pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Mitigation of SIR Epidemics Under Model Uncertainty
Authors: Baike She, Shreyas Sundaram, Philip E. Paré
Date: 2022-09-03 00:00:00
Tasks: model

We study the impact of model parameter uncertainty on optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider testing for isolation as the control strategy. We use a testing strategy to remove (isolate) a portion of the infected population. Our goal is to maintain the daily infected population below a certain level, while minimizing the total number of tests. Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a testing strategy by overestimating the seriousness of the epidemic and study the feasibility of the system under the impact of model parameter uncertainty. Compared to the optimal testing strategy, we establish that the proposed strategy under model parameter uncertainty will flatten the curve effectively but require more tests and a longer time period.
Distributed Interval Observers for Bounded-Error LTI Systems
Authors: Mohammad Khajenejad, Scott Brown, Sonia Martinez
Date: 2022-09-02 00:00:00

This paper proposes a novel distributed interval observer design for linear time-invariant (LTI) discrete-time systems subject to bounded disturbances. In the proposed observer algorithm, each agent in a networked group exchanges locally-computed framers or interval-valued state estimates with neighbors, and coordinates its update via an intersection operation. We show that the proposed framers are guaranteed to bound the true state trajectory of the system by construction, i.e., without imposing any additional assumptions or constraints. Moreover, we provide necessary and sufficient conditions for the collective stability of the distributed observer, i.e., to guarantee the uniform boundedness of the observer error sequence. In particular, we show that such conditions can be tractably satisfied through a constructive and distributed approach. Moreover, we provide an algorithm to verify some structural conditions for a given system, which guarantee the existence of the proposed observer. Finally, simulation results demonstrate the effectiveness of our proposed method compared to an existing distributed observer in the literature.
Quasicriticality explains variability of human neural dynamics across life span
Authors: L. J. Fosque, A. Alipour, M. Zare, R. V. Williams-Garcia, J. M. Beggs, G. Ortiz
Date: 2022-09-06 00:00:00

Ageing impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset -- the largest ageing cohort available -- in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to ageing, and increased vulnerability to distraction from irrelevant information. Our findings suggest a platform to develop biomarkers of neurological health.
Microstructure estimation from diffusion-MRI: Compartmentalized models in permeable cellular tissue
Authors: Rémy Gardier, Juan Luis Villarreal Haro, Erick J Canales-Rodrıguez, Ileana O. Jelescu, Gabriel Girard, Jonathan Rafael-Patino, Jean-Philippe Thiran
Date: 2022-09-06 00:00:00
Tasks: Diffusion  MRI

Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to characterize brain tissue microstructure employing tissue-specific biophysical models. A current limitation, however, is that most of the proposed models are based on the assumption of negligible water exchange between the intra- and extracellular compartments, which might not be valid in various brain tissues, including unmyelinated axons, gray matter, and tumors. The purpose of this work is to quantify the effect of membrane permeability on the estimates of two popular models neglecting exchange, and compare their performance with a model including exchange. To this aim, DW-MRI experiments were performed in controlled environments with Monte-Carlo simulations. The DW-MRI signals were generated in numerical substrates mimicking biological tissue made of spherical cells with permeable membranes like cancerous tissue or the brain gray matter. From these signals, the substrates properties were estimated using SANDI and VERDICT, the two compartment-based models neglecting exchange, and CEXI, a new model which includes exchange. Our results show that, in cellular permeable tissue, the model with exchange outperformed models without exchange in the estimation of the tissue properties by providing more stable estimates of cell size, intracellular volume fraction and extracellular diffusion coefficient. Moreover, the model with exchange estimated accurately the exchange time in the range of permeability reported for cellular tissue. Finally, the simulations performed in this work showed that the exchange between the intracellular and the extracellular space cannot be neglected in permeable tissue with a conventional PGSE sequence, to obtain accurate estimates. Consequently, existing compartmentalized models of impermeable tissue cannot be used for microstructure estimation of cellular permeable tissue.
Holding Periods: Measuring the Inverse of Money Velocity from Transaction Records
Authors: Carolina E. S. Mattsson, Allison Luedtke, Frank W. Takes
Date: 2022-09-03 00:00:00

This paper defines the average holding period of money and proposes a methodology for fully disaggregated measurement. Our measure is shown to be the inverse of the transfer velocity of money under stationary conditions, which is implicitly assumed in conventional aggregate measurement. Our methodology does not require stationarity. We leverage a recent computational technique to extract empirical holding periods from micro-level transaction data as recorded by real-world payment systems. This enables novel empirical analyses of money velocity under non-stationarity conditions. We illustrate several such analyses on Sarafu, a small digital community currency in Kenya, where transaction data is available from 25 January 2020 to 15 June 2021. Our measure implies faster circulation than does the aggregate transfer velocity; we can say that 58% of Sarafu was effectively static. We also disaggregate by geography to study the heterogeneous impact of economic disruptions related to the COVID-19 pandemic on Sarafu. Finally, we consider an ad-hoc currency management operation that took place in October 2020. Measuring the average holding period of money makes it possible to track money velocity during ongoing monetary interventions and on other important occasions when conditions are not stationary.
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP
Authors: Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
Date: 2022-09-05 00:00:00
Tasks: Data Augmentation, quantile regression

Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to nowcast conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk and the Expected Shortfall.
Combining Forecasts under Structural Breaks Using Graphical LASSO
Authors: Tae-Hwy Lee, Ekaterina Seregina
Date: 2022-09-04 00:00:00

In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes, which makes the forecast errors exhibit common factor structures. We use the Factor Graphical LASSO (FGL, Lee and Seregina (2023)) to separate common forecast errors from the idiosyncratic errors and exploit sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. We develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank's Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using FGL and RD-FGL.
Instrumental variable quantile regression under random right censoring
Authors: Jad Beyhum, Lorenzo Tedesco, Ingrid Van Keilegom
Date: 2022-09-03 00:00:00
Tasks: quantile regression, regression

This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile regression coefficients are a solution. Identification is obtained when this system of equations has a unique solution. Our estimation procedure solves an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove the validity of a bootstrap procedure for inference. The finite sample performance of the approach is evaluated through numerical simulations. An application to the national Job Training Partnership Act study illustrates the method.
Classicals versus Keynesians: Fifty Distinctions between Two Major Schools of Economic Thought
Authors: Seyyed Ali Zeytoon Nejad Moosavian
Date: 2022-09-06 00:00:00

Macroeconomics essentially discusses macroeconomic phenomena from the perspectives of various schools of economic thought, each of which takes different views on how macroeconomic agents make decisions and how the corresponding markets operate. Therefore, developing a clear, comprehensive understanding of how and in what ways these schools of economic thought differ is a key and a prerequisite for economics students to prosper academically and professionally in the discipline. This becomes even more crucial as economics students pursue their studies toward higher levels of education and graduate school, during which students are expected to attain higher levels of Bloom's taxonomy, including analysis, synthesis, evaluation, and creation. Teaching the distinctions and similarities of the two major schools of economic thought has never been an easy task to undertake in the classroom. Although the reason for such a hardship can be multi-fold, one reason has undoubtedly been students' lack of a holistic view on how the two mainstream economic schools of thought differ. There is strong evidence that students make smoother transition to higher levels of education after building up such groundwork, on which they can build further later on (e.g. Didia and Hasnat, 1998; Marcal and Roberts, 2001; Islam, et al., 2008; Green, et al., 2009; White, 2016). The paper starts with a visual spectrum of various schools of economic thought, and then narrows down the scope to the classical and Keynesian schools, i.e. the backbone of modern macroeconomics. Afterwards, a holistic table contrasts the two schools in terms of 50 aspects. Not only does this table help economics students enhance their comprehension, retention, and critical-thinking capability, it also benefits macroeconomic instructors to ...
Using the Interactive Graphic Syllabus in the Teaching of Economics
Authors: Seyyed Ali Zeytoon Nejad Moosavian
Date: 2022-09-06 00:00:00
Tasks: Management

Syllabus is essentially a concise outline of a course of study, and conventionally a text document. In the past few decades, however, two novel variations of syllabus have emerged, namely "the Graphic Syllabus" and "the Interactive Syllabus". Each of these two variations of syllabus has its own special advantages. The present paper argues that there could be devised a new combined version of the two mentioned variations, called "the Interactive Graphic Syllabus", which can potentially bring us the advantages of both at the same time. Specifically, using a well-designed Interactive Graphic syllabus can bring about many advantages such as clarifying complex relationships; causing a better retention; needing less cognitive energy for interpretation; helping instructors identify any snags in their course organization; capability of being integrated easily into a course management system; appealing to many of learning styles and engaging students with different learning styles.
Measuring Price Risk Aversion through Indirect Utility Functions: A Laboratory Experiment
Authors: Ali Zeytoon-Nejad
Date: 2022-09-06 00:00:00

The present paper introduces a theoretical framework through which the degree of risk aversion with respect to uncertain prices can be measured through the context of the indirect utility function (IUF) using a lab experiment. First, the paper introduces the main elements of the duality theory (DT) in economics. Next, it proposes the context of IUFs as a suitable framework for measuring price risk aversion through varying prices as opposed to varying payoffs, which has been common practice in the mainstream of experimental economics. Indeed, the DT in modern microeconomics indicates that the direct utility function (DUF) and the IUF are dual to each other, implicitly suggesting that the degree of risk aversion (or risk seeking) that a given rational subject exhibits in the context of the DUF must be equivalent to the degree of risk aversion (or risk seeking) elicited through the context of the IUF. This paper tests the accuracy of this theoretical prediction through a lab experiment using a series of relevant statistical tests. This study uses the multiple price list (MPL) method, which has been one of the most popular sets of elicitation procedures in experimental economics to study risk preferences in the experimental laboratory using non-interactive settings. The key findings of this study indicate that price risk aversion (PrRA) is statistically significantly greater than payoff risk aversion (PaRA). Additionally, it is shown that the risk preferences elicited under the expected utility theory (EUT) are somewhat subject to context. Other findings imply that the risk premium (RP), as a measure of willingness to pay for insuring an uncertain situation, is statistically significantly greater for stochastic prices compared to that for stochastic payoffs. These results are robust across different MPL designs and various statistical tests that are utilized.
The Coronavirus Tradeoff -- Life vs. Economy: Handling the Tradeoff Rationally and Optimally
Authors: Ali Zeytoon-Nejad, Tanzid Hasnain
Date: 2022-09-06 00:00:00

