pwc-archive/papers-with-abstracts huggingface dataset:
Specific Nucleic Acid Detection Using a Nanoparticle Hybridization Assay
Authors: A. A. Aldakheel, C. B. Raub, H. T. Bui
Date: 2024-09-06 00:00:00

Simple methods to detect biomolecules including specific nucleic acid sequences have received renewed attention since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus pandemic. Notably, biomolecule detection that uses some form of signal amplification will have some form of amplification-related error, which in the polymerase chain reaction involves mispriming and subsequent signal amplification in the no template control, ultimately providing a limit of detection. To demonstrate the feasibility of the detection of a DNA target sequence without molecular or chemical signal amplification that avoids amplification errors, a gold nanoparticle aggregation assay was developed and tested. Two primers bracketing a 94 base pair target sequence from SARS-CoV-2 were conjugated to 10 nm diameter gold nanoparticles by the salt aging method, with conjugation and primer-target hybridization confirmed by agarose gel electrophoresis and nanospectrophotometry. Upon mixing of both conjugated nanoparticles with target, a surface plasmon resonance shift of 6 nm was observed, and lower electrophoretic mobility of a band containing both DNA fluorescence and gold absorption signals. This did not occur in the presence of a control DNA molecule of the same size and composition as the target but with a randomly scrambled base position. Nanoparticle tracking at 30 frames per second using a sensitive darkfield microscope revealed a lower measured diffusion coefficient of scattering objects in the target mixture than in the control mixture or with bare gold nanoparticles.
Demonstration of Beyond Terabit/s/lambda Nonlinearity-free Transmission over the Hollow-core Fibre
Authors: Yang Hong, Sylvain Almonacil, Haik Mardoyan, Carina Castineiras Carrero, Sergio Osuna, Javier R. Gomez, David R. Knight, Jeremie Renaudier
Date: 2024-08-21 00:00:00

We demonstrate nonlinearity-free transmission of Terabit/s/lambda PCS-64QAM signals through an HCF-based optical recirculating loop, which yields ~17.4% higher capacity than SMF-based loop under 23-dBm launch power (~13.5 dBm/channel) after 25 loops. Both lab experiment and field trial show HCF exhibits ~1.6-us/km lower latency than SMF.
First Single-Carrier Transmission at Net Data Rates of 1.6 Tb/s over 9075 km and 2.4 Tb/s over 1210 km Using 300 GBd Dual-Polarization Signals and Probabilistic Constellation Shaping
Authors: Haik Mardoyan, Daniel Drayss, Sylvain Almonacil, Dengyang Fang, Alban Sherifaj, Amirhossein Ghazisaeidi, Mohamed Kelany, Carina Castineiras Carrero, Christian Koos, Jeremie Renaudier
Date: 2024-08-21 00:00:00

We report long-haul transmissions of single-carrier 300 GBd dual-polarization signals with optical arbitrary waveform generation and measurement. We demonstrate net 1.6 Tb/s over 9075 km with PCS-16QAM and 2.4 Tb/s over 1210 km with PCS-36QAM.
Pupil-Adaptive 3D Holography Beyond Coherent Depth-of-Field
Authors: Yujie Wang, Baoquan Chen, PRANEETH CHAKRAVARTHULA
Date: 2024-08-17 00:00:00
Tasks: 3D Holography

Recent holographic display approaches propelled by deep learning have shown remarkable success in enabling high-fidelity holographic projections. However, these displays have still not been able to demonstrate realistic focus cues, and a major gap still remains between the defocus effects possible with a coherent light-based holographic display and those exhibited by incoherent light in the real world. Moreover, existing methods have not considered the effects of the observer's eye pupil size variations on the perceived quality of 3D projections, especially on the defocus blur due to varying depth-of-field of the eye. In this work, we propose a framework that bridges the gap between the coherent depth-of-field of holographic displays and what is seen in the real world due to incoherent light. To this end, we investigate the effect of varying shape and motion of the eye pupil on the quality of holographic projections, and devise a method that changes the depth-of-the-field of holographic projections dynamically in a pupil-adaptive manner. Specifically, we introduce a learning framework that adjusts the receptive fields on-the-go based on the current state of the observer's eye pupil to produce image effects that otherwise are not possible in current computer-generated holography approaches. We validate the proposed method both in simulations and on an experimental prototype holographic display, and demonstrate significant improvements in the depiction of depth-of-field effects, outperforming existing approaches both qualitatively and quantitatively by at least 5 dB in peak signal-to-noise ratio.
Trends and biases in the social cost of carbon
Authors: Richard S. J. Tol
Date: 2024-09-12 00:00:00

