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on Econometrics |
| By: | David Kang; Seojeong Lee |
| Abstract: | This paper develops an asymptotic distribution theory for Generalized Method of Moments (GMM) estimators, including the one-step and iterated estimators, when the moment conditions are nonsmooth and possibly misspecified. We consider nonsmooth moment functions that are directionally differentiable—such as absolute value functions and functions with kinks—but not indicator functions. While GMM estimators remain √n-consistent and asymptotically normal for directionally differentiable moments, conventional GMM variance estimators are inconsistent under moment misspecification. We propose a consistent estimator for the asymptotic variance for valid inference. Additionally, we show that the nonparametric bootstrap provides asymptotically valid confidence intervals. Our theory is applied to quantile regression with endogeneity under the location-scale model, offering a robust inference procedure for the GMM estimators in Machado and Santos Silva (2019). Simulation results support our theoretical findings. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:lan:wpaper:423284005 |
| By: | Jan-Lukas Wermuth |
| Abstract: | This paper develops an intuitive concept of perfect dependence between two variables of which at least one has a nominal scale that is attainable for all marginal distributions and proposes a set of dependence measures that are 1 if and only if this perfect dependence is satisfied. The advantages of these dependence measures relative to classical dependence measures like contingency coefficients, Goodman-Kruskal's lambda and tau and the so-called uncertainty coefficient are twofold. Firstly, they are defined if one of the variables is real-valued and exhibits continuities. Secondly, they satisfy the property of attainability. That is, they can take all values in the interval [0, 1] irrespective of the marginals involved. Both properties are not shared by the classical dependence measures which need two discrete marginal distributions and can in some situations yield values close to 0 even though the dependence is strong or even perfect. Additionally, I provide a consistent estimator for one of the new dependence measures together with its asymptotic distribution under independence as well as in the general case. This allows to construct confidence intervals and an independence test, whose finite sample performance I subsequently examine in a simulation study. Finally, I illustrate the use of the new dependence measure in two applications on the dependence between the variables country and income or country and religion, respectively. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.00785 |
| By: | Jonas E. Arias; Juan F. Rubio-Ramirez; Minchul Shin |
| Abstract: | We develop a new algorithm for inference based on structural vector autoregressions (SVARs) identified with sign restrictions. The key insight of our algorithm is to break from the accept-reject tradition associated with sign-identified SVARs. We show that embedding an elliptical slice sampling within a Gibbs sampler approach can deliver dramatic gains in speed and turn previously infeasible applications into feasible ones. We provide a tractable example to illustrate the power of the elliptical slice sampling applied to sign-identified SVARs. We demonstrate the usefulness of our algorithm by applying it to a well-known small SVAR model of the oil market featuring a tight identified set, as well as to a large SVAR model with more than 100 sign restrictions. |
| Keywords: | large structural vector autoregressions; sign restrictions; slice elliptical sampling |
| JEL: | C32 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:100040 |
| By: | Torben G. Andersen (Department of Finance, Northwestern University); Yi Ding (Faculty of Business Administration, University of Macau); Viktor Todorov (Department of Finance, Northwestern University); Seunghyeon Yu (Department of Finance, Northwestern University) |
| Abstract: | We develop nonparametric estimates for tail risk in the cross-section of asset prices at high frequencies. We show that the tail behavior of the crosssectional return distribution depends on whether the time interval contains a systematic jump event. If so, the cross-sectional return tail is governed by the assets’ exposures to the systematic event while, otherwise, it is determined by the idiosyncratic jump tails of the stocks. We develop an estimator for the tail shape of the cross-sectional return distribution that display distinct properties with and without systematic jumps. Empirically, we provide evidence for symmetric cross-sectional return tails at high-frequency that exhibit nontrivial and persistent time series variation. A hypothesis of equal cross-sectional return tail shapes during periods with and without systematic jump events is strongly rejected by the data. |
