pwc-archive/papers-with-abstracts huggingface dataset:
Accurate Calculation of Switching Events in Electromagnetic Transient Simulation Considering State Variable Discontinuities
Authors: Sheng Lei
Date: 2024-11-07 00:00:00

Accurate calculation of switching events is important for electromagnetic transient simulation to obtain reliable results. The common presumption of continuous differential state variables could prevent the accurate calculation, thus leading to unreliable results. This paper explores accurately calculating switching events without presuming continuous differential state variables. Possibility of the calculation is revealed by the proposal of related methods. Feasibility and accuracy of the proposed methods are demonstrated and analyzed via numerical case studies.
A Distributionally Robust Control Strategy for Frequency Safety based on Koopman Operator Described System Model
Authors: Qianni Cao, Chen Shen
Date: 2024-11-07 00:00:00
Tasks: Prediction

As the proportion of renewable energy and power electronics in the power system increases, modeling frequency dynamics under power deficits becomes more challenging. Although data-driven methods help mitigate these challenges, they are exposed to data noise and training errors, leading to uncertain prediction errors. To address uncertain and limited statistical information of prediction errors, we introduce a distributionally robust data-enabled emergency frequency control (DREFC) framework. It aims to ensure a high probability of frequency safety and allows for adjustable control conservativeness for decision makers. Specifically, DREFC solves a min-max optimization problem to find the optimal control that is robust to distribution of prediction errors within a Wasserstein-distance-based ambiguity set. With an analytical approximation for VaR constraints, we achieve a computationally efficient reformulations. Simulations demonstrate that DREFC ensures frequency safety, low control costs and low computation time.
Effective Capacity of a Battery Energy Storage System Captive to a Wind Farm
Authors: Vinay A. Vaishampayan, Thilaharani Antony, Amirthagunaraj Yogarathnam
Date: 2024-11-06 00:00:00

Wind energy's role in the global electric grid is set to expand significantly. New York State alone anticipates offshore wind farms (WFs) contributing 9GW by 2035. Integration of energy storage emerges as crucial for this advancement. In this study, we focus on a WF paired with a captive battery energy storage system (BESS). We aim to ascertain the capacity credit for a BESS with specified energy and power ratings. Unlike prior methods rooted in reliability theory, we define a power alignment function, which leads to a straightforward definition of capacity and incremental capacity for the BESS. We develop a solution method based on a linear programming formulation. Our analysis utilizes wind data, collected by NYSERDA off Long Island's coast and load demand data from NYISO. Additionally, we present theoretical insights into BESS sizing and a key time-series property influencing BESS capacity, aiding in simulating wind and demand for estimating BESS energy requirements.
Observability and Generalized Sensor Placement for Nonlinear Quality Models in Drinking Water Networks
Authors: Mohamad H. Kazma, Salma M. Elsherif, Ahmad F. Taha
Date: 2024-11-06 00:00:00

This paper studies the problem of optimal placement of water quality (WQ) sensors in water distribution networks (WDNs), with a focus on chlorine transport, decay, and reaction models. Such models are traditionally used as suitable proxies for WQ. The literature on this topic is inveterate, but has a key limitation: it utilizes simplified single-species decay and reaction models that do not capture WQ transients for nonlinear, multi-species interactions. This results in sensor placements (SP) that do not account for nonlinear WQ dynamics. Furthermore, as WQ simulations are parameterized by hydraulic profiles and demand patterns, the placement of sensors are often hydraulics-dependent. This study produces a greedy algorithm that addresses the two aforementioned limitations. The algorithm is grounded in nonlinear dynamic systems and observability theory, and yields SPs that are submodular and robust to hydraulic changes. Case studies on benchmark water networks are provided. The key findings provide practical recommendations for WDN operators.
Strong anomalous diffusion for free-ranging birds
Authors: Ohad Vilk, Motti Charter, Sivan Toledo, Eli Barkai, Ran Nathan
Date: 2024-11-07 00:00:00

Natural and anomalous diffusion are widely observed and used to explore causes and consequences of movement across organisms, resulting in extensive use of the mean and mean-squared displacement (MSD). Using high-resolution data from over 70 million localizations of young and adult free-ranging Barn Owls (\textit{Tyto alba}), we demonstrate the necessity of a broad spectrum of displacement moments to characterize bird movement across scales. The mean and MSD -- interchangeable with moments $q=1$ and 2 -- are insufficient special cases. We reveal empirical strong anomalous diffusion as a nonlinear growth of displacement moments according to $\left<|x(t)|^q\right> \sim t^{\lambda(q)}$. The moment spectrum function $\lambda(q)$ displays piecewise linearity with a critical moment marking the crossover point between two scaling regimes, linked to a combination of age-specific behavioral modes. A critical timescale of five minutes marks an unexpected transition from a convex $\lambda_s(q)$ to a concave $\lambda_\ell(q)$, related to environmental and behavioral constraints. Using two stochastic models of varying ecological complexity, we demonstrate that strong anomalous diffusion may be widespread in animal movement, underscoring the importance of expanding analysis beyond the average and MSD.
A Coverage-Guided Testing Framework for Quantum Neural Networks
Authors: Minqi Shao, Jianjun Zhao
Date: 2024-11-03 00:00:00
Tasks: Diversity

