pwc-archive/papers-with-abstracts huggingface dataset:
On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units
Authors: Luis Alvarez, Bruno Ferman
Date: 2025-06-17 00:00:00
Tasks: Prediction Intervals, valid

We study how inference methods for settings with few treated units that rely on treatment effect homogeneity extend to alternative inferential targets when treatment effects are heterogeneous -- namely, tests of sharp null hypotheses, inference on realized treatment effects, and prediction intervals. We show that inference methods for these alternative targets are deeply interconnected: they are either equivalent or become equivalent under additional assumptions. Our results show that methods designed under treatment effect homogeneity can remain valid for these alternative targets when treatment effects are stochastic, offering new theoretical justifications and insights on their applicability.
Heterogeneous economic growth vulnerability across Euro Area countries under stressed scenarios
Authors: Claudio Lissona, Esther Ruiz
Date: 2025-06-17 00:00:00

We analyse economic growth vulnerability of the four largest Euro Area (EA) countries under stressed macroeconomic and financial conditions. Vulnerability, measured as a lower quantile of the growth distribution conditional on EA-wide and country-specific underlying factors, is found to be higher in Germany, which is more exposed to EA-wide economic conditions, and in Spain, which has large country-specific sectoral dynamics. We show that, under stress, financial factors amplify adverse macroeconomic conditions. Furthermore, even severe sectoral (financial or macro) shocks, whether common or country-specific, fail to fully explain the vulnerability observed under overall stress. Our results underscore the importance of monitoring both local and EA-wide macro-financial conditions to design effective policies for mitigating growth vulnerability.
A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
Authors: John Buckell, Alice Wreford, Matthew Quaife, Thomas O. Hancock
Date: 2025-06-17 00:00:00
Tasks: Discrete Choice Models

Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. A raft of parametric model specifications for these models are available. We test a range of alternatives assumptions, and model averaging, to test if or how model outputs are impacted. Design: Scoping review of current modelling practices. Seven alternative distributions, and model averaging over all distributional assumptions, were compared on four datasets: two were stated preference, one was revealed preference, and one was simulated. Analyses examined model fit, preference distributions, willingness-to-pay, and forecasting. Results: Almost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness-to-pay varied significantly across specifications, and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowed for greater flexibility, further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions impacted predictions from models. Limitations: Our focus was on mixed logit models since these models are the most common in health, though latent class models are also used. Conclusions: The standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Implications: Researchers should test alternative assumptions to normal distributions in their models.
Machine Learning-Based Estimation of Monthly GDP
Authors: Yonggeun Jung
Date: 2025-06-17 00:00:00

This paper proposes a scalable framework to estimate monthly GDP using machine learning methods. We apply Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Extreme Gradient Boosting (XGBoost), and Elastic Net regression to map monthly indicators to quarterly GDP growth, and reconcile the outputs with actual aggregates. Using data from China, Germany, the UK, and the US, our method delivers robust performance across varied data environments. Benchmark comparisons with prior US studies and UK official statistics validate its accuracy. The approach offers a flexible and data-driven tool for high-frequency macroeconomic monitoring and policy analysis.
High-Dimensional Spatial-Plus-Vertical Price Relationships and Price Transmission: A Machine Learning Approach
Authors: Mindy L. Mallory, Rundong Peng, Meilin Ma, H. Holly Wang
Date: 2025-06-16 00:00:00

Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the "curse of dimensionality" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time.
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Authors: Michael Balzer
Date: 2025-06-16 00:00:00
Tasks: Model Selection, regression, Variable Selection

Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is feasible even in high-dimensional settings where established quasi-maximum likelihood or generalized method of moments estimators do not yield unique solutions. The approach additionally enables data-driven variable and model selection in low- as well as high-dimensional settings. Since the bias-variance trade-off is also controlled in the algorithm, implicit regularization is imposed which improves prediction accuracy on out-of-sample spatial data. Detailed simulation studies regarding the performance of estimation, prediction and variable selection in low- and high-dimensional settings confirm proper functionality of the proposed methodology. To illustrative the functionality of the model-based gradient boosting algorithm, a case study is presented where the life expectancy in German districts is modeled incorporating a potential spatial dependence structure.
Production Function Estimation without Invertibility: Imperfectly Competitive Environments and Demand Shocks
Authors: Ulrich Doraszelski, Lixiong Li
Date: 2025-06-16 00:00:00

We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. Furthermore, we show how a modification of the procedure ensures Neyman orthogonality, enhancing efficiency and robustness by rendering the asymptotic distribution of the GMM estimator in the second step of the estimation procedure invariant to estimation noise from the first step.
Quantile Peer Effect Models
Authors: Aristide Houndetoungan
Date: 2025-06-15 00:00:00

