pwc-archive/papers-with-abstracts huggingface dataset:
A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data
Authors: Zhentao Shi, Jin Xi, Haitian Xie
Date: 2025-05-28 00:00:00
Tasks: Causal Inference, counterfactual

This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic control problem. To address this issue, we propose a synthetic business cycle framework that explicitly separates trend and cyclical components. By leveraging the treated unit's historical data to forecast its trend and using control units only for cyclical fluctuations, our divide-and-conquer strategy eliminates spurious correlations and improves the robustness of counterfactual prediction in macroeconomic applications. As empirical illustrations, we examine the cases of German reunification and the handover of Hong Kong, demonstrating the advantages of the proposed approach.
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
Authors: Jiawei Fu, Donald P. Green
Date: 2025-05-28 00:00:00
Tasks: Causal Inference, Experimental Design, regression

How should researchers analyze randomized experiments in which the main outcome is measured in multiple ways but each measure contains some degree of error? We describe modeling approaches that enable researchers to identify causal parameters of interest, suggest ways that experimental designs can be augmented so as to make linear latent variable models more credible, and discuss empirical tests of key modeling assumptions. We show that when experimental researchers invest appropriately in multiple outcome measures, an optimally weighted index of the outcome measures enables researchers to obtain efficient and interpretable estimates of causal parameters by applying standard regression methods, and that weights may be obtained using instrumental variables regression. Maximum likelihood and generalized method of moments estimators can be used to obtain estimates and standard errors in a single step. An empirical application illustrates the gains in precision and robustness that multiple outcome measures can provide.
Conditional Method Confidence Set
Authors: Lukas Bauer, Ekaterina Kazak
Date: 2025-05-27 00:00:00

This paper proposes a Conditional Method Confidence Set (CMCS) which allows to select the best subset of forecasting methods with equal predictive ability conditional on a specific economic regime. The test resembles the Model Confidence Set by Hansen et al. (2011) and is adapted for conditional forecast evaluation. We show the asymptotic validity of the proposed test and illustrate its properties in a simulation study. The proposed testing procedure is particularly suitable for stress-testing of financial risk models required by the regulators. We showcase the empirical relevance of the CMCS using the stress-testing scenario of Expected Shortfall. The empirical evidence suggests that the proposed CMCS procedure can be used as a robust tool for forecast evaluation of market risk models for different economic regimes.
Nonparametric "rich covariates" without saturation
Authors: Ludgero Glorias, Federico Martellosio, J. M. C. Santos Silva
Date: 2025-05-27 00:00:00

We consider two nonparametric approaches to ensure that instrumental variables estimators of a linear equation satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned and the model is not saturated. Both approaches start with a nonparametric estimate of the expectation of the instrument conditional on the covariates, and ensure that the rich-covariates condition is satisfied either by using as the instrument the difference between the original instrument and its estimated conditional expectation, or by adding the estimated conditional expectation to the set of regressors. We derive asymptotic properties of our instrumental variables estimators, and assess their finite sample performance relative to existing approaches using Monte Carlo simulations.
Debiased Ill-Posed Regression
Authors: AmirEmad Ghassami, James M. Robins, Andrea Rotnitzky
Date: 2025-05-27 00:00:00
Tasks: Causal Inference, regression

In various statistical settings, the goal is to estimate a function which is restricted by the statistical model only through a conditional moment restriction. Prominent examples include the nonparametric instrumental variable framework for estimating the structural function of the outcome variable, and the proximal causal inference framework for estimating the bridge functions. A common strategy in the literature is to find the minimizer of the projected mean squared error. However, this approach can be sensitive to misspecification or slow convergence rate of the estimators of the involved nuisance components. In this work, we propose a debiased estimation strategy based on the influence function of a modification of the projected error and demonstrate its finite-sample convergence rate. Our proposed estimator possesses a second-order bias with respect to the involved nuisance functions and a desirable robustness property with respect to the misspecification of one of the nuisance functions. The proposed estimator involves a hyper-parameter, for which the optimal value depends on potentially unknown features of the underlying data-generating process. Hence, we further propose a hyper-parameter selection approach based on cross-validation and derive an error bound for the resulting estimator. This analysis highlights the potential rate loss due to hyper-parameter selection and underscore the importance and advantages of incorporating debiasing in this setting. We also study the application of our approach to the estimation of regular parameters in a specific parameter class, which are linear functionals of the solutions to the conditional moment restrictions and provide sufficient conditions for achieving root-n consistency using our debiased estimator.
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
Authors: Guanhao Zhou, Yuefeng Han, Xiufan Yu
Date: 2025-05-26 00:00:00
Tasks: counterfactual, Imputation, Matrix Completion