| Keywords: | Jumps, high-dimensional analysis, high-frequency data, infinitely divisible distribution, linear factor model |
| JEL: | C12 C13 C14 C58 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202531 |
| By: | Stéphane Bonhomme (UNIVERSITY OF CHICAGO); Angela Denis (BANCO DE ESPAÑA) |
| Abstract: | Many traditional panel data methods are designed to estimate homogeneous coefficients. While a recent literature acknowledges the presence of coefficient heterogeneity, its main focus so far has been on average effects. In this paper we review various approaches that allow researchers to estimate heterogeneous coefficients, hence shedding light on how effects vary across units and over time. We start with traditional heterogeneous-coefficients fixed-effects methods, and point out some of their limitations. We then describe bias-correction methods, as well as two approaches that impose additional assumptions on the heterogeneity: grouping methods, and random-effects methods. We also review factor and grouped-factor methods that allow coefficients to vary over time. We illustrate these methods using panel data on temperature and corn yields in the United States, and find substantial heterogeneity across counties and over time in temperature impacts. |
| Keywords: | panel data, fixed effects, coefficient heterogeneity |
| JEL: | C10 C50 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2526 |
| By: | Marín Díazaraque, Juan Miguel; Romero, Eva; Lopes Moreira Da Veiga, María Helena |
| Abstract: | This paper introduces a new asymmetric stochastic volatility model designed to capture how both the sign and magnitude of past shocks influence future volatility. The proposed Leverage Propagation Stochastic Volatility (LPSV) model extends traditional formulations by allowing the feedback mechanism to evolve over time, offering a more persistent and realistic representation of leverage effects than standard asymmetric stochastic volatility models. Based on the intuition that the impact of negative shocks on volatility unfolds gradually, rather than instantaneously, the model encodes this ``leverage propagation'' directly in its structure. Under Gaussian assumptions, we establish stationarity conditions and derive closed-form expressions for variance, kurtosis, and a novel leverage propagation function that quantifies delayed transmission of asymmetry. A Monte Carlo study confirms the robustness of Bayesian inference via Markov chain Monte Carlo (MCMC), even under heavy-tailed shocks. In empirical applications, the LPSV model captures volatility clustering and asymmetric persistence more effectively than competing alternatives, using daily financial returns from the German DAX and U.S. S&P 500. Moreover, the model captures prolonged volatility responses to non-financial shocks -illustrated through PM2.5 air pollution data from Madrid during Saharan dust events, demonstrating its broader relevance for environmental volatility modelling. These findings highlight the versatility of the model to trace the dynamics of delayed volatility sensitive to sign in different domains where understanding the persistence of risk is crucial. |
| Keywords: | Asymmetric volatility; Bayesian inference; Heavy tails; Leverage effect; Volatility feedback; Stochastic volatility |
| Date: | 2025–05–26 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:47005 |
| By: | Sumedh Gupte; Prashanth L. A.; Sanjay P. Bhat |
| Abstract: | We consider the problems of estimation and optimization of two popular convex risk mea- sures: utility-based shortfall risk (UBSR) and Optimized Certainty Equivalent (OCE) risk. We extend these risk measures to cover possibly unbounded random variables. We cover prominent risk measures like the entropic risk, expectile risk, monotone mean-variance risk, Value-at-Risk, and Conditional Value-at-Risk as few special cases of either the UBSR or the OCE risk. In the context of estimation, we derive non-asymptotic bounds on the mean absolute error (MAE) and mean-squared error (MSE) of the classical sample average approximation (SAA) estimators of both, the UBSR and the OCE. Next, in the context of optimization, we derive expressions for the UBSR gradient and the OCE gradient under a smooth parameterization. Utilizing these expres- sions, we propose gradient estimators for both, the UBSR and the OCE. We use the SAA estimator of UBSR in both these gradient estimators, and derive non-asymptotic bounds on MAE and MSE for the proposed gradient estimation schemes. We incorporate the aforementioned gradient estima- tors into a stochastic gradient (SG) algorithm for optimization. Finally, we derive non-asymptotic bounds that quantify the rate of convergence of our SG algorithm for the optimization of the UBSR and the OCE risk measure |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.01101 |
