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on Econometrics |
| By: | Schafgans, Marcia M. A.; Zinde-Walsh, Victoria |
| Abstract: | This paper derives limit properties of nonparametric kernel regression estimators without requiring existence of density for regressors in ℝ . In functional regression limit properties are established for multivariate functional regression. The rate and asymptotic normality for the Nadaraya–Watson (NW) estimator is established for distributions of regressors in ℝ that allow for mass points, factor structure, multicollinearity and nonlinear dependence, as well as fractal distribution; when bounded density exists we provide statistical guarantees for the standard rate and the asymptotic normality without requiring smoothness. We demonstrate faster convergence associated with dimension reducing types of singularity, such as a fractal distribution or a factor structure in the regressors. The paper extends asymptotic normality of kernel functional regression to multivariate regression over a product of any number of metric spaces. Finite sample evidence confirms rate improvement due to singularity in regression over ℝ . For functional regression the simulations underline the importance of accounting for multiple functional regressors. We demonstrate the applicability and advantages of the NW estimator in our empirical study, which reexamines the job training program evaluation based on the LaLonde data . |
| Keywords: | Nadaraya–Watson estimator; singular distribution; multivariate functional regression; small cube probability |
| JEL: | C1 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:130725 |
| By: | Jean-Marie Dufour; Tianyu He |
| Abstract: | This paper proposes asymptotically distribution-free inference methods for comparing a broad range of welfare indices across dependent samples, including those employed in inequality, poverty, and risk analysis. Two distinct situations are considered. \emph{First}, we propose asymptotic and bootstrap intersection methods which are completely robust to arbitrary dependence between two samples. \emph{Second}, we focus on the common case of overlapping samples -- a special form of dependent samples where sample dependence arises solely from matched pairs -- and provide asymptotic and bootstrap methods for comparing indices. We derive consistent estimates for asymptotic variances using the influence function approach. The performance of the proposed methods is studied in a simulation experiment: we find that confidence intervals with overlapping samples exhibit satisfactory coverage rates with reasonable precision, whereas conventional methods based on an assumption of independent samples have an inferior performance in terms of coverage rates and interval widths. Asymptotic inference can be less reliable when dealing with heavy-tailed distributions, while the bootstrap method provides a viable remedy, unless the variance is substantial or nonexistent. The intersection method yields reliable results with arbitrary dependent samples, including instances where overlapping samples are not feasible. We demonstrate the practical applicability of our proposed methods in analyzing dynamic changes in household financial inequality in Italy over time. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21862 |
| By: | Liyuan Cui; Guanhao Feng; Yuefeng Han; Jiayan Li |
| Abstract: | We tackle the challenge of estimating grouping structures and factor loadings in asset pricing models, where traditional regressions struggle due to sparse data and high noise. Existing approaches, such as those using fused penalties and multi-task learning, often enforce coefficient homogeneity across cross-sectional units, reducing flexibility. Clustering methods (e.g., spectral clustering, Lloyd's algorithm) achieve consistent recovery under specific conditions but typically rely on a single data source. To address these limitations, we introduce the Panel Coupled Matrix-Tensor Clustering (PMTC) model, which simultaneously leverages a characteristics tensor and a return matrix to identify latent asset groups. By integrating these data sources, we develop computationally efficient tensor clustering algorithms that enhance both clustering accuracy and factor loading estimation. Simulations demonstrate that our methods outperform single-source alternatives in clustering accuracy and coefficient estimation, particularly under moderate signal-to-noise conditions. Empirical application to U.S. equities demonstrates the practical value of PMTC, yielding higher out-of-sample total $R^2$ and economically interpretable variation in factor exposures. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.23567 |
| By: | Julien Monardo |
| Abstract: | Discrete choice demand models are commonly used to answer various economic questions. This paper develops a representation theorem that establishes the necessary and sufficient functional form and shape restrictions characterizing a large family of discrete choice demand models extending beyond the traditional additive random utility framework. The representation theorem yields three significant empirical implications. First, it provides economic intuition for (parameter) restrictions commonly imposed on some popular discrete choice models. Second, it offers a specification toolfor building demand models that satisfy mild and easily verifiable properties while being consistent with utility maximization and accommodating rich substitution patterns, including complementarity in demand. Third, it provides an efficient numerical algorithm for demand inversion, a crucial step in the demand estimation procedure. |
| Date: | 2025–04–02 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:25/813 |
| By: | Haveresch, Nils; Ankel-Peters, Jörg; Bensch, Gunther |
| Abstract: | Exploiting exogenous natural variation to study the impact of hard-to-randomize policies based on instrumental variables (IVs) is a widespread research design in economics. The key identification assumption underlying this design is the exclusion restriction, requiring the IV to affect the outcome variable only through the instrumented treatment variable. We review the literature using topography as an IV to show that systematic violations of this assumption are likely because of topography's ubiquity in socio-economic relationships. Furthermore, as topography often lacks first-stage strength, even subtle violations of the exclusion restriction undermine any causal inference. Instead of the vindication that often accompanies IV applications, we advocate a falsificationist approach to the use of topographic IVs, grounded in precaution and skepticism. We apply this approach to a seminal example of a topographic IV, Dinkelman (2011). |
| Keywords: | Causal inference, collider bias, topography, exclusion restriction |
| JEL: | C26 C36 C52 O13 O18 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:i4rdps:278 |
| By: | Yesol Huh; Matthew Kling |