An updated and extended meta-analysis confirms that the central estimate of the social cost of carbon is around $200/tC with a large, right-skewed uncertainty and trending up. The pure rate of time preference and the inverse of the elasticity of intertemporal substitution are key assumptions, the total impact of 2.5K warming less so. The social cost of carbon is much higher if climate change is assumed to affect economic growth rather than the level of output and welfare. The literature is dominated by a relatively small network of authors, based in a few countries. Publication and citation bias have pushed the social cost of carbon up.
Testing for a Forecast Accuracy Breakdown under Long Memory
Authors: Jannik Kreye, Philipp Sibbertsen
Date: 2024-09-11 00:00:00
Tasks: Time Series

We propose a test to detect a forecast accuracy breakdown in a long memory time series and provide theoretical and simulation evidence on the memory transfer from the time series to the forecast residuals. The proposed method uses a double sup-Wald test against the alternative of a structural break in the mean of an out-of-sample loss series. To address the problem of estimating the long-run variance under long memory, a robust estimator is applied. The corresponding breakpoint results from a long memory robust CUSUM test. The finite sample size and power properties of the test are derived in a Monte Carlo simulation. A monotonic power function is obtained for the fixed forecasting scheme. In our practical application, we find that the global energy crisis that began in 2021 led to a forecast break in European electricity prices, while the results for the U.S. are mixed.
The Surprising Robustness of Partial Least Squares
Authors: João B. Assunção, Pedro Afonso Fernandes
Date: 2024-09-09 00:00:00
Tasks: Dimensionality Reduction, valid

Partial least squares (PLS) is a simple factorisation method that works well with high dimensional problems in which the number of observations is limited given the number of independent variables. In this article, we show that PLS can perform better than ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO) and ridge regression in forecasting quarterly gross domestic product (GDP) growth, covering the period from 2000 to 2023. In fact, through dimension reduction, PLS proved to be effective in lowering the out-of-sample forecasting error, specially since 2020. For the period 2000-2019, the four methods produce similar results, suggesting that PLS is a valid regularisation technique like LASSO or ridge.
Difference-in-Differences with Multiple Events
Authors: Lin-Tung Tsai
Date: 2024-09-08 00:00:00

This paper studies staggered Difference-in-Differences (DiD) design when there is a second event confounding the target event. When the events are correlated, the treatment and the control group are unevenly exposed to the effects of the second event, causing an omitted event bias. To address this bias, I propose a two-stage DiD design. In the first stage, I estimate the combined effects of both treatments using a control group that is neither treated nor confounded. In the second stage, I isolate the effects of the target treatment by leveraging a parallel treatment effect assumption and a control group that is treated but not yet confounded. Finally, I apply this method to revisit the effect of minimum wage increases on teen employment using state-level hikes between 2010 and 2020. I find that the Medicaid expansion under the ACA is a significant confounder: controlling for this bias reduces the short-term estimate of the minimum wage effect by two-thirds.
DEPLOYERS: An agent based modeling tool for multi country real world data
Authors: Martin Jaraiz, Ruth Pinacho
Date: 2024-09-07 00:00:00
Tasks: CPU

We present recent progress in the design and development of DEPLOYERS, an agent-based macroeconomics modeling (ABM) framework, capable to deploy and simulate a full economic system (individual workers, goods and services firms, government, central and private banks, financial market, external sectors) whose structure and activity analysis reproduce the desired calibration data, that can be, for example a Social Accounting Matrix (SAM) or a Supply-Use Table (SUT) or an Input-Output Table (IOT).Here we extend our previous work to a multi-country version and show an example using data from a 46-countries 64-sectors FIGARO Inter-Country IOT. The simulation of each country runs on a separate thread or CPU core to simulate the activity of one step (month, week, or day) and then interacts (updates imports, exports, transfer) with that country's foreign partners, and proceeds to the next step. This interaction can be chosen to be aggregated (a single row and column IO account) or disaggregated (64 rows and columns) with each partner. A typical run simulates thousands of individuals and firms engaged in their monthly activity and then records the results, much like a survey of the country's economic system. This data can then be subjected to, for example, an Input-Output analysis to find out the sources of observed stylized effects as a function of time in the detailed and realistic modeling environment that can be easily implemented in an ABM framework.
Horowitz-Manski-Lee Bounds with Multilayered Sample Selection
Authors: Kory Kroft, Ismael Mourifié, Atom Vayalinkal
Date: 2024-09-06 00:00:00