I propose a flexible structural model to estimate peer effects across various quantiles of the peer outcome distribution. The model allows peers with low, intermediate, and high outcomes to exert distinct influences, thereby capturing more nuanced patterns of peer effects than standard approaches that are based on aggregate measures. I establish the existence and uniqueness of the Nash equilibrium and demonstrate that the model parameters can be estimated using a straightforward instrumental variable strategy. Applying the model to a range of outcomes that are commonly studied in the literature, I uncover diverse and rich patterns of peer influences that challenge assumptions inherent in standard models. These findings carry important policy implications: key player status in a network depends not only on network structure, but also on the distribution of outcomes within the population.
Dynamic allocation: extremes, tail dependence, and regime Shifts
Authors: Yin Luo, Sheng Wang, Javed Jussa
Date: 2025-06-14 00:00:00

By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity factor performance, which can help equity investors to build better factor models and asset allocation managers to construct more efficient risk premia portfolios.
Moment Restrictions for Nonlinear Panel Data Models with Feedback
Authors: Stéphane Bonhomme, Kevin Dano, Bryan S. Graham
Date: 2025-06-14 00:00:00

Many panel data methods, while allowing for general dependence between covariates and time-invariant agent-specific heterogeneity, place strong a priori restrictions on feedback: how past outcomes, covariates, and heterogeneity map into future covariate levels. Ruling out feedback entirely, as often occurs in practice, is unattractive in many dynamic economic settings. We provide a general characterization of all feedback and heterogeneity robust (FHR) moment conditions for nonlinear panel data models and present constructive methods to derive feasible moment-based estimators for specific models. We also use our moment characterization to compute semiparametric efficiency bounds, allowing for a quantification of the information loss associated with accommodating feedback, as well as providing insight into how to construct estimators with good efficiency properties in practice. Our results apply both to the finite dimensional parameter indexing the parametric part of the model as well as to estimands that involve averages over the distribution of unobserved heterogeneity. We illustrate our methods by providing a complete characterization of all FHR moment functions in the multi-spell mixed proportional hazards model. We compute efficient moment functions for both model parameters and average effects in this setting.
Optimal treatment assignment rules under capacity constraints
Authors: Keita Sunada, Kohei Izumi
Date: 2025-06-13 00:00:00

We study treatment assignment problems under capacity constraints, where a planner aims to maximize social welfare by assigning treatments based on observable covariates. Such constraints are common in practice, as treatments are often costly or limited in supply. However, they introduce nontrivial challenges for deriving optimal statistical assignment rules because the planner needs to coordinate treatment assignment probabilities across the entire covariate distribution. To address these challenges, we reformulate the planner's constrained maximization problem as an optimal transport problem, which makes the problem effectively unconstrained. We then establish locally asymptotic optimality results of assignment rules using a limits of experiments framework. Finally, we illustrate our method with a voucher assignment problem for private secondary school attendance using data from Angrist et al. (2006).
Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies
Authors: Fabian Muny
Date: 2025-06-13 00:00:00

Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit.
Structural Characterizations of Marginal Policy Effects
Authors: Zhixin Wang, Yu Zhang, Zhengyu Zhang
Date: 2025-06-13 00:00:00

This paper investigates the structural interpretation of the marginal policy effect (MPE) within nonseparable models. We demonstrate that, for a smooth functional of the outcome distribution, the MPE equals its functional derivative evaluated at the outcome-conditioned weighted average structural derivative. This equivalence is definitional rather than identification-based. Building on this theoretical result, we propose an alternative identification strategy for the MPE that complements existing methods.
Identification and Inference of Partial Effects in Sharp Regression Kink Designs
Authors: Zhixin Wang, Zhengyu Zhang
Date: 2025-06-13 00:00:00

The partial effect refers to the impact of a change in a target variable D on the distribution of an outcome variable Y . This study examines the identification and inference of a wide range of partial effects at the threshold in the sharp regression kink (RK) design under general policy interventions. We establish a unifying framework for conducting inference on the effect of an infinitesimal change in D on smooth functionals of the distribution of Y, particularly when D is endogenous and instrumental variables are unavailable. This framework yields a general formula that clarifies the causal interpretation of numerous existing sharp RK estimands in the literature. We develop the relevant asymptotic theory, introduce a multiplier bootstrap procedure for inference, and provide practical implementation guidelines. Applying our method to the effect of unemployment insurance (UI) benefits on unemployment duration, we find that while higher benefits lead to longer durations, they also tend to reduce their dispersion. Furthermore, our results show that the magnitude of the partial effect can change substantially depending on the specific form of the policy intervention.
Suspense and Surprise in European Football
Authors: Raphael Flepp, Tim Pawlowski, Travis Richardson
Date: 2025-06-26 00:00:00

We propose utilizing match-level suspense and surprise - which capture the entertainment utility created by competitive balance and outcome uncertainty for sports spectators - as alternative policy targets for league organizers and managers. Through simulations, we derive a benchmark range for suspense and surprise based on a perfectly balanced match before analyzing over 25,000 men's matches (2010/11-2023/24) and 725 women's matches (2023/24) from Europe's top football leagues. Our findings reveal that an average match generates lower suspense compared to the benchmark range, particularly for top teams, while surprise values consistently align with the benchmark. Moreover, we observe nuanced trends over time in men's football and highlight notable differences across leagues and clubs in both men's and women's competitions. These insights enhance our understanding of how the attractiveness of matches arises from competitive balance and carry important policy implications.
Exiting National Anti-Poverty Campaign, Social Support, and Improved Mental Health
Authors: Zhengwen Liu, Castiel Chen Zhuang, Yibo Wu
Date: 2025-06-25 00:00:00

