pwc-archive/papers-with-abstracts huggingface dataset:
A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes
Authors: Radostin D. Simitev, Rebecca J. Gilchrist, Zhechao Yang, Rachel Myles, Francis Burton, Godfrey L. Smith
Date: 2025-01-14 00:00:00
Tasks: parameter estimation

Recent high-throughput experiments unveil substantial electrophysiological diversity among uncoupled healthy myocytes under identical conditions. To quantify inter-cell variability, the values of a subset of the parameters in a well-regarded mathematical model of the action potential of rabbit ventricular myocytes are estimated from fluorescence voltage measurements of a large number of cells. Statistical inference yields a population of nearly 1200 cell-specific model variants that, on a population-level replicate experimentally measured biomarker ranges and distributions, and in contrast to earlier studies, also match experimental biomarker values on a cell-by-cell basis. This model population may be regarded as a random sample from the phenotype of healthy rabbit ventricular myocytes. Uni-variate and bi-variate joint marginal distributions of the estimated parameters are presented, and the parameter dependencies of several commonly utilised electrophysiological biomarkers are revealed. Parameter values are weakly correlated, while summary metrics such as the action potential duration are not strongly dependent on any single electrophysiological characteristic of the myocyte. Our results demonstrate the feasibility of accurately and efficiently fitting entire action potential waveforms at scale. Keywords: cellular excitability, rabbit ventricular myocytes, fluorescence voltage measurements, action potential waveform, parameter estimation in differential equations, noisy time series
Geomorphodynamics, evolution, and ecology of vertical roots
Authors: Martin Heidelman, Dervis Can Vural
Date: 2025-01-14 00:00:00

The roots of some coastal and wetland trees grow peculiar vertical protrusions, the function of which remains unclear. Here, using computational simulations based on first-principles fluid and sedimentation dynamics, we argue that the protrusions work together to create an elevated patch of sediment downstream of the tree, thereby creating its own fertile flood-protected breeding grounds for the seedlings. In our simulations, we vary the vertical root diameter, root spacing and total root area and show that there is an optimal vertical root spacing that depends on root thickness. Next, we quantify and discuss the cooperative effects between adjacent vertical root patches. Lastly, by varying vertical root spacing of a patch of trees, we estimate a maximal vegetation density for which vertical root production has a beneficial geomorphological response. Our hypothesis suggests that vertical roots, such as the "knee roots" of baldcypress trees, have an important role in shaping riparian geomorphology and community structure.
Rolling phase modulation regime for dynamic full field OCT
Authors: Tual Monfort, Kate Grieve, Olivier Thouvenin
Date: 2025-01-14 00:00:00

Dynamic full-field optical coherence tomography (DFFOCT) has recently emerged as an invaluable label-free microscopy technique, owing to its sensitivity to cell activity, as well as speed and sectioning ability. However, the quality of DFFOCT images are often degraded due to phase noise and fringe artifacts. In this work, we present a new implementation named Rolling-Phase (RP) DFFOCT, in which the reference arm is slowly scanned over magnitudes exceeding 2$\pi$. We demonstrate mathematically and experimentally that it shows superior image quality while enabling to extract both static and dynamic contrast simultaneously. We showcase RP DFFOCT on monkey retinal explant, and demonstrate its ability to better resolve subcellular structures, including intranuclear activity.
Convergence Rates of GMM Estimators with Nonsmooth Moments under Misspecification
Authors: Byunghoon Kang, Seojeong Lee, Juha Song
Date: 2025-01-16 00:00:00

The asymptotic behavior of GMM estimators depends critically on whether the underlying moment condition model is correctly specified. Hong and Li (2023, Econometric Theory) showed that GMM estimators with nonsmooth (non-directionally differentiable) moment functions are at best $n^{1/3}$-consistent under misspecification. Through simulations, we verify the slower convergence rate of GMM estimators in such cases. For the two-step GMM estimator with an estimated weight matrix, our results align with theory. However, for the one-step GMM estimator with the identity weight matrix, the convergence rate remains $\sqrt{n}$, even under severe misspecification.
Recovering latent linkage structures and spillover effects with structural breaks in panel data models
Authors: Ryo Okui, Yutao Sun, Wendun Wang
Date: 2025-01-16 00:00:00