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful neural network architectures to cohesively deal with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. The proposed CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-output autoencoder) to form a heterogeneous causal panel model. The nonlinear covariate component offers a flexible framework for capturing the complex influences of covariates on outcomes. The nonlinear factor analysis enables CoDEAL to effectively capture both cross-sectional and temporal dependencies inherent in the data panel. This latent structural information is subsequently integrated into a customized matrix completion algorithm, thereby facilitating more accurate imputation of missing counterfactual outcomes. Moreover, the use of a multi-output autoencoder explicitly accounts for heterogeneity across units and enhances the model interpretability of the latent factors. We establish theoretical guarantees on the convergence of the estimated counterfactuals, and demonstrate the compelling performance of the proposed method using extensive simulation studies and a real data application.
Intraday Functional PCA Forecasting of Cryptocurrency Returns
Authors: Joann Jasiak, Cheng Zhong
Date: 2025-05-26 00:00:00
Tasks: Time Series Analysis

We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we introduce a new algorithm based on the FPCA applied by rolling, which can be used for any data observed continuously 24/7. The proposed FPCA forecasting methods are applied to return functions computed from data sampled hourly and at 15-minute intervals. Next, the functional forecasts evaluated at discrete points in time are compared with the forecasts based on other methods, including machine learning and a traditional ARMA model. The proposed FPCA-based methods perform well in terms of forecast accuracy and outperform competitors in terms of directional (sign) of return forecasts at fixed points in time.
Large structural VARs with multiple linear shock and impact inequality restrictions
Authors: Lukas Berend, Jan Prüser
Date: 2025-05-25 00:00:00

We propose a high-dimensional structural vector autoregression framework capable of accommodating a large number of linear inequality restrictions on impact impulse responses, structural shocks, and their element-wise products. Combining impact- and shock-inequality restrictions can be flexibly used to sharpen inference and to disentangle structurally interpretable shocks through sign and shock constraints. To estimate the model, we develop a highly efficient sampling algorithm that scales well with model dimension and the number of inequality restrictions on impact responses, as well as structural shocks. It remains computationally feasible even when existing algorithms may break down. To demonstrate the practical utility of our approach, we identify five structural shocks and examine the dynamic responses of thirty macroeconomic variables, highlighting the model's flexibility and feasibility in complex empirical settings. We provide empirical evidence that financial shocks are the most important driver of the dynamics of the business cycle.
Comparative analysis of financial data differentiation techniques using LSTM neural network
Authors: Dominik Stempień, Janusz Gajda
Date: 2025-05-25 00:00:00
Tasks: Time Series Forecasting

We compare traditional approach of computing logarithmic returns with the fractional differencing method and its tempered extension as methods of data preparation before their usage in advanced machine learning models. Differencing parameters are estimated using multiple techniques. The empirical investigation is conducted on data from four major stock indices covering the most recent 10-year period. The set of explanatory variables is additionally extended with technical indicators. The effectiveness of the differencing methods is evaluated using both forecast error metrics and risk-adjusted return trading performance metrics. The findings suggest that fractional differentiation methods provide a suitable data transformation technique, improving the predictive model forecasting performance. Furthermore, the generated predictions appeared to be effective in constructing profitable trading strategies for both individual assets and a portfolio of stock indices. These results underline the importance of appropriate data transformation techniques in financial time series forecasting, supporting the application of memory-preserving techniques.
Model-based Estimation of Difference-in-Differences with Staggered Treatments
Authors: Siddhartha Chib, Kenichi Shimizu
Date: 2025-05-23 00:00:00
Tasks: Uncertainty Quantification

