|
on Econometrics |
| By: | Jinghao Sun; Eric J. Tchetgen Tchetgen |
| Abstract: | We introduce a novel extension of the influential changes-in-changes (CiC) framework [Athey and Imbens, 2006] to estimate the average treatment effect on the treated (ATT) and distributional causal estimands in panel data settings with unmeasured confounding. While CiC relaxes the parallel trends assumption inherent in difference-in-differences (DiD), existing approaches typically accommodate only a single scalar unobserved confounder and rely on monotonicity assumptions between the confounder and the outcome. Moreover, current formulations lack inference procedures and theoretical guarantees that accommodate continuous covariates. Motivated by the intricate nature of confounding in empirical applications and the need to incorporate continuous covariates in a principled manner, we make two key contributions in this technical report. First, we establish nonparametric identification under a novel set of assumptions that permit high-dimensional unmeasured confounders and non-monotonic relationships between confounders and outcomes. Second, we construct efficient estimators that are Neyman orthogonal to infinite-dimensional nuisance parameters, facilitating valid inference even in the presence of high-dimensional continuous or discrete covariates and flexible machine learning-based nuisance estimation. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07228 |
| By: | Antonio F. Galvao; Gabriel Montes-Rojas |
| Abstract: | This paper introduces a new framework for multivariate quantile regression based on the multivariate distribution function, termed multivariate quantile regression (MQR). In contrast to existing approaches--such as directional quantiles, vector quantile regression, or copula-based methods--MQR defines quantiles through the conditional probability structure of the joint conditional distribution function. The method constructs multivariate quantile curves using sequential univariate quantile regressions derived from conditioning mechanisms, allowing for an intuitive interpretation and flexible estimation of marginal effects. The paper develops theoretical foundations of MQR, including asymptotic properties of the estimators. Through simulation exercises, the estimator demonstrates robust finite sample performance across different dependence structures. As an empirical application, the MQR framework is applied to the analysis of exchange rate pass-through in Argentina from 2004 to 2024. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.15749 |
| By: | Jialing Han; Yu-Ning Li |
| Abstract: | We propose a novel framework for approximate factor models that integrates an S-vine copula structure to capture complex dependencies among common factors. Our estimation procedure proceeds in two steps: first, we apply principal component analysis (PCA) to extract the factors; second, we employ maximum likelihood estimation that combines kernel density estimation for the margins with an S-vine copula to model the dependence structure. Jointly fitting the S-vine copula with the margins yields an oblique factor rotation without resorting to ad hoc restrictions or traditional projection pursuit methods. Our theoretical contributions include establishing the consistency of the rotation and copula parameter estimators, developing asymptotic theory for the factor-projected empirical process under dependent data, and proving the uniform consistency of the projected entropy estimators. Simulation studies demonstrate convergence with respect to both the dimensionality and the sample size. We further assess model performance through Value-at-Risk (VaR) estimation via Monte Carlo methods and apply our methodology to the daily returns of S&P 500 Index constituents to forecast the VaR of S&P 500 index. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11619 |
| By: | Carolina Caetano; Gregorio Caetano; Leonard Goff; Eric Nielsen |
| Abstract: | We show that causal effects can be identified when there is bunching in the distribution of a continuous treatment variable, without imposing any parametric assumptions. This yields a new nonparametric method for overcoming selection bias in the absence of instrumental variables, panel data, or other popular research designs for causal inference. The method leverages the change of variables theorem from integration theory, relating the selection bias to the ratio of the density of the treatment and the density of the part of the outcome that varies with confounders. At the bunching point, the treatment level is constant, so the variation in the outcomes is due entirely to unobservables, allowing us to identify the denominator. Our main result identifies the average causal response to the treatment among individuals who marginally select into the bunching point. We further show that under additional smoothness assumptions on the selection bias, treatment effects away from the bunching point may also be identified. We propose estimators based on standard software packages and apply the method to estimate the effect of maternal smoking during pregnancy on birth weight. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05210 |
| By: | Xinran Li; Peizan Sheng; Zeyang Yu |