Quantum Neural Networks (QNNs) integrate quantum computing and deep neural networks, leveraging quantum properties like superposition and entanglement to enhance machine learning algorithms. These characteristics enable QNNs to outperform classical neural networks in tasks such as quantum chemistry simulations, optimization problems, and quantum-enhanced machine learning. Despite their early success, their reliability and safety issues have posed threats to their applicability. However, due to the inherently non-classical nature of quantum mechanics, verifying QNNs poses significant challenges. To address this, we propose QCov, a set of test coverage criteria specifically designed to systematically evaluate QNN state exploration during testing, with an emphasis on superposition. These criteria help evaluate test diversity and detect underlying defects within test suites. Extensive experiments on benchmark datasets and QNN models validate QCov's effectiveness in reflecting test quality, guiding fuzz testing efficiently, and thereby improving QNN robustness. We also evaluate sampling costs of QCov under realistic quantum scenarios to justify its practical feasibility. Finally, the effects of unrepresentative training data distribution and parameter choice are further explored.
The role of expansion strategies and operational attributes on hotel performance: a compositional approach
Authors: Carles Mulet-Forteza, Berta Ferrer-Rosell, Onofre Martorell Cunill, Salvador Linares-Mustarós
Date: 2024-11-07 00:00:00
Tasks: Management

This study aims to explore the impact of expansion strategies and specific attributes of hotel establishments on the performance of international hotel chains, focusing on four key performance indicators: RevPAR, efficiency, occupancy, and asset turnover. Data were collected from 255 hotels across various international hotel chains, providing a comprehensive assessment of how different expansion strategies and hotel attributes influence performance. The research employs compositional data analysis (CoDA) to address the methodological limitations of traditional financial ratios in statistical analysis. The findings indicate that ownership-based expansion strategies result in higher operational performance, as measured by revenue per available room, but yield lower economic performance due to the high capital investment required. Non-ownership strategies, such as management contracts and franchising, show superior economic efficiency, offering more flexibility and reduced financial risk. This study contributes to the hospitality management literature by applying CoDA, a novel methodological approach in this field, to examine the performance of different hotel expansion strategies with a sound and more appropriate method. The insights provided can guide hotel managers and investors in making informed decisions to optimize both operational and economic performance.
Partial Identification of Distributional Treatment Effects in Panel Data using Copula Equality Assumptions
Authors: Heshani Madigasekara, D. S. Poskitt, Lina Zhang, Xueyan Zhao
Date: 2024-11-07 00:00:00

This paper aims to partially identify the distributional treatment effects (DTEs) that depend on the unknown joint distribution of treated and untreated potential outcomes. We construct the DTE bounds using panel data and allow individuals to switch between the treated and untreated states more than once over time. Individuals are grouped based on their past treatment history, and DTEs are allowed to be heterogeneous across different groups. We provide two alternative group-wise copula equality assumptions to bound the unknown joint and the DTEs, both of which leverage information from the past observations. Testability of these two assumptions are also discussed, and test results are presented. We apply this method to study the treatment effect heterogeneity of exercising on the adults' body weight. These results demonstrate that our method improves the identification power of the DTE bounds compared to the existing methods.
Identification of Long-Term Treatment Effects via Temporal Links, Observational, and Experimental Data
Authors: Filip Obradović
Date: 2024-11-07 00:00:00

Recent literature proposes combining short-term experimental and long-term observational data to provide credible alternatives to conventional observational studies for identification of long-term average treatment effects (LTEs). I show that experimental data have an auxiliary role in this context. They bring no identifying power without additional modeling assumptions. When modeling assumptions are imposed, experimental data serve to amplify their identifying power. If the assumptions fail, adding experimental data may only yield results that are farther from the truth. Motivated by this, I introduce two assumptions on treatment response that may be defensible based on economic theory or intuition. To utilize them, I develop a novel two-step identification approach that centers on bounding temporal link functions -- the relationship between short-term and mean long-term potential outcomes. The approach provides sharp bounds on LTEs for a general class of assumptions, and allows for imperfect experimental compliance -- extending existing results.
Lee Bounds with a Continuous Treatment in Sample Selection
Authors: Ying-Ying Lee, Chu-An Liu
Date: 2024-11-06 00:00:00
Tasks: Causal Inference, Selection bias

