pwc-archive/papers-with-abstracts huggingface dataset:
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
Authors: Hevish Cowlessur, Tansu Alpcan, Chandra Thapa, Seyit Camtepe, Neel Kanth Kundu
Date: 2025-06-24 00:00:00
Tasks: Dimensionality Reduction, Quantum Machine Learning

Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as quantum autoencoders can be constrained by current hardware capabilities and may exhibit vulnerabilities to reconstruction attacks due to their invertibility. We propose Quantum Principal Geodesic Analysis (qPGA), a novel, non-invertible method for dimensionality reduction and qubit-efficient encoding. Executed classically, qPGA leverages Riemannian geometry to project data onto the unit Hilbert sphere, generating outputs inherently suitable for quantum amplitude encoding. This technique preserves the neighborhood structure of high-dimensional datasets within a compact latent space, significantly reducing qubit requirements for amplitude encoding. We derive theoretical bounds quantifying qubit requirements for effective encoding onto noisy systems. Empirical results on MNIST, Fashion-MNIST, and CIFAR-10 show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders. Additionally, we demonstrate that qPGA enhances resistance to reconstruction attacks due to its non-invertible nature. In downstream QML classification tasks, qPGA can achieve over 99% accuracy and F1-score on MNIST and Fashion-MNIST, outperforming quantum-dependent baselines. Initial tests on real hardware and noisy simulators confirm its potential for noise-resilient performance, offering a scalable solution for advancing QML applications.
Continuous-variable Quantum Diffusion Model for State Generation and Restoration
Authors: Haitao Huang, Chuangtao Chen, Qinglin Zhao
Date: 2025-06-24 00:00:00
Tasks: Denoising

The generation and preservation of complex quantum states against environmental noise are paramount challenges in advancing continuous-variable (CV) quantum information processing. This paper introduces a novel framework based on continuous-variable quantum diffusion principles, synergizing them with CV quantum neural networks (CVQNNs) to address these dual challenges. For the task of state generation, our Continuous-Variable Quantum Diffusion Generative model (CVQD-G) employs a physically driven forward diffusion process using a thermal loss channel, which is then inverted by a learnable, parameter-efficient backward denoising process based on a CVQNN with time-embedding. This framework's capability is further extended for state recovery by the Continuous-Variable Quantum Diffusion Restoration model (CVQD-R), a specialized variant designed to restore quantum states, particularly coherent states with unknown parameters, from thermal degradation. Extensive numerical simulations validate these dual capabilities, demonstrating the high-fidelity generation of diverse Gaussian (coherent, squeezed) and non-Gaussian (Fock, cat) states, typically with fidelities exceeding 99%, and confirming the model's ability to robustly restore corrupted states. Furthermore, a comprehensive complexity analysis reveals favorable training and inference costs, highlighting the framework's efficiency, scalability, and its potential as a robust tool for quantum state engineering and noise mitigation in realistic CV quantum systems.
Multilevel Decomposition of Generalized Entropy Measures Using Constrained Bayes Estimation: An Application to Japanese Regional Data
Authors: Yuki Kawakubo, Kazuhiko Kakamu
Date: 2025-06-26 00:00:00

We propose a method for multilevel decomposition of generalized entropy (GE) measures that explicitly accounts for nested population structures such as national, regional, and subregional levels. Standard approaches that estimate GE separately at each level do not guarantee compatibility with multilevel decomposition. Our method constrains lower-level GE estimates to match higher-level benchmarks while preserving hierarchical relationships across layers. We apply the method to Japanese income data to estimate GE at the national, prefectural, and municipal levels, decomposing national inequality into between-prefecture and within-prefecture inequality, and further decomposing prefectural GE into between-municipality and within-municipality inequality.
Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach
Authors: Nektarios Aslanidis, Aurelio Bariviera, George Kapetanios, Vasilis Sarafidis
Date: 2025-06-26 00:00:00
Tasks: Variable Selection

This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates exposures to high-frequency idiosyncratic and market risk only, using asset-level IV regressions. The second stage identifies latent economy-wide factors by extracting the leading principal component from the model residuals and mapping it to lower-frequency macro-financial uncertainty and sentiment-based indicators via high-dimensional variable selection. Structured patterns of heterogeneity in exposures are uncovered using Mean Group estimators across asset categories. The method is applied to a broad sample of crypto assets, covering more than 80% of total market capitalization. We document short-term mean reversion and significant average exposures to idiosyncratic volatility and illiquidity. Green and DeFi assets are, on average, more exposed to market-level and economy-wide risk than their non-Green and non-DeFi counterparts. By contrast, stablecoins are less exposed to idiosyncratic, market-level, and economy-wide risk factors relative to non-stablecoins. At a conceptual level, our study develops a coherent framework for isolating distinct layers of risk in crypto markets. Empirically, it sheds light on how return sensitivities vary across digital asset categories -- insights that are important for both portfolio design and regulatory oversight.
Wild Bootstrap Inference for Linear Regressions with Many Covariates
Authors: Wenze Li
Date: 2025-06-26 00:00:00