| Abstract: | Although appealing, randomization inference for treatment effects can suffer from severe size distortion due to sample attrition. We propose new, computationally efficient methods for randomization inference that remain valid under a range of potentially informative missingness mechanisms. We begin by constructing valid p-values for testing sharp null hypotheses, using the worst-case p-value from the Fisher randomization test over all possible imputations of missing outcomes. Leveraging distribution-free test statistics, this worst-case p-value admits a closed-form solution, connecting naturally to bounds in the partial identification literature. Our test statistics incorporate both potential outcomes and missingness indicators, allowing us to exploit structural assumptions-such as monotone missingness-for increased power. We further extend our framework to test non-sharp null hypotheses concerning quantiles of individual treatment effects. The methods are illustrated through simulations and an empirical application. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.00795 |
| By: | Atsushi Inoue; \`Oscar Jord\`a; Guido M. Kuersteiner |
| Abstract: | We consider the Anderson-Rubin (AR) statistic for a general set of nonlinear moment restrictions. The statistic is based on the criterion function of the continuous updating estimator (CUE) for a subset of parameters not constrained under the Null. We treat the data distribution nonparametrically with parametric moment restrictions imposed under the Null. We show that subset tests and confidence intervals based on the AR statistic are uniformly valid over a wide range of distributions that include moment restrictions with general forms of heteroskedasticity. We show that the AR based tests have correct asymptotic size when parameters are unidentified, partially identified, weakly or strongly identified. We obtain these results by constructing an upper bound that is using a novel perturbation and regularization approach applied to the first order conditions of the CUE. Our theory applies to both cross-sections and time series data and does not assume stationarity in time series settings or homogeneity in cross-sectional settings. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01167 |
| By: | David Van Dijcke |
| Abstract: | Standard methods for detecting discontinuities in conditional means are not applicable to outcomes that are complex, non-Euclidean objects like distributions, networks, or covariance matrices. This article develops a nonparametric test for jumps in conditional means when outcomes lie in a non-Euclidean metric space. Using local Fr\'echet regression, the method estimates a mean path on either side of a candidate cutoff. This extends existing $k$-sample tests to a non-parametric regression setting with metric-space valued outcomes. I establish the asymptotic distribution of the test and its consistency against contiguous alternatives. For this, I derive a central limit theorem for the local estimator of the conditional Fr\'echet variance and a consistent estimator of its asymptotic variance. Simulations confirm nominal size control and robust power in finite samples. Two empirical illustrations demonstrate the method's ability to reveal discontinuities missed by scalar-based tests. I find sharp changes in (i) work-from-home compositions at an income threshold for non-compete enforceability and (ii) national input-output networks following the loss of preferential U.S. trade access. These findings show the value of analyzing regression outcomes in their native metric spaces. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04560 |
| By: | Xun Lu; Liangjun Su |
| Abstract: | We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. We propose a two-way mean group (TW-MG) estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We also consider a pooled estimator and provide a Hausman-type test for poolability. Simulations demonstrate the excellent performance of our estimators and inference methods in finite samples. We apply our new methods to two datasets to examine the relationship between health-care expenditure and income, and estimate a production function. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10302 |
| By: | Eidous, Omar |
| Abstract: | One way of improving the rate of convergent of classical kernel estimator is to use higher –order kernel function. In this thesis suggests anew” additive kernel estimator” to estimate f(x) , the proposed estimator are simple and interpretable as the higher –order estimator . The asymptotic properties of the proposed estimator are derived and formula for the smoothing parameter is given based on minimizing the asymptotic mean square error (AMSE), and some important case of this estimator are studied . Theoretical and practical result show the good potential properties of the proposed estimator over the higher –order kernel estimator . Keyword: Kernel method, higher –order kernel estimator, smoothing parameter, bias rate of convergence. |
| Date: | 2025–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:thesis:nmpz9_v1 |
| By: | Jikai Jin; Lester Mackey; Vasilis Syrgkanis |
| Abstract: | Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a surprising way on the distribution of the treatment noise. Focusing on the partially linear model of \citet{robinson1988root}, we first show that the widely adopted double machine learning (DML) estimator is minimax rate-optimal for Gaussian treatment noise, resolving an open problem of \citet{mackey2018orthogonal}. Meanwhile, for independent non-Gaussian treatment noise, we show that DML is always suboptimal by constructing new practical procedures with higher-order robustness to nuisance errors. These \emph{ACE} procedures use structure-agnostic cumulant estimators to achieve $r$-th order insensitivity to nuisance errors whenever the $(r+1)$-st treatment cumulant is non-zero. We complement these core results with novel minimax guarantees for binary treatments in the partially linear model. Finally, using synthetic demand estimation experiments, we demonstrate the practical benefits of our higher-order robust estimators. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02275 |
