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on Econometrics |
| By: | Haokun Lu; Xiaojun Song |
| Abstract: | This paper develops tuning-free tests for treatment-effect heterogeneity. By characterizing the null hypothesis as constancy of the expectile treatment-effect curve, we construct test statistics as continuous functionals of the mean-centered sample expectile process. We establish a uniform Bahadur representation, derive the asymptotic properties of the test statistics under the null and fixed alternatives, and show nontrivial power against local alternatives approaching the null at the $n^{-1/2}$ rate. For feasible inference, we develop a fast multiplier bootstrap that consistently approximates the nonpivotal null distribution while capturing the first-order effect of estimating the unknown common shift. The procedure is tuning-free, requiring no density or score-function estimation, bandwidth selection, or repeated refitting. The framework further incorporates a pointwise-studentized refinement and a covariate adjustment that reduces asymptotic variation without altering the population discrepancy. Monte Carlo comparisons with existing methods show generally reliable size control, competitive power, and substantial computational savings. An application to a large-scale water-conservation experiment illustrates the proposed tests. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25986 |
| By: | Kaicheng Chen; Hugo Freeman |
| Abstract: | We study inference on common slope parameters in linear panel models with two-way unobserved heterogeneity. In linear regression, bias arises from unobserved heterogeneity that is shared between the residual and the covariates. Our common unobserved heterogeneity (CUH) estimator targets this common variation directly: it searches over functionals of the residualised outcome for variation that also resides in the covariates, and seeks to further project out only bias-inducing variation. This makes the estimator less sensitive to weak outcome factors while avoiding the complete removal of exogenous variation in covariates that is unrelated to the confounding heterogeneity. We develop a weak-factor theory based on a singular-value strength index and establish $\sqrt{NT}$-asymptotic normality of the cross-fitted CUH estimator. Under homoskedastic errors, its asymptotic variance is no larger than that of a common correlated effects factor estimator. Simulations and empirical applications illustrate the resulting bias--variance trade-off and the gains from selectively removing common variation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25589 |
| By: | Geert Dhaene; Arturas Juodis |
| Abstract: | Panel local projections are widely used to estimate impulse responses in policy analysis. This paper examines the properties of the fixed-effect estimator and various bias-correction methods in panel local projections, focusing on settings with autoregressive control variables. We analyze the bias and variance of these estimators while allowing for persistence in both the target and control variables. Our theoretical findings indicate that the half-panel jackknife has desirable bias-reducing properties beyond the stationary setup. For empirical researchers, we recommend using bias-correction-adjusted standard errors to account for the sampling uncertainty associated with the half-panel jackknife estimator, regardless of the time-series dimension of the data. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.26427 |
| By: | Oriol González-Casasús |
| Abstract: | Models with latent groups are estimated in two steps: a classifier assigns units to groups, and estimated memberships are treated as data. Standard inference relies on the oracle property, which treats classification error as negligible, an assumption that is dubious in practice. I develop a local misclassification framework in which classification error and sampling noise are of the same order, and derive the first-order bias in linear models with grouped fixed-effects. Three findings matter most for practice: misclassification does not attenuate but mixes group-specific distortions, with weights of either sign; residual-based classifiers endogenously offset a fraction of the bias; and the time dimension attenuates bias when group paths are unsynchronized, beyond its effect on classification errors. I construct sharp, bias-aware confidence intervals, easy to compute and uniformly valid over misclassification patterns compatible with bounds on group-wise misclassification rates. Under some conditions, combining two classifiers provides valid standard inference without knowledge of the misclassification pattern. |
| Keywords: | classification error, latent grouped heterogeneity, local misspecification |
| JEL: | C12 C23 C38 G31 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1601 |
| By: | Ioannis Kasparis (University of Cyprus) |
| Abstract: | Existing regression inference methods for nonstationary heavy tailed data are largely limited to unit roots tests. Functional central limit theorems (FCLTs), which are commonly used for the asymptotic analysis of nonstationary models, are inapplicable to nonstationary data characterized by a memory parameter $d |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25237 |