We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcomes and their observability (e.g., employment or survey responses). We generalized the widely used Lee (2009)'s bounds for binary treatment effects. Our key innovation is a sufficient treatment values assumption that imposes weak restrictions on selection heterogeneity and is implicit in separable threshold-crossing models, including monotone effects on selection. Our double debiased machine learning estimator enables nonparametric and high-dimensional methods, using covariates to tighten the bounds and capture heterogeneity. Applications to Job Corps and CCC program evaluations reinforce prior findings under weaker assumptions.
Bounded Rationality in Central Bank Communication
Authors: Wonseong Kim, Choong Lyol Lee
Date: 2024-11-06 00:00:00
Tasks: Sentiment Analysis

This study explores the influence of FOMC sentiment on market expectations, focusing on cognitive differences between experts and non-experts. Using sentiment analysis of FOMC minutes, we integrate these insights into a bounded rationality model to examine the impact on inflation expectations. Results show that experts form more conservative expectations, anticipating FOMC stabilization actions, while non-experts react more directly to inflation concerns. A lead-lag analysis indicates that institutions adjust faster, though the gap with individual investors narrows in the short term. These findings highlight the need for tailored communication strategies to better align public expectations with policy goals.
An Adversarial Approach to Identification
Authors: Irene Botosaru, Isaac Loh, Chris Muris
Date: 2024-11-06 00:00:00
Tasks: counterfactual, valid

We introduce a new framework for characterizing identified sets of structural and counterfactual parameters in econometric models. By reformulating the identification problem as a set membership question, we leverage the separating hyperplane theorem in the space of observed probability measures to characterize the identified set through the zeros of a discrepancy function with an adversarial game interpretation. The set can be a singleton, resulting in point identification. A feature of many econometric models, with or without distributional assumptions on the error terms, is that the probability measure of observed variables can be expressed as a linear transformation of the probability measure of latent variables. This structure provides a unifying framework and facilitates computation and inference via linear programming. We demonstrate the versatility of our approach by applying it to nonlinear panel models with fixed effects, with parametric and nonparametric error distributions, and across various exogeneity restrictions, including strict and sequential.
Bridging an energy system model with an ensemble deep-learning approach for electricity price forecasting
Authors: Souhir Ben Amor, Thomas Möbius, Felix Müsgens
Date: 2024-11-07 00:00:00

This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benchmarks the results against several econometric alternatives. Lastly, we demonstrate the economic value of improved price estimators maximising the revenue from an electric storage resource. The results demonstrate that our integrated model improves overall forecasting accuracy by 18 %, compared to available literature benchmarks. Furthermore, our robustness checks reveal that a) the Ensemble Deep Neural Network model performs best in our dataset and b) adding output from the techno-economic energy systems model as econometric model input improves the performance of all econometric models. The empirical relevance of the forecast improvement is confirmed by the results of the exemplary storage optimisation, in which the integration of the techno-economic energy system model leads to a revenue increase of up to 10 %.
Words that Move Markets- Quantifying the Impact of RBI's Monetary Policy Communications on Indian Financial Market
Authors: Rohit Kumar, Sourabh Bikas Paul, Nikita Singh
Date: 2024-11-07 00:00:00
Tasks: Management, Sentiment Analysis

We analyze the impact of the Reserve Bank of India's (RBI) monetary policy communications on Indian financial market from April 2014 to June 2024 using advanced natural language processing techniques. Employing BERTopic for topic modeling and a fine-tuned RoBERTa model for sentiment analysis, we assess how variations in sentiment across different economic topics affect the stock market. Our findings indicate that dovish sentiment generally leads to declines in equity markets, particularly in topics related to the interest rate policy framework and economic growth, suggesting that market participants interpret dovish language as signaling economic weakness rather than policy easing. Conversely, dovish sentiment regarding foreign exchange reserves management has a positive impact on equity market. These results highlight the importance of topic-specific communication strategies for central banks in emerging markets.
On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages
Authors: Marco Caliendo, Katrin Huber, Ingo E. Isphording, Jakob Wegmann
Date: 2024-11-07 00:00:00
Tasks: Survey