| By: | Mikihito Nishi |
| Abstract: | We consider panel data models with group structure. We study the asymptotic behavior of least-squares estimators and information criterion for the number of groups, allowing for the presence of small groups that have an asymptotically negligible relative size. Our contributions are threefold. First, we derive sufficient conditions under which the least-squares estimators are consistent and asymptotically normal. One of the conditions implies that a longer sample period is required as there are smaller groups. Second, we show that information criteria for the number of groups proposed in earlier works can be inconsistent or perform poorly in the presence of small groups. Third, we propose modified information criteria (MIC) designed to perform well in the presence of small groups. A Monte Carlo simulation confirms their good performance in finite samples. An empirical application illustrates that K-means clustering paired with the proposed MIC allows one to discover small groups without producing too many groups. This enables characterizing small groups and differentiating them from the other large groups in a parsimonious group structure. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.15408 |
| By: | Kevin Dano; Bo E. Honor\'e; Martin Weidner |
| Abstract: | This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies for eliminating nuisance parameters: conditional likelihood methods, which remove fixed effects by conditioning on sufficient statistics, and moment-based methods, which derive fixed-effect-free moment conditions. We demonstrate how these approaches apply to a variety of models, summarizing key findings from the literature while also presenting new examples and new results. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11556 |
| By: | Alexis Derumigny; Lucas Girard; Yannick Guyonvarch |
| Abstract: | We contribute to bridging the gap between large- and finite-sample inference by studying confidence sets (CSs) that are both non-asymptotically valid and asymptotically exact uniformly (NAVAE) over semi-parametric statistical models. NAVAE CSs are not easily obtained; for instance, we show they do not exist over the set of Bernoulli distributions. We first derive a generic sufficient condition: NAVAE CSs are available as soon as uniform asymptotically exact CSs are. Second, building on that connection, we construct closed-form NAVAE confidence intervals (CIs) in two standard settings -- scalar expectations and linear combinations of OLS coefficients -- under moment conditions only. For expectations, our sole requirement is a bounded kurtosis. In the OLS case, our moment constraints accommodate heteroskedasticity and weak exogeneity of the regressors. Under those conditions, we enlarge the Central Limit Theorem-based CIs, which are asymptotically exact, to ensure non-asymptotic guarantees. Those modifications vanish asymptotically so that our CIs coincide with the classical ones in the limit. We illustrate the potential and limitations of our approach through a simulation study. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16776 |
| By: | Matthias Eckardt; Philipp Otto |
| Abstract: | Compositional data, such as regional shares of economic sectors or property transactions, are central to understanding structural change in economic systems across space and time. This paper introduces a spatiotemporal multivariate autoregressive model tailored for panel data with composition-valued responses at each areal unit and time point. The proposed framework enables the joint modelling of temporal dynamics and spatial dependence under compositional constraints and is estimated via a quasi maximum likelihood approach. We build on recent theoretical advances to establish identifiability and asymptotic properties of the estimator when both the number of regions and time points grow. The utility and flexibility of the model are demonstrated through two applications: analysing property transaction compositions in an intra-city housing market (Berlin), and regional sectoral compositions in Spain's economy. These case studies highlight how the proposed framework captures key features of spatiotemporal economic processes that are often missed by conventional methods. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14389 |
| By: | Cl\'ement de Chaisemartin; Xavier D'Haultf{\oe}uille |
| Abstract: | This paper considers the identification of dynamic treatment effects with panel data, in complex designs where the treatment may not be binary and may not be absorbing. We first show that under no-anticipation and parallel-trends assumptions, we can identify event-study effects comparing outcomes under the actual treatment path and under the status-quo path where all units would have kept their period-one treatment throughout the panel. Those effects can be helpful to evaluate ex-post the policies that effectively took place, and once properly normalized they estimate weighted averages of marginal effects of the current and lagged treatments on the outcome. Yet, they may still be hard to interpret, and they cannot be used to evaluate the effects of other policies than the ones that were conducted. To make progress, we impose another restriction, namely a random coefficients distributed-lag linear model, where effects remain constant over time. Under this model, the usual distributed-lag two-way-fixed-effects regression may be misleading. Instead, we show that this random coefficients model can be estimated simply. We illustrate our findings by revisiting Gentzkow et al. (2011). |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.07808 |