This paper introduces a framework to analyze time-varying spillover effects in panel data. We consider panel models where a unit's outcome depends not only on its own characteristics (private effects) but also on the characteristics of other units (spillover effects). The linkage of units is allowed to be latent and may shift at an unknown breakpoint. We propose a novel procedure to estimate the breakpoint, linkage structure, spillover and private effects. We address the high-dimensionality of spillover effect parameters using penalized estimation, and estimate the breakpoint with refinement. We establish the super-consistency of the breakpoint estimator, ensuring that inferences about other parameters can proceed as if the breakpoint were known. The private effect parameters are estimated using a double machine learning method. The proposed method is applied to estimate the cross-country R&D spillovers, and we find that the R&D spillovers become sparser after the financial crisis.
The Impact of Digitalisation and Sustainability on Inclusiveness: Inclusive Growth Determinants
Authors: Radu Rusu, Camelia Oprean-Stan
Date: 2025-01-14 00:00:00
Tasks: Management

Inclusiveness and economic development have been slowed by the pandemics and military conflicts. This study investigates the main determinants of inclusiveness at the European level. A multi-method approach is used, with Principal Component Analysis (PCA) applied to create the Inclusiveness Index and Generalised Method of Moments (GMM) analysis used to investigate the determinants of inclusiveness. The data comprises a range of 22 years, from 2000 to 2021, for 32 European countries. The determinants of inclusiveness and their effects were identified. First, economic growth, industrial upgrading, electricity consumption, digitalisation, and the quantitative aspect of governance, all have a positive impact on inclusive growth in Europe. Second, the level of CO2 emissions and inflation have a negative impact on inclusiveness. Tomorrow's inclusive and sustainable growth must include investments in renewable energy, digital infrastructure, inequality policies, sustainable governance, human capital, and inflation management. These findings can help decision makers design inclusive growth policies.
Forecasting for monetary policy
Authors: Laura Coroneo
Date: 2025-01-13 00:00:00

This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England's inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks.
Social and Genetic Ties Drive Skewed Cross-Border Media Coverage of Disasters
Authors: Thiemo Fetzer, Prashant Garg
Date: 2025-01-13 00:00:00
Tasks: Articles

Climate change is increasing the frequency and severity of natural disasters worldwide. Media coverage of these events may be vital to generate empathy and mobilize global populations to address the common threat posed by climate change. Using a dataset of 466 news sources from 123 countries, covering 135 million news articles since 2016, we apply an event study framework to measure cross-border media activity following natural disasters. Our results shows that while media attention rises after disasters, it is heavily skewed towards certain events, notably earthquakes, accidents, and wildfires. In contrast, climatologically salient events such as floods, droughts, or extreme temperatures receive less coverage. This cross-border disaster reporting is strongly related to the number of deaths associated with the event, especially when the affected populations share strong social ties or genetic similarities with those in the reporting country. Achieving more balanced media coverage across different types of natural disasters may be essential to counteract skewed perceptions. Further, fostering closer social connections between countries may enhance empathy and mobilize the resources necessary to confront the global threat of climate change.
Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.'s Net Income and Stock Prices
Authors: Kevin Ungar, Camelia Oprean-Stan
Date: 2025-01-11 00:00:00
Tasks: regression

This article presents a comprehensive methodology for processing financial datasets of Apple Inc., encompassing quarterly income and daily stock prices, spanning from March 31, 2009, to December 31, 2023. Leveraging 60 observations for quarterly income and 3774 observations for daily stock prices, sourced from Macrotrends and Yahoo Finance respectively, the study outlines five distinct datasets crafted through varied preprocessing techniques. Through detailed explanations of aggregation, interpolation (linear, polynomial, and cubic spline) and lagged variables methods, the study elucidates the steps taken to transform raw data into analytically rich datasets. Subsequently, the article delves into regression analysis, aiming to decipher which of the five data processing methods best suits capital market analysis, by employing both linear and polynomial regression models on each preprocessed dataset and evaluating their performance using a range of metrics, including cross-validation score, MSE, MAE, RMSE, R-squared, and Adjusted R-squared. The research findings reveal that linear interpolation with polynomial regression emerges as the top-performing method, boasting the lowest validation MSE and MAE values, alongside the highest R-squared and Adjusted R-squared values.
Sectorial Exclusion Criteria in the Marxist Analysis of the Average Rate of Profit: The United States Case (1960-2020)
Authors: Jose Mauricio Gomez Julian
Date: 2025-01-09 00:00:00