We propose a model-based framework for estimating treatment effects in Difference-in-Differences (DiD) designs with multiple time-periods and variation in treatment timing. We first present a simple model for potential outcomes that respects the identifying conditions for the average treatment effects on the treated (ATT's). The model-based perspective is particularly valuable in applications with small sample sizes, where existing estimators that rely on asymptotic arguments may yield poor approximations to the sampling distribution of group-time ATT's. To improve parsimony and guide prior elicitation, we reparametrize the model in a way that reduces the effective number of parameters. Prior information about treatment effects is incorporated through black-box training sample priors and, in small-sample settings, by thick-tailed t-priors that shrink ATT's of small magnitudes toward zero. We provide a straightforward and computationally efficient Bayesian estimation procedure and establish a Bernstein-von Mises-type result that justifies posterior inference for the treatment effects. Simulation studies confirm that our method performs well in both large and small samples, offering credible uncertainty quantification even in settings that challenge standard estimators. We illustrate the practical value of the method through an empirical application that examines the effect of minimum wage increases on teen employment in the United States.
Bayesian Deep Learning for Discrete Choice
Authors: Daniel F. Villarraga, Ricardo A. Daziano
Date: 2025-05-23 00:00:00
Tasks: Bayesian Inference, Deep Learning, Discrete Choice Models, Econometrics, Uncertainty Quantification

Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences. DCMs play a central role in applied econometrics by enabling inference on key economic variables, such as marginal rates of substitution, rather than focusing solely on predicting choices on new unlabeled data. However, while traditional DCMs offer high interpretability and support for point and interval estimation of economic quantities, these models often underperform in predictive tasks compared to deep learning (DL) models. Despite their predictive advantages, DL models remain largely underutilized in discrete choice due to concerns about their lack of interpretability, unstable parameter estimates, and the absence of established methods for uncertainty quantification. Here, we introduce a deep learning model architecture specifically designed to integrate with approximate Bayesian inference methods, such as Stochastic Gradient Langevin Dynamics (SGLD). Our proposed model collapses to behaviorally informed hypotheses when data is limited, mitigating overfitting and instability in underspecified settings while retaining the flexibility to capture complex nonlinear relationships when sufficient data is available. We demonstrate our approach using SGLD through a Monte Carlo simulation study, evaluating both predictive metrics--such as out-of-sample balanced accuracy--and inferential metrics--such as empirical coverage for marginal rates of substitution interval estimates. Additionally, we present results from two empirical case studies: one using revealed mode choice data in NYC, and the other based on the widely used Swiss train choice stated preference data.
Beyond Leaders and Laggards: A Typology of Renewable Energy Adoption Trajectories with Evidence from Off-Grid Communities
Authors: Roni Blushtein-Livnon, Tal Svoray, Itai Ficshhendler, Havatzelet Yanel, Emir Galilee, Michael Dorman
Date: 2025-05-28 00:00:00

Understanding the dynamics of renewable energy adoption is essential for designing strategies that accelerate its spread - an urgent priority for advancing climate goals and improving well-being, especially in off-grid regions facing energy poverty. This study introduces a time-series-based analytical framework that quantifies and classifies adoption behaviors of geographic entities within a region. A novel metric, the Adoption over Time Index (ATI), captures cumulative adoption intensity and identifies shifts in adoption trends, improving the ability to distinguish between fundamental adoption paths. By combining ATI with three key features found to be indicative of adoption dynamics, we define a typology of eight distinct paths, including two newly identified trajectories - the decelerating path and the declining moderate path. Applying this framework to a case study of an off-grid Bedouin population in southern Israel reveals that these retreating paths exist in substantial proportions. Identifying such trends is critical for addressing stagnation and preventing backsliding in the diffusion process. The leaping path, by contrast, was nearly absent. We also identify behavioral diversity within both front-runner and trailing groups. Differentiating among these groups can help tailor acceleration strategies. The analysis further reveals significant disparities in adoption levels across the region, with lagging clusters being widespread and overall adoption falling short of the region's potential.
Borrowing on Belief? Consumer Confidence and U.S. Credit -- A VECM Study
Authors: Samiha Tariq, Weikang Zhang
Date: 2025-05-27 00:00:00