| Abstract: | This paper introduces parallel trends forest, a novel approach to selecting optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to find control units that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data. Applying the parallel trends forest to analyze the impact of post-trade transparency in corporate bond markets, we find that it produces more robust estimates compared to traditional two-way fixed effects models. Our results suggest that the effect of transparency on bond turnover is small and not statistically significant when allowing for constrained deviations from parallel trends. This method offers researchers a powerful tool for conducting more reliable DiD analyses in complex, real-world settings. |
| Keywords: | Causal inference; Difference-in-differences; Parallel trends assumption; Random forest |
| JEL: | C10 C21 C23 G12 |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-91 |
| By: | Andreï Kostyrka (DEM, Université du Luxembourg) |
| Abstract: | Empirical likelihood (EL) breaks down when the hypothesised mean falls outside the convex hull of the sample. We propose extrapolated EL (ExEL) – two splicing schemes that extend the log-EL ratio beyond the hull while leaving it unchanged on a user-chosen interior region. The first scheme, ExEL1, continues EL past a data-driven cut-off using its local quadratic (Taylor) expansion. The second scheme, ExEL2, smoothly splices EL to its globalWald quadratic approximation via a convex bridge. Both methods extend naturally to multiple dimensions by radial reduction. In simulations with small samples – where convex-hull violations are common – ExEL remains well-behaved and distinguishes mild from severe violations. It also has attractive inferential properties, delivering accurate coverage probabilities with bootstrap calibration. |
| Keywords: | empirical likelihood; convex hull; moment-condition models; extrapolation and splicing; radial reduction |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:luc:wpaper:25-19 |
| By: | Jaeho Kim (Sogang University); Scott C. Linn (University of Oklahoma); Sora Chon (Inha University) |
| Abstract: | We show that conclusions drawn from widely used measures of price discovery are highly sensitive to the presence of price outliers in the calculations. We demonstrate using simulation studies however that the long-run information share (LFS) measure of price discovery location proposed by Kim and Linn (2022), coupled with Bayesian estimation of a Vector Error Correction Model (VECM) allowing for outliers, provides the most robust and reliable metric for evaluating price discovery in the presence of outliers. A separate empirical analysis of the spot and futures prices of non-ferrous metals shows the pervasive presence of price outliers. Implementation of our proposed estimation of a VECM using Bayesian methods allowing for outliers and the subsequent calculation of LFS, provides strong evidence that both spot and futures markets for non-ferrous metals contribute significantly to the price discovery process when daily price data are employed. |
| Keywords: | Price discovery; Cointegration; Outliers; Robust estimation; Heavytailed distributions. |
| JEL: | C11 C32 C58 G14 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:inh:wpaper:2025-1 |
| By: | Nathan Kallus |
| Abstract: | Aligning large language models to preference data is commonly implemented by assuming a known link function between the distribution of observed preferences and the unobserved rewards (e.g., a logistic link as in Bradley-Terry). If the link is wrong, however, inferred rewards can be biased and policies be misaligned. We study policy alignment to preferences under an unknown and unrestricted link. We consider an $f$-divergence-constrained reward maximization problem and show that realizability of the solution in a policy class implies a semiparametric single-index binary choice model, where a scalar-valued index determined by a policy captures the dependence on demonstrations and the rest of the preference distribution is an unrestricted function thereof. Rather than focus on estimation of identifiable finite-dimensional structural parameters in the index as in econometrics, we focus on policy learning, focusing on error to the optimal policy and allowing unidentifiable and nonparametric indices. We develop a variety of policy learners based on profiling the link function, orthogonalizing the link function, and using link-agnostic bipartite ranking objectives. We analyze these and provide finite-sample policy error bounds that depend on generic functional complexity measures of the index class. We further consider practical implementations using first-order optimization suited to neural networks and batched data. The resulting methods are robust to unknown preference noise distribution and scale, while preserving the direct optimization of policies without explicitly fitting rewards. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21917 |
| By: | Laurens Cherchye; Thomas Demuynck; Bram De Rock; Joshua Lanier |
| Abstract: | Abstract We present a statistical test for the hypothesis of (approximate) utility maximization on the basis of nonparametric revealed preference conditions. We take as null hypothesis that the consumer behaves randomly, and we reject this hypothesis only if the data provides sufficient evidence to support the alternative hypothesis of approximate utility maximization. Our statistical test uses a permutation method to operationalize the principle of random consumption behavior. We show that our test (i) is valid for any sample size under the null and (ii) has an asymptotic power of one. We also provide simulated power results and two empirical applications. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/398375 |
| By: | Geert Goeyvaerts; Jakob Vanschoonbeek |
| Abstract: | In an influential paper, Borusyak & Hull (2023, BH) introduce a ‘recentering’ method to estimate treatment effects from random shocks when units systematically differ in shock exposure but the shock assignment process is known. This comment clarifies how the interpretation of the estimates based on recentering alters in dynamic settings with forward-looking agents, such as when firms make irreversible location or investment decisions based on anticipated infrastructure improvements. |
| Date: | 2025–12–12 |
| URL: | https://d.repec.org/n?u=RePEc:ete:vivwps:778148 |
| By: | Jan David Schneider |
| Abstract: | This paper quantifies the contribution of sector-specific supply and demand shocks to personal consumption expenditure (PCE) inflation. It derives identification restrictions that are consistent with a large class of dynamic stochastic general equilibrium models with production networks. It then imposes these restrictions in structural factor augmented vector autoregressive models with sectoral data on PCE inflation and consumption growth. The identification scheme allows the study to remain agnostic on theoretical modeling assumptions yet still gain structural empirical results: sectoral shocks cannot explain the initial inflation increases that followed the COVID-19 pandemic. This changed from the end of 2021 onward when shocks originating in non-services sectors became a major source of the post-pandemic inflation surge. |
| Keywords: | Business fluctuations and cycles; Econometric and statistical methods; Inflation and prices |
| JEL: | C50 E31 E32 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-37 |