00:00

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity -- both proxies of economic activity -- before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.
Dealer Strategies in Agent-Based Models
Authors: Wladimir Ostrovsky
Date: 2023-12-10 00:00:00

This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse intricate dynamics. This is achieved by extending the agent-based model for auction markets by \cite{Chiarella.2008} to include liquidity providers. By constantly and passively quoting, the dealers influence their own wealth but also have ramifications on the market as a whole and the other participating agents. Through synthetic market simulations, the optimal behaviour of different dealer strategies and their consequences on market dynamics are examined. The analysis reveals that dealers exhibiting greater risk aversion tend to yield better performance outcomes. The choice of quote sizes by dealers is strategy-dependent: one strategy demonstrates enhanced performance with larger quote sizes, whereas the other strategy show a better results with smaller ones. Increasing quote size shows positive influence on the market in terms of volatility and kurtosis with both dealer strategies. However, the impact stemming from larger risk aversion is mixed. While one of the dealer strategies shows no discernible effect, the other strategy results in mixed outcomes, encompassing both positive and negative effects.
The New Age of Collusion? An Empirical Study into Airbnb's Pricing Dynamics and Market Behavior
Authors: Richeng Piao
Date: 2023-12-09 00:00:00

This study investigates the implications of algorithmic pricing in digital marketplaces, focusing on Airbnb's pricing dynamics. With the advent of Airbnb's new pricing tool, this research explores how digital tools influence hosts' pricing strategies, potentially leading to market dynamics that straddle the line between efficiency and collusion. Utilizing a Regression Discontinuity Design (RDD) and Propensity Score Matching (PSM), the study examines the causal effects of the pricing tool on pricing behavior among hosts with different operational strategies. The findings aim to provide insights into the evolving landscape of digital economies, examining the balance between competitive market practices and the risk of tacit collusion facilitated by algorithmic pricing. This study contributes to the discourse on digital market regulation, offering a nuanced understanding of the implications of AI-driven tools in market dynamics and antitrust analysis.
An empirical analysis of the determinants of network construction for Azul Airlines
Authors: B. F. Oliveira, A. V. M Oliveira
Date: 2023-12-09 00:00:00

This paper describes an econometric model of the Brazilian domestic carrier Azul Airlines' network construction. We employed a discrete-choice framework of airline route entry to examine the effects of the merger of another regional carrier, Trip Airlines, with Azul in 2012, especially on its entry decisions. We contrasted the estimated entry determinants before and after the merger with the benchmarks of the US carriers JetBlue Airways and Southwest Airlines obtained from the literature, and proposed a methodology for comparing different airline entry patterns by utilizing the kappa statistic for interrater agreement. Our empirical results indicate a statistically significant agreement between raters of Azul and JetBlue, but not Southwest, and only for entries on previously existing routes during the pre-merger period. The results suggest that post-merger, Azul has adopted a more idiosyncratic entry pattern, focusing on the regional flights segment to conquer many monopoly positions across the country, and strengthening its profitability without compromising its distinguished expansion pace in the industry.
Simple Proofs of the Variational and Multiple Priors Representations
Authors: Ian Ball
Date: 2023-12-11 00:00:00

This note gives simpler proofs of the variational and multiple priors representations in Maccheroni et al. (2006) and Gilboa and Schmeidler (1989).
Social preferences and expected utility
Authors: Mehmet S. Ismail, Ronald Peeters
Date: 2023-12-11 00:00:00

It is well known that ex ante social preferences and expected utility are not always compatible. In this note, we introduce a novel framework that naturally separates social preferences from selfish preferences to answer the following question: What specific forms of social preferences can be accommodated within the expected utility paradigm? In a departure from existing frameworks, our framework reveals that ex ante social preferences are not inherently in conflict with expected utility in games, provided a decision-maker's aversion to randomization in selfish utility "counterbalances" her social preference for randomization. We also show that when a player's preferences in both the game (against another player) and the associated decision problem (against Nature) conform to expected utility axioms, the permissible range of social preferences becomes notably restricted. Only under this condition do we reaffirm the existing literature's key insight regarding the incompatibility of ex ante inequality aversion with expected utility.
Artificial Intelligence in the Knowledge Economy
Authors: Enrique Ide, Eduard Talamas
Date: 2023-12-09 00:00:00

Artificial Intelligence (AI) can transform the knowledge economy by automating non-codifiable work. To analyze this transformation, we incorporate AI into an economy where humans form hierarchical organizations: Less knowledgeable individuals become "workers" doing routine work, while others become "solvers" handling exceptions. We model AI as a technology that converts computational resources into "AI agents" that operate autonomously (as co-workers and solvers/co-pilots) or non-autonomously (solely as co-pilots). Autonomous AI primarily benefits the most knowledgeable individuals; non-autonomous AI benefits the least knowledgeable. However, output is higher with autonomous AI. These findings reconcile contradictory empirical evidence and reveal tradeoffs when regulating AI autonomy.
Public policy for management of forest pests within an ownership mosaic
Authors: Andrew R. Tilman, Robert G. Haight
Date: 2023-12-08 00:00:00
Tasks: Management

Urban forests provide ecosystem services that are public goods with local (shade) to global (carbon sequestration) benefits and occur on both public and private lands. Thus, incentives for private tree owners to invest in tree care may fall short of those of a public forest manager aiming to optimize ecosystem service benefits for society. The management of a forest pest provides a salient focus area because pests threaten public goods provision and pest management generates feedback that mitigates future risks to forests. We use a game theoretic model to determine optimal pest treatment subsidies for a focal privately owned tree and use an optimization approach to guide targeted public treatment of a representative public tree. We find that optimal public subsidies for private tree treatment depend on assessed tree health and on the prevalence of the pest in the community, considerations absent from many existing programs. Next, by applying our pest treatment policies to a community-scale model of emerald ash borer forest pest dynamics, we predict ash mortality under a range of treatment scenarios over a 50-year time horizon. Our results highlight how designing policies that consider the public goods benefits of private actions can contribute to sustainable land management.
A Geometry-based Stochastic Wireless Channel Model using Channel Images
Authors: Seongjoon Kang
Date: 2023-12-11 00:00:00

Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate wireless systems. This paper introduces a new method to model any GBSM by training a generative neural network using images formed by channel parameters. In this work, we obtain channel parameters from the ray-tracing simulation in a specific area and process them in the form of images to train any generative model. Through a case study, we confirm that the use of channel images completes the training of the generative model within 10 epochs. In addition, we show that the trained generative model based on channel images faithfully represents the distributions of the original data under the assigned conditions. Therefore, we argue that the proposed data-to-image methods will facilitate the modeling of GBSM using any generative neural network under general wireless conditions.
Beyond Line of Sight Defense Communication Systems: Recent Advances and Future Challenges
Authors: Ruhul Amin Khalil, Muhammad Haris, Nasir Saeed
Date: 2023-12-11 00:00:00
Tasks: Management

Beyond Line of Sight (BLOS) communication stands as an indispensable element within defense communication strategies, facilitating information exchange in scenarios where traditional Line of Sight (LOS) methodologies encounter obstruction. This article delves into the forefront of technologies driving BLOS communication, emphasizing advanced systems like phantom networks, nanonetworks, aerial relays, and satellite-based defense communication. Moreover, we present a practical use case of UAV path planning using optimization techniques amidst radar-threat war zones that add concrete relevance, underscoring the tangible applications of BLOS defense communication systems. Additionally, we present several future research directions for BLOS communication in defense systems, such as resilience enhancement, the integration of heterogeneous networks, management of contested spectrums, advancements in multimedia communication, adaptive methodologies, and the burgeoning domain of the Internet of Military Things (IoMT). This exploration of BLOS technologies and their applications lays the groundwork for synergistic collaboration between industry and academia, fostering innovation in defense communication paradigms.
DoA-Aided MMSE Channel Estimation for Wireless Communication Systems
Authors: Franz Weißer, Nurettin Turan, Wolfgang Utschick
Date: 2023-12-11 00:00:00

This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation. We propose a direction-of-arrival (DoA)-aided two-stage channel estimation technique that utilizes the decomposition of wireless communication channels into a line-of-sight (LoS) path and its orthogonal subspace. After estimating the channel along the dominant direction, we utilize a Gaussian mixture model to estimate the conditionally Gaussian distributed random vector, which represents the multipath propagation. The proposed two-stage estimator allows pre-computing the respective estimation filters, tremendously reducing the computational complexity. Numerical simulations with typical channel models depict the superior performance of our proposed two-stage estimation approach compared to state-of-the-art methods.
Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance
Authors: Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu
Date: 2023-12-11 00:00:00
Tasks: Collision Avoidance, Decision Making

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this paper, we consider a path planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV non-cooperative scenarios involve random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep Q-network (D3QN) is proposed to learn the decision making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.
Analysis of Synchrosqueezed Transforms and Application Perspectives
Authors: Rayyan Abdalla
Date: 2023-12-11 00:00:00

High-resolution time-frequency (TF) analysis plays crucial role in characterizing multicomponent signal (MCSs) and estimating oscillatory properties. Linear time-frequency representations (TFRs) such as classical short-time Fourier transform (STFT) and continuous wavelet transform (CWT) incur constrained TF resolution and energy diffusion in both time and frequency direction. The synchrosqueezing transform (SST) represents a powerful sparse reassignment method that allows component reconstruction. This work introduces SST as extension to STFT and CWT and illustrates corresponding advantages of sharpening TFRs and recovery of instantaneous components. The SST effectiveness is assessed in practical situations that involve comparing STFT-based and CWT-based versions of synthetic data and also applying SST to optimize deep learning (DL) prediction model. It is demonstrated how SST achieves promising results in terms of improving TFR readability and increasing accuracy of DL-based prediction models.
Comparative Study of ZF, LMS and RLS Adaptive Equalization for alpha-mu Fading Channels
Authors: Rayyan Abdalla
Date: 2023-12-11 00:00:00

Wireless communication systems generally endure severe fading and interference caused by the time-dispersive channel. Major transmission distortion is produced by channel multipath propagation and overlap of subsequent symbols. To counteract channel response, equalization techniques are employed to operate on channel output and recover transmitted signal at the receiver. This work investigates equalization of channels undergoing alpha-mu fading distribution, which models multipath propagation in millimeter Wave (mmWave) and sub-Terahertz (sub-THz) frequencies bands. Three equalization algorithms, which are non-adaptive Zero-Forcing (ZF), adaptive Least-Mean-Square (LMS) and adaptive Recursive-Least-Square (RLS) are implemented and addressed in terms of Bit Error Rate (BER), Convergence speed and implementation complexity. Monte Carlo simulations are then carried out to compare algorithms and assess performance by varying multiple parameters such as training lengths, channel order, equalization taps and diverse fading conditions.
Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems
Authors: Hongwei Hou, Xuan He, Tianhao Fang, Xinping Yi, Wenjin Wang, Shi Jin
Date: 2023-12-10 00:00:00

This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model the sparsity in the delay domain to explore inter-subcarrier correlations and propose the beam-delay domain sparse representation of spatial-frequency domain channels. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures on the beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial estimation stage is pruned for the refinement stage for the computational complexity reduction. Numerical simulations demonstrate the significant superiority of the proposed algorithms over benchmarks with both near-field and beam-squint effects.