| By: | Gregorio Impavido |
| Abstract: | This paper proposes a “quasi-agnostic” sign restriction procedure to identify structural shocks in frequentist structural vector autoregression (SVAR) models. It argues that low acceptance rates, inherent to agnostic sign restriction procedures, are not necessarily an indication of model misspecification. They can be low because agnostic procedures fail to exploit the ex ante priors on the sign of responses of macro variables to structural shocks. |
| Keywords: | VARs; SVARs; parametric restrictions; sign restrictions |
| Date: | 2025–08–15 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/162 |
| By: | Zhongyuan Lyu; Ming Yuan |
| Abstract: | Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the noise has complex dependence structure. We argue that the inconsistency often stems from bias and introduce a general approach to restore consistency. Specifically, we propose a general weighting scheme for PCA and show that with a suitable choice of weighting matrices, it is possible to deduce consistent and asymptotic normal estimators under much weaker conditions than the usual PCA. While the optimal weight matrix may require knowledge about the factors and covariance of the idiosyncratic noise that are not known a priori, we develop an agnostic approach to adaptively choose from a large class of weighting matrices that can be viewed as PCA for weighted linear combinations of auto-covariances among the observations. Theoretical and numerical results demonstrate the merits of our methodology over the usual PCA and other recently developed techniques for large-dimensional approximate factor models. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.15675 |
| By: | Bruno E. Holtz; Ricardo S. Ehlers; Adriano K. Suzuki; Francisco Louzada |
| Abstract: | Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the SMSN family (DynSSV-SMSN), using priors that penalize model complexity. Parameter estimation was carried out using the Hamiltonian Monte Carlo (HMC) method via the \texttt{RStan} package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO-CV information criteria. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10778 |
| By: | Weilong Liu; Yanchu Liu |
| Abstract: | The comovement phenomenon in financial markets creates decision scenarios with positively correlated asset returns. This paper addresses covariance matrix estimation under such conditions, motivated by observations of significant positive correlations in factor-sorted portfolio monthly returns. We demonstrate that fine-tuning eigenvectors linked to weak factors within rotation-equivariant frameworks produces well-conditioned covariance matrix estimates. Our Eigenvector Rotation Shrinkage Estimator (ERSE) pairwise rotates eigenvectors while preserving orthogonality, equivalent to performing multiple linear shrinkage on two distinct eigenvalues. Empirical results on factor-sorted portfolios from the Ken French data library demonstrate that ERSE outperforms existing rotation-equivariant estimators in reducing out-of-sample portfolio variance, achieving average risk reductions of 10.52\% versus linear shrinkage methods and 12.46\% versus nonlinear shrinkage methods. Further checks indicate that ERSE yields covariance matrices with lower condition numbers, produces more concentrated and stable portfolio weights, and provides consistent improvements across different subperiods and estimation windows. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01545 |
| By: | Bahaa Aly, Tarek |
| Abstract: | This paper introduced a novel methodology for measuring nonlinear Granger causality in macroeconomic time series by combining Multivariate Singular Spectrum Analysis (MSSA) for data denoising with Artificial Neural Network (ANN) input occlusion for causal inference. We applied this framework to five countries, analyzing key macro-financial variables, including yield curve latent factors, equity indices, exchange rates, inflation, GDP, and policy rates. MSSA enhanced data quality by maximizing signal-to-noise ratios while preserving structural patterns, resulting in more stable ΔMSE values and reduced error variability. ANNs were trained on MSSA-denoised data to predict each target variable using lagged inputs, with input occlusion evaluating the marginal predictive contribution of each input to derive causality p-values. This approach outperformed traditional VAR-based Granger causality tests, identifying 38 significant causal relationships compared to 24 for VAR. Cross-country analysis of variables revealed differences in transmission mechanisms, monetary policy effectiveness, and growth-inflation dynamics. Notably, feature importance rankings showed that policy rates and stock market indices predominantly drove macroeconomic outcomes across countries, underscoring their critical role in economic dynamics. These findings demonstrated that combining MSSA and ANN input occlusion offered a robust framework for analyzing nonlinear causality in complex macroeconomic systems. |
| Keywords: | Nonlinear Granger causality, Input Occlusion, Multiple Singular Spectrum Analysis, p-values |
| JEL: | C45 |
| Date: | 2025–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125453 |
| By: | Edward P. Herbst; Benjamin K. Johannsen |