This study explores the interdependent relationship between consumer credit and consumer confidence in the United States using monthly data from January 1978 to August 2024. Utilizing a Vector Error Correction Model (VECM), the analysis focuses on the interplay between household borrowing behaviour and consumer sentiment while controlling for macroeconomic factors such as interest rates, inflation, unemployment, and money supply. The results reveal a stable long-run equilibrium: heightened consumer confidence is associated with increased credit utilization, reflecting greater financial optimism among households. In the short run, shifts in consumer confidence exert relatively modest immediate influence on credit usage, whereas consumer credit adjusts slowly, displaying significant inertia. Impulse-response analysis confirms that shocks to consumer confidence generate sustained positive effects on borrowing, while unexpected increases in credit initially depress sentiment but only fleetingly. These findings underscore the critical role of the relationship between consumer confidence and credit-market dynamics and highlight its policy relevance for fostering balanced and stable household finances.
When Experimental Economics Meets Large Language Models: Tactics with Evidence
Authors: Shu Wang, Zijun Yao, Shuhuai Zhang, Jianuo Gai, Tracy Xiao Liu, Songfa Zhong
Date: 2025-05-27 00:00:00

Advancements in large language models (LLMs) have sparked a growing interest in measuring and understanding their behavior through experimental economics. However, there is still a lack of established guidelines for designing economic experiments for LLMs. By combining principles from experimental economics with insights from LLM research in artificial intelligence, we outline and discuss eight practical tactics for conducting experiments with LLMs. We further perform two sets of experiments to demonstrate the significance of these tactics. Our study enhances the design, replicability, and generalizability of LLM experiments, and broadens the scope of experimental economics in the digital age.
The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics
Authors: Rafael Acuna, Aldie Alejandro, Robert Leung
Date: 2025-05-27 00:00:00

Dynasties have long dominated Philippine politics. Despite the theoretical consensus that dynastic rule erodes democratic accountability, there is limited empirical evidence establishing dynasties' true impact on development. A key challenge has been developing robust metrics for characterizing dynasties that facilitate meaningful comparisons across geographies and election cycles. Using election data from 2004 to 2022, we leverage methods from graph theory to develop four indicators to investigate dynastic evolution: Political Herfindahl-Hirschman Index (HHI), measuring dynastic power concentration; Centrality Gini Coefficient (CGC), reflecting inequalities of influence between clan members; Connected Component Density (CCD), representing the degree of inter-clan connection; and Average Community Connectivity (ACC), quantifying intra-clan cohesion. Our analysis reveals three key findings. Firstly, dynasties have grown stronger and more interconnected, occupying an increasing share of elected positions. Dominant clans have also remained tightly knit, but with great power imbalances between members. Secondly, we examine variations in party-hopping between dynastic and non-dynastic candidates. Across every election cycle, party-hopping rates are significantly higher (p<0.01) among dynastic candidates than non-dynasts, suggesting that the dominance of dynasties may weaken institutional trust within parties. Finally, applying a Linear Mixed Model regression, controlling for geographic random-effects and time fixed-effects, we observe that provinces with high power asymmetries within clans (high CGCs) and with deeply interconnected clans (high CCDs) record significantly lower (p<0.05) Human Development Index scores. These findings suggest that clan structure, rather than power concentration alone--may be the chief determinant of a ruling dynasty's developmental impact.
Do Betting Markets Sense a Goal Coming? Evidence from the German Bundesliga
Authors: David Winkelmann, Christian Deutscher
Date: 2025-05-27 00:00:00
Tasks: State Space Models

We use the fertile ground of betting markets to study the anticipation of major news in financial markets. While there is a considerable body of literature on the accuracy and efficiency of betting markets after important in-match events, there are no studies dealing with the anticipation of such events. This paper tracks bookmaker odds and betting stakes to provide insights into the movement of both prior to goals. Utilising high-resolution (1 Hz) data from a leading European bookmaker for a full season of the top German football league, we analyse whether market participants anticipate major news. In particular, we consider the case of the first goal scored within a match, with its strong impact on the match outcome. Using regression models and state-space models (SSMs) accounting for an underlying market activity level, we investigate whether the bookmaker adjusts odds and bettors tend to place higher stakes on the scoring team right before the first goal is scored. Our results indicate that neither side of the market anticipates goals by significantly adjusting their behaviour.