We propose a simple modification to the wild bootstrap procedure and establish its asymptotic validity for linear regression models with many covariates and heteroskedastic errors. Monte Carlo simulations show that the modified wild bootstrap has excellent finite sample performance compared with alternative methods that are based on standard normal critical values, especially when the sample size is small and/or the number of controls is of the same order of magnitude as the sample size.
Analytic inference with two-way clustering
Authors: Laurent Davezies, Xavier D'Haultfœuille, Yannick Guyonvarch
Date: 2025-06-25 00:00:00
Tasks: Clustering

This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inference is invalid in non-Gaussian regimes, namely when the estimator of the parameter of interest is not asymptotically Gaussian. We consider a simple fix that addresses both issues. In Gaussian regimes, the corresponding tests are asymptotically exact and equivalent to usual ones. Otherwise, the new tests are asymptotically conservative. We also establish their uniform validity over a certain class of data generating processes. Independently of our tests, we highlight potential issues with multiple testing and nonlinear estimators under two-way clustering. Finally, we compare our approach with existing ones through simulations.
Daily Fluctuations in Weather and Economic Growth at the Subnational Level: Evidence from Thailand
Authors: Sarun Kamolthip
Date: 2025-06-25 00:00:00

This study investigates the historical relationship between temperature fluctuations and Thailand's aggregate economic output. The results show inverted-U shape temperature effects on the annual growth rate of provincial output per capita. The estimated marginal change in the annual growth rate of provincial output per capita of a 1 degree Celsius temperature increase is between -1.248 and -3.799 percentage points, depending on the functional form used. I find that the warming temperature may affect the growth of economic output rather than its level. The results also suggest that temperature changes affect output growth only in the agricultural sector, but not in the industrial and service sectors. Province-level projections under both the RCP4.5 and RCP8.5 emission scenarios show that climate change will affect half of the Thai population by 2050. Projections show that climate change will make 62% (RCP4.5) and 86% (RCP8.5) of the Thai population poorer in per-capita terms than they would be in the absence of climate change in 2090. Nonetheless, these projections are sensitive to potential biases in future climate projections, particularly in models that do not account for lagged effects. All projections should be interpreted with caution.
A Sharp and Robust Test for Selective Reporting
Authors: Stefan Faridani
Date: 2025-06-24 00:00:00
Tasks: valid

This paper proposes a test that is consistent against every detectable form of selective reporting and remains interpretable even when the t-scores are not exactly normal. The test statistic is the distance between the smoothed empirical t-curve and the set of all t-curves that would be possible in the absence of any selective reporting. This novel projection test can only be evaded in large meta-samples by selective reporting that also evades all other valid tests of restrictions on the t-curve. A second benefit of the projection test is that under the null we can interpret the projection residual as noise plus bias incurred from approximating the t-score's exact distribution with the normal. Applying the test to the Brodeur et al. (2020) meta-data, we find that the t-curves for RCTs, IVs, and DIDs are more distorted than could arise by chance. But an Edgeworth Expansion reveals that these distortions are small enough to be plausibly explained by the only approximate normality of the individual t-scores. The detection of selective reporting in this meta-sample is therefore more fragile than previously known.
Single-Index Quantile Factor Model with Observed Characteristics
Authors: Ruofan Xu, Qingliang Fan
Date: 2025-06-24 00:00:00
Tasks: model

We propose a characteristics-augmented quantile factor (QCF) model, where unknown factor loading functions are linked to a large set of observed individual-level (e.g., bond- or stock-specific) covariates via a single-index projection. The single-index specification offers a parsimonious, interpretable, and statistically efficient way to nonparametrically characterize the time-varying loadings, while avoiding the curse of dimensionality in flexible nonparametric models. Using a three-step sieve estimation procedure, the QCF model demonstrates high in-sample and out-of-sample accuracy in simulations. We establish asymptotic properties for estimators of the latent factor, loading functions, and index parameters. In an empirical study, we analyze the dynamic distributional structure of U.S. corporate bond returns from 2003 to 2020. Our method outperforms the benchmark quantile Fama-French five-factor model and quantile latent factor model, particularly in the tails ($\tau=0.05, 0.95$). The model reveals state-dependent risk exposures driven by characteristics such as bond and equity volatility, coupon, and spread. Finally, we provide economic interpretations of the latent factors.
100-Day Analysis of USD/IDR Exchange Rate Dynamics Around the 2025 U.S. Presidential Inauguration
Authors: Sandy H. S. Herho, Siti N. Kaban, Cahya Nugraha
Date: 2025-06-23 00:00:00
Tasks: Management