| By: | Handziuk, Yurii |
| Abstract: | Many institutional investors hold portfolios with few holdings. This makes it challenging to precisely estimate their individual demand. In this paper, I seek to make two contributions. First, I propose a data augmentation technique based on the generation of data-driven and economically interpretable synthetic assets. I show that this data augmentation acts as an adaptive nonlinear shrinkage which automatically adjusts the shape of the penalty to the cost of overfitting faced by the nonlinear demand function estimator. The resulting estimation technique leads to substantial improvement in cross-out-of-sample R2 for estimation of both low-dimensional and high-dimensional demand functions. Second, I use the proposed methodology to construct a measure of investor differentiation. Using the Morningstar mutual fund ratings reform in 2002 as a shock to competition for alpha, I show that mutual funds escape the increased competition intensity by differentiating from their competitors. |
| Keywords: | Asset demand system; Asset management; Competition; Differentiation; Machine learning; Data augmentation; Synthetic data |
| JEL: | C13 G11 G23 |
| Date: | 2025–01–15 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1541 |
| By: | Philippe Goulet Coulombe; Massimiliano Marcellino; Dalibor Stevanovic |
| Abstract: | We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle cross-sectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross-sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings. Nous étudions le nowcasting des variables budgétaires des États américains à l’aide de modèles d’apprentissage automatique (machine learning) et de prédicteurs à fréquence mixte, dans un cadre en panel. Les réseaux de neurones intégrant des variables continues et des identifiants catégoriels surpassent systématiquement les alternatives linéaires, en particulier lorsqu’ils sont combinés à des structures en panel mutualisé. Ces architectures permettent de capter les différences entre les États tout en tirant parti des régularités partagées. Les gains de prévision sont particulièrement importants pour les variables volatiles comme les dépenses et les déficits. Le regroupement des données améliore la stabilité des prévisions, et les modèles d’apprentissage automatique sont mieux adaptés pour traiter les non-linéarités transversales. Les résultats montrent que les améliorations prédictives sont généralisées et que même quelques indicateurs infranuels spécifiques aux États contribuent de manière significative à la précision des prévisions. Nos résultats soulignent la complémentarité entre la modélisation flexible et le regroupement transversal, faisant des réseaux de neurones en panel un outil puissant pour un suivi budgétaire rapide et précis dans des contextes hétérogènes. |
| Keywords: | Machine learning, Nowcasting, Panel, Mixed-frequency, Fiscal indicators, Apprentissage automatique, Panel, Fréquences mixtes, Indicateurs budgétaires, Prévisions à court terme |
| JEL: | C53 C55 E37 H72 |
| Date: | 2025–05–27 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2025s-15 |
| By: | Qiang Chen; Tianyang Han; Jin Li; Ye Luo; Yuxiao Wu; Xiaowei Zhang; Tuo Zhou |
| Abstract: | Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates an agentic AI's capability to master econometrics, focusing on empirical analysis performance. We develop an ``Econometrics AI Agent'' built on the open-source MetaGPT framework. This agent exhibits outstanding performance in: (1) planning econometric tasks strategically, (2) generating and executing code, (3) employing error-based reflection for improved robustness, and (4) allowing iterative refinement through multi-round conversations. We construct two datasets from academic coursework materials and published research papers to evaluate performance against real-world challenges. Comparative testing shows our domain-specialized agent significantly outperforms both benchmark large language models (LLMs) and general-purpose AI agents. This work establishes a testbed for exploring AI's impact on social science research and enables cost-effective integration of domain expertise, making advanced econometric methods accessible to users with minimal coding expertise. Furthermore, our agent enhances research reproducibility and offers promising pedagogical applications for econometrics teaching. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00856 |