We study the psychological and social impacts of exiting a national anti-poverty campaign, leveraging China's phase-out of its national poverty assistance as a natural experiment. Using a regression discontinuity design, we find that exiting the national campaign improves mental wellbeing. These improvements are accompanied by stronger social and family ties -- such as greater perceived support and communication, while income and material conditions remain largely unchanged. Our findings offer insights into the design of policy exits and underscore the importance of incorporating measures that sustain community- and family-based support systems when implementing or ending assistance programs.
Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management
Authors: Arega Getaneh Abate, Xiaobing Zhang, Xiufeng Liu, Dogan Keles
Date: 2025-06-25 00:00:00

Integrating electric mobility (electric vehicles (EVs), electric trucks (ETs)) and renewable energy sources (RES) with the power grid is paramount for achieving decarbonization, efficiency, and stability. Given the rapid growth of decentralized technologies and their critical role in decarbonization, two critical challenges emerge: first, the development of a digital platform for operational coordination; and second, rigorous research into their cost-benefit profile. This paper addresses this by presenting a comprehensive cost-benefit analysis (CBA) of an AI-driven operational digital platform (ODP) designed for holistic, cross-sectoral optimization. The ODP aims to enhance energy efficiency, grid reliability, and environmental sustainability. A seven-step CBA framework, aligned with EU guidelines, quantifies economic, reliability, and environmental benefits against capital and operational expenditures, explicitly linking benefit magnitude to AI-driven ODP and optimization efficiencies, such as quantified improvements in market arbitrage from ODP, enabled forecasting, and enhanced operational efficiencies across various services.
International Trade and Intellectual Property
Authors: Gaetan de Rassenfosse
Date: 2025-06-21 00:00:00
Tasks: Causal Inference

Intellectual property (IP) rules have the potential to shape cross-border trade far more than their legalistic origins might suggest. Drawing on three decades of evidence, this review shows that stronger IP rights simultaneously create market-power forces that raise prices and market-expansion forces that broaden demand, while dynamic incentives spur quality upgrading and new export varieties. Micro-data and quasi-natural experiments after TRIPS reveal that IP most often boosts trade along the extensive margin and redirects some activity toward licensing and foreign investment. Policy bundling and measurement gaps on the strength of IP rights still cloud causal inference. Future work must map intangible flows and enforcement quality to capture the digital, data-driven frontier of international commerce.
An experiment in price perception error
Authors: Shawn Berry
Date: 2025-06-24 00:00:00
Tasks: Decision Making

The process of consumer decision-making is multidimensional, and price perception is a very important but still not well-understood dimension for both marketers and consumers. Although heuristics or mental shortcuts are seen as biased and can cause decision errors, consumers tend to use price knowledge heuristics for purchase decisions, sometimes relying on old information. This study examined the effects of individual attitudinal and demographic factors on the ability of 351 respondents to correctly guess the prices of 13 products and services using ANOVA and the development of a seven-factor price perception model using latent variable analysis. While most respondents preferred to either research prices first or compare prices to a similar product, they either systematically underestimated or overestimated the prices of the products. In the ANOVA, brand loyalty, importance of substitute products and knockoff products, how financially well-off the respondent household was growing up, product quality importance, haggling tendency, and level of income were statistically significant predictors of price perception error. Latent variable analysis revealed that demographics, decision making, and price sensitivity factors had the greatest influence on price perception error. Level of education and income, frequency of regret, coupon importance, and how respondents chose to save or spend money were significant latent variables.
The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland
Authors: Agata Galkiewicz
Date: 2025-06-24 00:00:00

Random disturbances such as air pollution may affect cognitive performance, which, particularly in high-stakes settings, may have severe consequences for an individual's productivity and well-being. This paper examines the short-term effects of air pollution on school leaving exam results in Poland. I exploit random variation in air pollution between the days on which exams are held across three consecutive school years. I aim to capture this random variation by including school and time fixed effects. The school-level panel data is drawn from a governmental program where air pollution is continuously measured in the schoolyard. This localized hourly air pollution measure is a unique feature of my study, which increases the precision of the estimated effects. In addition, using distant and aggregated air pollution measures allows me for the comparison of the estimates in space and time. The findings suggest that a one standard deviation increase in the concentration of particulate matter PM2.5 and PM10 decreases students' exam scores by around 0.07-0.08 standard deviations. The magnitude and significance of these results depend on the location and timing of the air pollution readings, indicating the importance of the localized air pollution measure and the distinction between contemporaneous and lingering effects. Further, air pollution effects gradually increase in line with the quantiles of the exam score distribution, suggesting that high-ability students are more affected by the random disturbances caused by air pollution.