The long-term estimation of the Marxist average rate of profit does not adhere to a theoretically grounded standard regarding which economic activities should or should not be included for such purposes, which is relevant because methodological non-uniformity can be a significant source of overestimation or underestimation, generating a less accurate reflection of the capital accumulation dynamics. This research aims to provide a standard Marxist decision criterion regarding the inclusion and exclusion of economic activities for the calculation of the Marxist average profit rate for the case of United States economic sectors from 1960 to 2020, based on the Marxist definition of productive labor, its location in the circuit of capital, and its relationship with the production of surplus value. Using wavelet-transformed Daubechies filters with increased symmetry, empirical mode decomposition, Hodrick-Prescott filter embedded in unobserved components model, and a wide variety of unit root tests the internal theoretical consistency of the presented criteria is evaluated. Also, the objective consistency of the theory is evaluated by a dynamic factor auto-regressive model, Principal Component Analysis, Singular Value Decomposition and Backward Elimination with Linear and Generalized Linear Models. The results are consistent both theoretically and econometrically with the logic of Marx's political economy.
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM
Authors: Konstantinos-Leonidas Bisdoulis
Date: 2024-12-27 00:00:00
Tasks: Feature Engineering, Feature Importance

Fluctuations in the stock market rapidly shape the economic world and consumer markets, impacting millions of individuals. Hence, accurately forecasting it is essential for mitigating risks, including those associated with inactivity. Although research shows that hybrid models of Deep Learning (DL) and Machine Learning (ML) yield promising results, their computational requirements often exceed the capabilities of average personal computers, rendering them inaccessible to many. In order to address this challenge in this paper we optimize LightGBM (an efficient implementation of gradient-boosted decision trees (GBDT)) for maximum performance, while maintaining low computational requirements. We introduce novel feature engineering techniques including indicator-price slope ratios and differences of close and open prices divided by the corresponding 14-period Exponential Moving Average (EMA), designed to capture market dynamics and enhance predictive accuracy. Additionally, we test seven different feature and target variable transformation methods, including returns, logarithmic returns, EMA ratios and their standardized counterparts as well as EMA difference ratios, so as to identify the most effective ones weighing in both efficiency and accuracy. The results demonstrate Log Returns, Returns and EMA Difference Ratio constitute the best target variable transformation methods, with EMA ratios having a lower percentage of correct directional forecasts, and standardized versions of target variable transformations requiring significantly more training time. Moreover, the introduced features demonstrate high feature importance in predictive performance across all target variable transformation methods. This study highlights an accessible, computationally efficient approach to stock market forecasting using LightGBM, making advanced forecasting techniques more widely attainable.
Evolution of exchange rate regime: Impact of macroeconomy of Bangladesh
Authors: Liza Fahmida
Date: 2025-01-16 00:00:00

Bangladesh has experienced two distinct exchange rate regimes: a fixed exchange rate system from January 1972 to May 2003 and a floating one since June 2003. After adopting the floating exchange rate regime, Bangladesh positively impacted macroeconomic development. The key macroeconomic variables considered include foreign reserves, worker's remittances, and export proceeds. However, ongoing challenges for the country include the depreciating trend of the local currency within a highly inflationary economy. This paper aims to evaluate macroeconomic performance across the two regimes and analyze the current currency situation in Bangladesh.
The National Intangible Resources and their Importance in the Current Knowledge-Based Economy
Authors: Camelia Oprean-Stan, Sebastian Emanuel Stan, Antonio Pele
Date: 2025-01-16 00:00:00
Tasks: Position

In this article, models for assessing national intangible resources are analysed through a lecture in the literature, and the best-known evaluation methods are categorized into academic models and models of international organizations, with the most important differences being identified. The European Innovation Scoreboard (EIS) and the World Economic Forum annual reports on Global Competitiveness were considered to assess Romania's position in the international context in terms of intangible assets. Despite the importance of intangible resources at national level and the fact that they are an important factor in determining economic growth in the current knowledge-based economy, this article concludes that Romania's position in the international context regarding intangible assets is very weak, with many weak points in research and innovation performance compared to other EU Member States. Therefore, there is a need in our country to re-evaluate the areas where all efforts need to be focused to stimulate innovation performance, to properly manage national intangible resources, a crucial process for improving the quality of life.
Discretionary vs nondiscretionary in fiscal mechanism. Non-automatic fiscal stabilisers vs automatic fiscal stabilisers
Authors: Vasile Bratian, Amelia Bucur, Camelia Oprean, Cristina Tanasescu
Date: 2025-01-15 00:00:00