This paper investigates the causal effect of job training on wage rates in the presence of firm heterogeneity. When training affects the sorting of workers to firms, sample selection is no longer binary but is ``multilayered". This paper extends the canonical Heckman (1979) sample selection model -- which assumes selection is binary -- to a setting where it is multilayered. In this setting Lee bounds set identifies a total effect that combines a weighted-average of the causal effect of job training on wage rates across firms with a weighted-average of the contrast in wages between different firms for a fixed level of training. Thus, Lee bounds set identifies a policy-relevant estimand only when firms pay homogeneous wages and/or when job training does not affect worker sorting across firms. We derive analytic expressions for sharp bounds for the causal effect of job training on wage rates at each firm that leverage information on firm-specific wages. We illustrate our partial identification approach with two empirical applications to job training experiments. Our estimates demonstrate that even when conventional Lee bounds are strictly positive, our within-firm bounds can be tight around 0, showing that the canonical Lee bounds may capture only a pure sorting effect of job training.
An MPEC Estimator for the Sequential Search Model
Authors: Shinji Koiso, Suguru Otani
Date: 2024-09-06 00:00:00
Tasks: model

This paper proposes a constrained maximum likelihood estimator for sequential search models, using the MPEC (Mathematical Programming with Equilibrium Constraints) approach. This method enhances numerical accuracy while avoiding ad hoc components and errors related to equilibrium conditions. Monte Carlo simulations show that the estimator performs better in small samples, with lower bias and root-mean-squared error, though less effectively in large samples. Despite these mixed results, the MPEC approach remains valuable for identifying candidate parameters comparable to the benchmark, without relying on ad hoc look-up tables, as it generates the table through solved equilibrium constraints.
Extreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals
Authors: Yuya Sasaki, Yulong Wang
Date: 2024-09-06 00:00:00

We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures robust performance even in extreme tails, where data may be sparse or entirely absent. Simulation studies confirm the theoretical robustness of our approach. Applying our method to assess the impact of job training provided by the Job Training Partnership Act (JTPA), we find significantly negative QTEs for the lowest quantiles (i.e., the most disadvantaged individuals), contrasting with previous literature that emphasizes positive QTEs for intermediate quantiles.
Performance of Empirical Risk Minimization For Principal Component Regression
Authors: Christian Brownlees, Guðmundur Stefán Guðmundsson, Yaping Wang
Date: 2024-09-05 00:00:00
Tasks: Prediction, regression

This paper establishes bounds on the predictive performance of empirical risk minimization for principal component regression. Our analysis is nonparametric, in the sense that the relation between the prediction target and the predictors is not specified. In particular, we do not rely on the assumption that the prediction target is generated by a factor model. In our analysis we consider the cases in which the largest eigenvalues of the covariance matrix of the predictors grow linearly in the number of predictors (strong signal regime) or sublinearly (weak signal regime). The main result of this paper shows that empirical risk minimization for principal component regression is consistent for prediction and, under appropriate conditions, it achieves near-optimal performance in both the strong and weak signal regimes.
Momentum Dynamics in Competitive Sports: A Multi-Model Analysis Using TOPSIS and Logistic Regression
Authors: Mingpu Ma
Date: 2024-09-04 00:00:00
Tasks: regression

This paper explores the concept of "momentum" in sports competitions through the use of the TOPSIS model and 0-1 logistic regression model. First, the TOPSIS model is employed to evaluate the performance of two tennis players, with visualizations used to analyze the situation's evolution at every moment in the match, explaining how "momentum" manifests in sports. Then, the 0-1 logistic regression model is utilized to verify the impact of "momentum" on match outcomes, demonstrating that fluctuations in player performance and the successive occurrence of successes are not random. Additionally, this paper examines the indicators that influence the reversal of game situations by analyzing key match data and testing the accuracy of the models with match data. The findings show that the model accurately explains the conditions during matches and can be generalized to other sports competitions. Finally, the strengths, weaknesses, and potential future improvements of the model are discussed.
The Impact of Data Elements on Narrowing the Urban-Rural Consumption Gap in China: Mechanisms and Policy Analysis
Authors: Mingpu Ma
Date: 2024-09-04 00:00:00

The urban-rural consumption gap, as one of the important indicators in social development, directly reflects the imbalance in urban and rural economic and social development. Data elements, as an important component of New Quality Productivity, are of significant importance in promoting economic development and improving people's living standards in the information age. This study, through the analysis of fixed-effects regression models, system GMM regression models, and the intermediate effect model, found that the development level of data elements to some extent promotes the narrowing of the urban-rural consumption gap. At the same time, the intermediate variable of urban-rural income gap plays an important role between data elements and consumption gap, with a significant intermediate effect. The results of the study indicate that the advancement of data elements can promote the balance of urban and rural residents' consumption levels by reducing the urban-rural income gap, providing theoretical support and policy recommendations for achieving common prosperity and promoting coordinated urban-rural development. Building upon this, this paper emphasizes the complex correlation between the development of data elements and the urban-rural consumption gap, and puts forward policy suggestions such as promoting the development of the data element market, strengthening the construction of the digital economy and e-commerce, and promoting integrated urban-rural development. Overall, the development of data elements is not only an important path to reducing the urban-rural consumption gap but also one of the key drivers for promoting the balanced development of China's economic and social development. This study has a certain theoretical and practical significance for understanding the mechanism of the urban-rural consumption gap and improving policies for urban-rural economic development.
The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model
Authors: Mingpu Ma
Date: 2024-09-04 00:00:00