Surveys are an indispensable source of data for applied economic research; however, their reliance on self-reported information can introduce bias, especially if core variables such as personal income are misreported. To assess the extent and impact of this misreporting bias, we compare self-reported wages from the German Socio-Economic Panel (SOEP) with administrative wages from social security records (IEB) for the same individuals. Using a novel and unique data linkage (SOEP-ADIAB), we identify a modest but economically significant reporting bias, with SOEP respondents underreporting their administrative wages by about 7.3%. This misreporting varies systematically with individual, household, and especially job and firm characteristics. In replicating common empirical analyses in which wages serve as either dependent or independent variables, we find that misreporting is consequential for some, but not all estimated relationships. It turns out to be inconsequential for examining the returns to education, but relevant for analyzing the gender wage gap. In addition we find that misreporting bias can significantly affect the results when wage is used as the independent variable. Specifically, estimates of the wage-satisfaction relationship are substantially overestimated when based on survey data, although this bias is mitigated when focusing on interpersonal changes. Our findings underscore that survey-based measures of individual wages can significantly bias commonly estimated empirical relationships. They also demonstrate the enormous research potential of linked administrative-survey data.
The economic value of empowering older patients transitioning from hospital to home: Evidence from the 'Your Care Needs You' intervention
Authors: Alfredo Palacios, Simon Walker, Beth Woods, Catherine Hewitt, Alison Cracknell, Jenni Murray, Rebecca Lawton, Gerry Richardson
Date: 2024-11-07 00:00:00
Tasks: MME, Sensitivity

Hospital-to-home transitions are a critical component of effective healthcare delivery, especially for patients aged 75 and older. This study evaluates the cost-effectiveness of the 'Your Care Needs You' (YCNY) intervention, a patient-centred approach designed to empower older adults during discharge, compared to standard care. The analysis adopts the perspective of the National Health Service (NHS) and Personal Social Services. Data were drawn from a cluster randomised controlled trial (cRCT) conducted within the UK NHS over a 90-day post-discharge follow-up period. Adjusted differences in costs and Quality-Adjusted Life Years (QALYs) were estimated using Multilevel Mixed-Effects Generalised Linear Models (MME-GLM) to account for the hierarchical structure of the trial design. Alternatively, Seemingly Unrelated Regression (SUR) models were employed to address potential correlations between costs and QALYs. Scenario analyses and probabilistic sensitivity analyses were conducted to assess the robustness of the results. The YCNY intervention reduced costs by {\pounds}269 and achieved a QALY gain of 0.0057, resulting in a net health benefit (NHB) of 0.0246 at a {\pounds}15,000/QALY threshold. It demonstrated an 89% probability of cost-effectiveness compared to standard care within the trial's time horizon. Findings remained robust across alternative scenarios and sensitivity analyses. These results suggest that YCNY is a promising and potentially cost-effective strategy for improving hospital-to-home transitions for older adults. By enhancing outcomes and reducing costs, the study supports integrating patient-centred interventions like YCNY into routine NHS practice, with the potential to improve both efficiency and quality of healthcare delivery.
Artificial Intelligence in Financial Forecasting: Analyzing the Suitability of AI Models for Dollar/TL Exchange Rate Predictions
Authors: Asef Yelghi, Aref Yelghi, Shirmohammad Tavangari
Date: 2024-11-06 00:00:00
Tasks: valid

The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.
Robust Regulation of Labour Contracts
Authors: Théo Durandard, Alexis Ghersengorin
Date: 2024-11-07 00:00:00

We study the robust regulation of labour contracts in moral hazard problems. A firm offers a contract to incentivise a worker protected by limited liability. A regulator chooses the set of permissible contracts to (i) improve efficiency and (ii) protect the worker. The regulator does not know the worker's actions and the firm's costs and evaluates regulations by their worst-case regret. The regret-minimising regulation imposes a minimum piece rate compensation for the worker: it allows all contracts above a minimum linear contract. The slope of the minimum contract balances the worker's protection and the necessary flexibility for incentive provision.
A Pre-training Framework that Encodes Noise Information for Speech Quality Assessment
Authors: Subrina Sultana, Donald S. Williamson
Date: 2024-11-07 00:00:00
Tasks: Self-Supervised Learning

Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks. SSL uses global and contextual methods to produce robust representations, where SSL even outperforms supervised models. Most self-supervised approaches, however, are limited to embedding information about, i.e., the phonemes, speaker identity, and emotion, into the extracted representations, where they become invariant to background sounds due to contrastive and auto-regressive learning. This is limiting because many downstream tasks leverage noise information to function accurately. Therefore, we propose a pre-training framework that learns information pertaining to background noise in a supervised manner, while jointly embedding speech information using a self-supervised strategy. We experiment with multiple encoders and show that our framework is useful for perceptual speech quality estimation, which relies on background cues. Our results show that the proposed approach improves performance with fewer parameters, in comparison to multiple baselines.