The goal of the present study is to increase the intelligibility of macroeconomic phenomena triggered by governmental intervention in economy by means of fiscal policies. During cyclical movements, fiscal policy can play an important role in order to help stabilise the economy. But discretionary policy usually implies implementation lags and is not automatically reversed when economic conditions change. In contrast, automatic fiscal stabilisers (SFA) ensure a prompter, and self-correcting fiscal response. The present study aims to tackle the topic of discretionary vs nondiscretionary characteristic of fiscal stabilisers (SF). In this context, the scope of the research undertaking is to launch a scientific debate over the definitions of the concepts of non-automatic fiscal stabilisers (SfnA) and SFAs. We describe how we can quantify the discretionary and non-discretionary character of the fiscal policy, by the analysis of the structure of the conventional budget balance (SBc), budget balance associated with the current GDP. In the final part of this article, we propose a quantitative equilibrium model for establishing the mathematical prerequisites for an SF to become automatic. Likewise, on the basis of the proposed mathematical model we have performed a qualitative analysis of the influence factors.
The Cost of Delivery Delays
Authors: Maria Jose Carreras-Valle, Alessandro Ferrari
Date: 2025-01-15 00:00:00

Since 2018, there has been a consistent decline in the distance traveled by U.S. manufacturing imports, reaching a level not observed since 2008. This trend is the result of the substitution away from imports from China and towards imports from closer countries. At the same time, U.S. manufacturing inventory-to-sales ratio has continued to rise. These trends are at odds with the literature, which finds that reductions in the distance of imports are associated with a decline in inventories. We argue that a rise in delivery time risk, driven by longer and more frequent delays and supply disruptions, can reconcile these trends. We do so in the context of a model of global sourcing with stochastic delivery times and inventories. Firms trade off the lower price of farther inputs with the increase in exposure to demand volatility and longer delays. In response, firms increase their inventories. Yet, as delivery delays rise, firms need to carry more inventories per unit of the input used. We calibrate the model for the period from 2018 to 2024 using data on the increase in tariffs for inputs from China, and the rise in inventories over sales. We find an increase in delivery delays for foreign inputs of 21 days across the period. The rise in delays and tariffs had an output loss of 7.3% and a price increase of 1.8%. Of these, the rise in delivery delays alone generated a 2.6% drop in output and a 0.4% increase in prices.
Empirical Study on the Factors Influencing Stock Market Volatility in China
Authors: Jingchu Zhang
Date: 2025-01-15 00:00:00
Tasks: Descriptive

This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China's Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model
The Response of Farmer Welfares Amidst Food Prices Shock and Inflation in the Province of East Java
Authors: Moh. Hairus Zaman, Diah Wahyuningsih, Ris Yuwono Yudo Nugroho
Date: 2025-01-15 00:00:00
Tasks: Time Series

Price uncertainty in food commodities can create uncertainty for farmers and potentially negatively impact the level of farmer household well-being. On the other hand, the agriculture sector in the province of East Java has greatly contributed to East Java's economy. This paper analyses the response of farmer welfare through farmer exchange values amidst fluctuation shock of food needed prices and inflation level in the east java province. The research method of this paper employs the impulse response function of the Bayesian Vector Autoregressive (BVAR) model by using time series secondary data from May 2017 until December 2023. This paper finds that the shock that happens to aggregate food prices can increase farmer exchange values even though the shock to the inflation level has reduced farmer exchange values and increased aggregate food prices.
What is the general Welfare? Welfare Economic Perspectives
Authors: Charles F. Manski
Date: 2025-01-14 00:00:00

Researchers do not know what the framers of the United States Constitution intended when they wrote of the general Welfare. Nevertheless, economists can conjecture by specifying social welfare functions that aim to express the preferences of the population. Economists have often simplified utilitarian analysis of public policy by assuming that individuals have homogeneous, consequentialist, and self-centered preferences. In reality, individuals may hold heterogeneous private and social preferences. To enhance policy analysis, I argue that economists should specify social welfare functions that express the richness and variety of actual personal preferences over social states. The possibilities are vast. I focus on preferences for population prosperity and equity. There has been much controversy regarding interpretation of equity, a term that public discourse has used in vague and conflicting ways. Specifying social welfare functions that formally express different interpretations of equity may not eliminate disagreements, but it should clarify concepts of equity and reduce the inconsistencies that afflict verbal communication.