This paper presents an analysis of Green Gross Domestic Product (GGDP) using the System of Environmental-Economic Accounting (SEEA) model to evaluate its impact on global climate mitigation and economic health. GGDP is proposed as a superior measure to tradi-tional GDP by incorporating natural resource consumption, environmental pollution control, and degradation factors. The study develops a GGDP model and employs grey correlation analysis and grey prediction models to assess its relationship with these factors. Key findings demonstrate that replacing GDP with GGDP can positively influence climate change, partic-ularly in reducing CO2 emissions and stabilizing global temperatures. The analysis further explores the implications of GGDP adoption across developed and developing countries, with specific predictions for China and the United States. The results indicate a potential increase in economic levels for developing countries, while developed nations may experi-ence a decrease. Additionally, the shift to GGDP is shown to significantly reduce natural re-source depletion and population growth rates in the United States, suggesting broader envi-ronmental and economic benefits. This paper highlights the universal applicability of the GGDP model and its potential to enhance environmental and economic policies globally.
Distribution Regression Difference-In-Differences
Authors: Iván Fernández-Val, Jonas Meier, Aico van Vuuren, Francis Vella
Date: 2024-09-03 00:00:00
Tasks: counterfactual, regression

We provide a simple distribution regression estimator for treatment effects in the difference-in-differences (DiD) design. Our procedure is particularly useful when the treatment effect differs across the distribution of the outcome variable. Our proposed estimator easily incorporates covariates and, importantly, can be extended to settings where the treatment potentially affects the joint distribution of multiple outcomes. Our key identifying restriction is that the counterfactual distribution of the treated in the untreated state has no interaction effect between treatment and time. This assumption results in a parallel trend assumption on a transformation of the distribution. We highlight the relationship between our procedure and assumptions with the changes-in-changes approach of Athey and Imbens (2006). We also reexamine the Card and Krueger (1994) study of the impact of minimum wages on employment to illustrate the utility of our approach.
Bandit Algorithms for Policy Learning: Methods, Implementation, and Welfare-performance
Authors: Toru Kitagawa, Jeff Rowley
Date: 2024-08-31 00:00:00

Static supervised learning-in which experimental data serves as a training sample for the estimation of an optimal treatment assignment policy-is a commonly assumed framework of policy learning. An arguably more realistic but challenging scenario is a dynamic setting in which the planner performs experimentation and exploitation simultaneously with subjects that arrive sequentially. This paper studies bandit algorithms for learning an optimal individualised treatment assignment policy. Specifically, we study applicability of the EXP4.P (Exponential weighting for Exploration and Exploitation with Experts) algorithm developed by Beygelzimer et al. (2011) to policy learning. Assuming that the class of policies has a finite Vapnik-Chervonenkis dimension and that the number of subjects to be allocated is known, we present a high probability welfare-regret bound of the algorithm. To implement the algorithm, we use an incremental enumeration algorithm for hyperplane arrangements. We perform extensive numerical analysis to assess the algorithm's sensitivity to its tuning parameters and its welfare-regret performance. Further simulation exercises are calibrated to the National Job Training Partnership Act (JTPA) Study sample to determine how the algorithm performs when applied to economic data. Our findings highlight various computational challenges and suggest that the limited welfare gain from the algorithm is due to substantial heterogeneity in causal effects in the JTPA data.
Economic impacts of a drastic gas supply shock and short-term mitigation strategies
Authors: Anton Pichler, Jan Hurt, Tobias Reisch, Johannes Stangl, Stefan Thurner
Date: 2024-09-12 00:00:00
Tasks: Management

The Russian invasion of Ukraine on February 24, 2022 entailed the threat of a drastic and sudden reduction of natural gas supply to the European Union. This paper presents a techno-economic analysis of the consequences of a sudden gas supply shock to Austria, one of the most dependent countries on imports of Russian gas. Our analysis comprises (a) a detailed assessment of supply and demand side countermeasures to mitigate the immediate shortfall in Russian gas imports, (b) a mapping of the net reduction in gas supply to industrial sectors to quantify direct economic shocks and expected relative reductions in gross output and (c) the quantification of higher-order economic impacts through using a dynamic out-of-equilibrium input-output model. Our results show that potential economic consequences can range from relatively mild to highly severe, depending on the implementation and success of counteracting mitigation measures. We find that securing alternative gas imports, storage management, and incentivizing fuel switching represent the most important short-term policy levers to mitigate the adverse impacts of a sudden import stop.