The recent coronavirus outbreak has made governments face an inconvenient tradeoff choice, i.e. the choice between saving lives and saving the economy, forcing them to make immensely consequential decisions among alternative courses of actions without knowing what the ultimate results would be for the society as a whole. This paper attempts to frame the coronavirus tradeoff problem as an economic optimization problem and proposes mathematical optimization methods to make rationally optimal decisions when faced with trade-off situations such as those involved in managing through the recent coronavirus pandemic. The framework introduced and the method proposed in this paper are on the basis of the theory of rational choice at a societal level, which assumes that the government is a rational, benevolent agent that systematically and purposefully takes into account the social marginal costs and social marginal benefits of its actions to its citizens and makes decisions that maximize the society's well-being as a whole. We approach solving this tradeoff problem from a static as well as a dynamic point of view. Finally, we provide several numerical examples clarifying how the proposed framework and methods can be applied in the real-world context.
Do Unions Shape Political Ideologies at Work?
Authors: Johannes Matzat, Aiko Schmeißer
Date: 2022-09-06 00:00:00
Tasks: Management

Labor unions influence economic outcomes not only through bargaining with employers over work contracts but also via political activities that can profoundly shape political systems. In unionized workplaces, they may mobilize and change the ideological positions of both unionizing workers and their non-unionizing management. In this paper, we analyze the workplace-level impact of unionization on workers' and managers' political campaign contributions in the United States from 1980 to 2016. To do so, we link establishment-level union election data with transaction-level campaign contributions to federal and local candidates. Using a difference-in-differences design, validated through regression discontinuity tests and a novel instrumental variable approach, we find that unionization leads to a leftward shift of campaign contributions. Unionization increases the support for Democrats relative to Republicans not only among workers but also among managers, suggesting that it fosters political alignment between the two groups. Our findings are not driven by compositional changes of the workforce and are stronger in establishments where, after union certification, the employer and union agree on a collective bargaining agreement.
Single and Attractive: Uniqueness and Stability of Economic Equilibria under Monotonicity Assumptions
Authors: Patrizio Bifulco, Jochen Glück, Oliver Krebs, Bohdan Kukharskyy
Date: 2022-09-06 00:00:00

This paper characterizes equilibrium properties of a broad class of economic models that allow multiple heterogeneous agents to interact in heterogeneous manners across several markets. Our key contribution is a new theorem providing sufficient conditions for uniqueness and stability of equilibria in this class of models. To illustrate the applicability of our theorem, we characterize the general equilibrium properties of two commonly used quantitative trade models. Specifically, our analysis provides a first proof of uniqueness and stability of the equilibrium in multi-country trade models featuring (i) multiple sectors, or (ii) heterogeneity across countries in terms of their labor cost shares. These examples also provide a practical toolkit for future research on how our theorem can be applied to establish uniqueness and stability of equilibria in a broad set of economic models.
TSO-DSO Coordination for the Procurement of Balancing and Congestion Management Services: Assessment of a meshed-to-meshed topology
Authors: Leandro Lind, Rafael Cossent, Pablo Frias
Date: 2022-09-06 00:00:00
Tasks: Management

This paper proposes a comprehensive model for different Coordination Schemes (CSs) for Transmission (TSO) and Distribution System Operators (DSO) in the context of distributed flexibility procurement for balancing and congestion management. The model proposed focuses on the coordination between the EHV (TSO) and the HV (DSO) levels, exploring the meshed-to-meshed topology, including multiple TSO-DSO interface substations. The model is then applied to a realistic case study in which the Swedish power system is modeled for one year, considering a representation of the transmission grid together with the subtransmission grid of Uppsala city. The base case scenario is then subject to different scalability and replication scenarios. The paper corroborates the finding that the Common CS leads to the least overall cost of flexibility procurement. Moreover, it shows the effectiveness of the Local Flexibility Market (LFM) for the DSO in the Swedish context in reducing potential penalties in a Multi-level CS.
Bidirectional coupling of a long-term integrated assessment model REMIND v3.0.0 with an hourly power sector model DIETER v1.0.2
Authors: Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, Gunnar Luderer
Date: 2022-09-06 00:00:00
Tasks: model

Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the spatio-temporal detail required to properly analyze the key role of variable renewable electricity (VRE) for decarbonizing the power sector and end-use electrification. In contrast, power sector models (PSMs) incorporate high spatio-temporal resolutions, but tend to have narrower scopes and shorter time horizons. To overcome these limitations, we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a IAM and a PSM. This framework uses the market values of power generation as well as the capture prices of demand in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REMIND and the PSM DIETER, and confirm the theoretical prediction of almost-full convergence both in terms of decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is <5% for a simple configuration (no storage, no flexible demand), and 6-7% for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. Since our approach is based on fundamental economic principles, it is applicable also to other IAM-PSM pairs.