Using a 100-day symmetric window around the January 2025 U.S. presidential inauguration, non-parametric statistical methods with bootstrap resampling (10,000 iterations) analyze distributional properties and anomalies. Results indicate a statistically significant 3.61\% Indonesian rupiah depreciation post-inauguration, with a large effect size (Cliff's Delta $= -0.9224$, CI: $[-0.9727, -0.8571]$). Central tendency shifted markedly, yet volatility remained stable (variance ratio $= 0.9061$, $p = 0.504$). Four significant anomalies exhibiting temporal clustering are detected. These findings provide quantitative evidence of political transition effects on emerging market currencies, highlighting implications for monetary policy and currency risk management.
The Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
Authors: Alper Deniz Karakas
Date: 2025-06-23 00:00:00
Tasks: regression

This study examines the long-term economic impact of the colonial Mita system in Peru, building on Melissa Dell's foundational work on the enduring effects of forced labor institutions. The Mita, imposed by the Spanish colonial authorities from 1573 to 1812, required indigenous communities within a designated boundary to supply labor to mines, primarily near Potosi. Dell's original regression discontinuity design (RDD) analysis, leveraging the Mita boundary to estimate the Mita's legacy on modern economic outcomes, indicates that regions subjected to the Mita exhibit lower household consumption levels and higher rates of child stunting. In this paper, I replicate Dell's results and extend this analysis. I apply Double Machine Learning (DML) methods--the Partially Linear Regression (PLR) model and the Interactive Regression Model (IRM)--to further investigate the Mita's effects. DML allows for the inclusion of high-dimensional covariates and enables more flexible, non-linear modeling of treatment effects, potentially capturing complex relationships that a polynomial-based approach may overlook. While the PLR model provides some additional flexibility, the IRM model allows for fully heterogeneous treatment effects, offering a nuanced perspective on the Mita's impact across regions and district characteristics. My findings suggest that the Mita's economic legacy is more substantial and spatially heterogeneous than originally estimated. The IRM results reveal that proximity to Potosi and other district-specific factors intensify the Mita's adverse impact, suggesting a deeper persistence of regional economic inequality. These findings underscore that machine learning addresses the realistic non-linearity present in complex, real-world systems. By modeling hypothetical counterfactuals more accurately, DML enhances my ability to estimate the true causal impact of historical interventions.
Poverty Targeting with Imperfect Information
Authors: Juan C. Yamin
Date: 2025-06-22 00:00:00

A key challenge for targeted antipoverty programs in developing countries is that policymakers must rely on estimated rather than observed income, which leads to substantial targeting errors. I propose a statistical decision framework in which a benevolent planner, subject to a budget constraint and equipped only with noisy income estimates, allocates cash transfers to the poorest individuals. In this setting, the commonly used plug-in rule, which allocates transfers based on point estimates, is inadmissible and uniformly dominated by a shrinkage-based alternative. Building on this result, I propose an empirical Bayes (EB) targeting rule. I show that the regret of the empirical Bayes rule converges at the same rate as that of the posterior mean estimator, despite applying a nonsmooth transformation to it. Simulations show that the EB rule delivers large improvements over the plug-in approach in an idealized setting and modest but consistent gains in a more realistic application.
Beyond utility: incorporating eye-tracking, skin conductance and heart rate data into cognitive and econometric travel behaviour models
Authors: Thomas O. Hancock, Stephane Hess, Charisma F. Choudhury
Date: 2025-06-22 00:00:00
Tasks: Decision Making

Choice models for large-scale applications have historically relied on economic theories (e.g. utility maximisation) that establish relationships between the choices of individuals, their characteristics, and the attributes of the alternatives. In a parallel stream, choice models in cognitive psychology have focused on modelling the decision-making process, but typically in controlled scenarios. Recent research developments have attempted to bridge the modelling paradigms, with choice models that are based on psychological foundations, such as decision field theory (DFT), outperforming traditional econometric choice models for travel mode and route choice behaviour. The use of physiological data, which can provide indications about the choice-making process and mental states, opens up the opportunity to further advance the models. In particular, the use of such data to enrich 'process' parameters within a cognitive theory-driven choice model has not yet been explored. This research gap is addressed by incorporating physiological data into both econometric and DFT models for understanding decision-making in two different contexts: stated-preference responses (static) of accomodation choice and gap-acceptance decisions within a driving simulator experiment (dynamic). Results from models for the static scenarios demonstrate that both models can improve substantially through the incorporation of eye-tracking information. Results from models for the dynamic scenarios suggest that stress measurement and eye-tracking data can be linked with process parameters in DFT, resulting in larger improvements in comparison to simpler methods for incorporating this data in either DFT or econometric models. The findings provide insights into the value added by physiological data as well as the performance of different candidate modelling frameworks for integrating such data.
An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models
Authors: Wenze Li
Date: 2025-06-22 00:00:00
Tasks: Causal Inference