| Abstract: | This comment discusses Kolesár and Plagborg-Møller's (2025) finding that the standard linear local projection (LP) estimator recovers the average marginal effect (AME) even in nonlinear settings. We apply and discuss a subset their results using a simple nonlinear time series model, emphasizing the role of the weighting function and the impact of nonlinearities on small-sample properties. |
| Keywords: | Local projections; Average marginal effect; Nonlinear time series; Small-sample properties; Weighting function |
| JEL: | C32 C52 E32 |
| Date: | 2025–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-58 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper introduces a novel framework for causal inference in spatial economics that explicitly models the stochastic transition from partial to general equilibrium effects. We develop a Denoising Diffusion Probabilistic Model (DDPM) integrated with boundary detection methods from stochastic process theory to identify when and how treatment effects propagate beyond local markets. Our approach treats the evolution of spatial spillovers as a L\'evy process with jump-diffusion dynamics, where the first passage time to critical thresholds indicates regime shifts from partial to general equilibrium. Using CUSUM-based sequential detection, we identify the spatial and temporal boundaries at which local interventions become systemic. Applied to AI adoption across Japanese prefectures, we find that treatment effects exhibit L\'evy jumps at approximately 35km spatial scales, with general equilibrium effects amplifying partial equilibrium estimates by 42\%. Monte Carlo simulations show that ignoring these stochastic boundaries leads to underestimation of treatment effects by 28-67\%, with particular severity in densely connected economic regions. Our framework provides the first rigorous method for determining when spatial spillovers necessitate general equilibrium analysis, offering crucial guidance for policy evaluation in interconnected economies. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.06594 |
| By: | Soheil Ghili; K. Sudhir; Nitish Jain; Ankur Garg |
| Abstract: | We build on theoretical results from the mechanism design literature to analyze empirical models of second-degree price discrimination (2PD). We show that for a random-coefficients discrete choice ("BLP") model to be suitable for studying 2PD, it must capture the covariance between two key random effects: (i) the "baseline" willingness to pay (affecting all product versions), and (ii) the perceived differentiation between versions. We then develop an experimental design that, among other features, identifies this covariance under common data constraints in 2PD environments. We implement this experiment in the field in collaboration with an international airline. Estimating the theoretically motivated empirical model on the experimental data, we demonstrate its applicability to 2PD decisions. We also show that test statistics from our design can enable qualitative inference on optimal 2PD policy even before estimating a demand model. Our methodology applies broadly across second-degree price discrimination settings. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.13426 |
| By: | Zheng Fan; Worapree Maneesoonthorn; Yong Song |
| Abstract: | We propose the Markov Switching Dynamic Shrinkage process (MSDSP), nesting the Dynamic Shrinkage Process (DSP) of Kowal et al. (2019). We revisit the Meese-Rogoff puzzle (Meese and Rogoff, 1983a, b, 1988) by applying the MSDSP to the economic models deemed inferior to the random walk model for exchange rate predictions. The flexibility of the MSDSP model captures the possibility of zero coefficients (sparsity), constant coefficient (dynamic shrinkage), as well as sudden and gradual parameter movements (structural change) in the time-varying parameter model setting. We also apply MSDSP in the context of Bayesian predictive synthesis (BPS) (McAlinn and West, 2019), where dynamic combination schemes exploit the information from the alternative economic models. Our analysis provide a new perspective to the Meese-Rogoff puzzle, illustrating that the economic models, enhanced with the parameter flexibility of the MSDSP, produce predictive distributions that are superior to the random walk model, even when stochastic volatility is considered. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14408 |
| By: | Phillip Heiler; Michael C. Knaus |
| Abstract: | Analysis of effect heterogeneity at the group level is standard practice in empirical treatment evaluation research. However, treatments analyzed are often aggregates of multiple underlying treatments which are themselves heterogeneous, e.g. different modules of a training program or varying exposures. In these settings, conventional approaches such as comparing (adjusted) differences-in-means across groups can produce misleading conclusions when underlying treatment propensities differ systematically between groups. This paper develops a novel decomposition framework that disentangles contributions of effect heterogeneity and qualitatively distinct components of treatment heterogeneity to observed group-level differences. We propose semiparametric debiased machine learning estimators that are robust to complex treatments and limited overlap. We revisit a widely documented gender gap in training returns of an active labor market policy. The decomposition reveals that it is almost entirely driven by women being treated differently than men and not by heterogeneous returns from identical treatments. In particular, women are disproportionately targeted towards vocational training tracks with lower unconditional returns. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01517 |