Instrumental variable (IV) regression is recognized as one of the five core methods for causal inference, as identified by Angrist and Pischke (2008). This paper compares two leading approaches to inference under weak identification for just-identified IV models: the classical Anderson-Rubin (AR) procedure and the recently popular tF method proposed by Lee et al. (2022). Using replication data from the American Economic Review (AER) and Monte Carlo simulation experiments, we evaluate the two procedures in terms of statistical significance testing and confidence interval (CI) length. Empirically, we find that the AR procedure typically offers higher power and yields shorter CIs than the tF method. Nonetheless, as noted by Lee et al. (2022), tF has a theoretical advantage in terms of expected CI length. Our findings suggest that the two procedures may be viewed as complementary tools in empirical applications involving potentially weak instruments.
Efficient Difference-in-Differences and Event Study Estimators
Authors: Xiaohong Chen, Pedro H. C. Sant'Anna, Haitian Xie
Date: 2025-06-21 00:00:00

This paper investigates efficient Difference-in-Differences (DiD) and Event Study (ES) estimation using short panel data sets within the heterogeneous treatment effect framework, free from parametric functional form assumptions and allowing for variation in treatment timing. We provide an equivalent characterization of the DiD potential outcome model using sequential conditional moment restrictions on observables, which shows that the DiD identification assumptions typically imply nonparametric overidentification restrictions. We derive the semiparametric efficient influence function (EIF) in closed form for DiD and ES causal parameters under commonly imposed parallel trends assumptions. The EIF is automatically Neyman orthogonal and yields the smallest variance among all asymptotically normal, regular estimators of the DiD and ES parameters. Leveraging the EIF, we propose simple-to-compute efficient estimators. Our results highlight how to optimally explore different pre-treatment periods and comparison groups to obtain the tightest (asymptotic) confidence intervals, offering practical tools for improving inference in modern DiD and ES applications even in small samples. Calibrated simulations and an empirical application demonstrate substantial precision gains of our efficient estimators in finite samples.
Leave No One Undermined: Policy Targeting with Regret Aversion
Authors: Toru Kitagawa, Sokbae Lee, Chen Qiu
Date: 2025-06-19 00:00:00

While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice. We study the problem of policy targeting for a regret-averse planner when training data gives a rich set of observable characteristics while the assignment rules can only depend on its subset. Grounded in decision theory, our regret-averse criterion reflects a planner's concern about regret inequality across the population, which generally leads to a fractional optimal rule due to treatment effect heterogeneity beyond the average treatment effects conditional on the subset characteristics. We propose a debiased empirical risk minimization approach to learn the optimal rule from data. Viewing our debiased criterion as a weighted least squares problem, we establish new upper and lower bounds for the excess risk, indicating a convergence rate of 1/n and asymptotic efficiency in certain cases. We apply our approach to the National JTPA Study and the International Stroke Trial.
Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice
Authors: Charles Shaw
Date: 2025-06-15 00:00:00
Tasks: Causal Inference, Kolmogorov-Arnold Networks

The double/debiased machine learning (DML) framework has become a cornerstone of modern causal inference, allowing researchers to utilise flexible machine learning models for the estimation of nuisance functions without introducing first-order bias into the final parameter estimate. However, the choice of machine learning model for the nuisance functions is often treated as a minor implementation detail. In this paper, we argue that this choice can have a profound impact on the substantive conclusions of the analysis. We demonstrate this by presenting and comparing two distinct Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE) estimators. The first estimator leverages standard machine learning models like Random Forests for nuisance function estimation, while the second is a novel estimator employing Kolmogorov-Arnold Networks (KANs). We establish the asymptotic properties of these estimators and evaluate their performance through Monte Carlo simulations. An empirical application analysing the distributional effects of 401(k) participation on net financial assets reveals that the choice of machine learning model for nuisance functions can significantly alter substantive conclusions, with the KAN-based estimator suggesting more complex treatment effect heterogeneity. These findings underscore a critical "caveat emptor". The selection of nuisance function estimators is not a mere implementation detail. Instead, it is a pivotal choice that can profoundly impact research outcomes in causal inference.
Fast Learning of Optimal Policy Trees
Authors: James Cussens, Julia Hatamyar, Vishalie Shah, Noemi Kreif
Date: 2025-06-18 00:00:00

We develop and implement a version of the popular "policytree" method (Athey and Wager, 2021) using discrete optimisation techniques. We test the performance of our algorithm in finite samples and find an improvement in the runtime of optimal policy tree learning by a factor of nearly 50 compared to the original version. We provide an R package, "fastpolicytree", for public use.