| By: | Simon M. S. Lo; Ralf A. Wilke; Takeshi Emura |
| Abstract: | The assumption of hazard rates being proportional in covariates is widely made in empirical research and extensive research has been done to develop tests of its validity. This paper does not contribute on this end. Instead, it gives new insights on the implications of proportional hazards (PH) modelling in competing risks models. It is shown that the use of a PH model for the cause-specific hazards or subdistribution hazards can strongly restrict the class of copulas and marginal hazards for being compatible with a competing risks model. The empirical researcher should be aware that working with these models can be so restrictive that only degenerate or independent risks models are compatible. Numerical results confirm that estimates of cause-specific hazards models are not informative about patterns in the data generating process. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10577 |
| By: | Luan M. T. de Moraes; Ant\^onio M. S. Mac\^edo; Giovani L. Vasconcelos; Raydonal Ospina |
| Abstract: | We introduce a method for describing eigenvalue distributions of correlation matrices from multidimensional time series. Using our newly developed matrix H theory, we improve the description of eigenvalue spectra for empirical correlation matrices in multivariate financial data by considering an informational cascade modeled as a hierarchical structure akin to the Kolmogorov statistical theory of turbulence. Our approach extends the Marchenko-Pastur distribution to account for distinct characteristic scales, capturing a larger fraction of data variance, and challenging the traditional view of noise-dressed financial markets. We conjecture that the effectiveness of our method stems from the increased complexity in financial markets, reflected by new characteristic scales and the growth of computational trading. These findings not only support the turbulent market hypothesis as a source of noise but also provide a practical framework for noise reduction in empirical correlation matrices, enhancing the inference of true market correlations between assets. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14325 |
| By: | Danilo Leiva-León; Viacheslav Sheremirov; Jenny Tang; Egon Zakrajšek |
| Abstract: | This paper develops an econometric framework for identifying latent factors that provide real time estimates of supply and demand conditions shaping goods- and services-related price pressures in the U.S. economy. The factors are estimated using category-specific personal consumption expenditures (PCE) data on prices and quantities, using a sign-restricted dynamic factor model that imposes theoretical predictions of the effects of fluctuations in supply and demand on prices and associated quantities through factor loadings. The resulting estimates are used to decompose total PCE inflation into contributions from common factors—goods demand, goods supply, services demand, services supply, and inflation expectations—and category specific idiosyncratic components. Validation exercises demonstrate that the estimated factors provide an informative and coherent narrative of inflation dynamics over time and can be effectively used for forecasting and policy analysis. |
| Keywords: | inflation; goods; services; supply; demand; expectations; dynamic factor models; sign restrictions; factor loadings |
| JEL: | C11 C32 E31 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:101428 |
| By: | Cristiano Salvagnin; Vittorio del Tatto; Maria Elena De Giuli; Antonietta Mira; Aldo Glielmo |
| Abstract: | We propose to use a recently introduced non-parametric tool named Differentiable Information Imbalance (DII) to identify variables that are causally related -- potentially through non-linear relationships -- to the financial returns of the European Union Allowances (EUAs) within the EU Emissions Trading System (EU ETS). We examine data from January 2013 to April 2024 and compare the DII approach with multivariate Granger causality, a well-known linear approach based on VAR models. We find significant overlap among the causal variables identified by linear and non-linear methods, such as the coal futures prices and the IBEX35 index. We also find important differences between the two causal sets identified. On two synthetic datasets, we show how these differences could originate from limitations of the linear methodology. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.15667 |
| By: | David Imhof; Emanuel W Viklund; Martin Huber |
| Abstract: | We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging predictive features suggested in prior research. We test our approach on a large dataset covering 13 markets across seven countries. Our results show that predictive models based on GATs, trained on a subset of the markets, can be effectively transferred to other markets, achieving accuracy rates between 80% and 90%, depending on the hyperparameter settings. The best-performing configuration, applied to eight markets from Switzerland and the Japanese region of Okinawa, yields an average accuracy of 91% for cross-market prediction. When extended to 12 markets, the method maintains a strong performance with an average accuracy of 84%, surpassing traditional ensemble approaches in machine learning. These results suggest that GAT-based detection methods offer a promising tool for competition authorities to screen markets for potential cartel activity. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12369 |