| By: | Francesco Bravo; Degui Li; Zheng Li |
| Abstract: | This paper considers nonparametric quantile regression for directed dyadic data observed on the ordered pairs of a complete network with n vertices. The observations exhibit dyadic dependence: dyads with disjoint indices are independent, whereas dyads sharing one or two indices are correlated. The local linear smoothing technique is adopted to estimate the quantile regression function. The asymptotic theory reveals that convergence rates of the proposed dyadic quantile regression estimation crucially depend on whether degeneracy occurs for the underlying network generating process. A rate-adaptive standardized version of the limit distribution is derived under some mild conditions. In addition, an easy-to-implement method is proposed to estimate the asymptotic variance of nonparametric dyadic quantile regression estimation. The finite-sample performance and applicability of the proposed methodology are illustrated with Monte-Carlo simulation and a real data application to bilateral portfolio holdings. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25009 |
| By: | Hongqi Chen; Chengwang Liao; Ziwei Mei; Zhentao Shi |
| Abstract: | LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions with many regressors, the convex penalty of LASSO overly suppresses the coefficients of cointegrated regressors and therefore fails to deliver consistent parameter estimation. We employ folded concave penalties (FCP), including the well-known SCAD and MCP, to address this issue. Using FCP, we establish two asymptotic results: (i) a local oracle property for the penalized regression, and (ii) a global oracle property for regressions with a penalty-level constraint. In particular, the latter guarantees a global solution that consistently estimates the true coefficients and enjoys oracle prediction performance. Simulation studies and an application to predicting the euro area unemployment rate show that FCP improves variable selection and out-of-sample prediction relative to LASSO. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.14617 |
| By: | Christian Jobelius Schulz; Christoph Hanck; Simon Hirsch; Florian Ziel |
| Abstract: | We propose an online multivariate distributional forecasting model in which we condition the parameters of a vine copula on covariates. An online algorithm updates the parameters sequentially using only the most recent observations. Existing approaches require full batch re-estimation using all available data at each update step, which makes them computationally slow in large-scale forecasting applications and less suitable for settings in which dependence structures change over time. Simulation results show that the online procedure achieves forecasting performance comparable to that of the batch estimator, while reducing computation time substantially. We illustrate the method in a forecasting application to electricity net-demand across five regions of Great Britain and show that (i) the conditional vine copula model outperforms unconditional Gaussian and vine copula benchmarks and (ii) that online estimation improves forecast accuracy when batch re-estimation is not available. The algorithm is implemented in the Python package ondil. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.23119 |
| By: | Abhik Rana; Sarvesh Ravichandran Iyer; Sandeep Juneja |
| Abstract: | Effective transaction cost is a necessary step for devising any competing financial trading strategy in the absence of quote data. Classically, Roll's model estimated the transaction cost using the first order autocovariance of the difference between daily transaction prices, while Hasbrouck's Bayesian approach used iterative posterior simulation. However, Roll's model fails when the first order autocovariance becomes positive, while Hasbrouck's estimator is computationally intensive, especially when many securities are involved. We develop empirical characteristic function methods with a first order bias correcting for spread estimation, and establish the asymptotic distribution of the corrected estimator under suitable conditions. Our numerical experiments illustrate the bias-variance tradeoff of characteristic-function methods compared to Roll's model, and their computational advantage over Hasbrouck's model. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25445 |
| By: | Alberto Abadie; Yucheng Shang |
| Abstract: | The synthetic control method matches a treated unit to a convex combination of donor units, with the relative importance of each predictor determined by a weight matrix $V$. In standard practice, $V$ is selected through a bi-level optimization problem that is nonconvex and lacks convergence guarantees. This article derives an exact geometric solution for synthetic control estimators. For any positive definite $V$, a synthetic control is given by the projection of the treated unit's predictors onto the convex hull of the donor predictors under a $V$-weighted norm. As $V$ varies over its candidate class, these projections trace out the set of attainable synthetic controls. We characterize this set. When the predictor vector of the treated unit lies outside the convex hull of the donor predictors, the attainable set under general positive definite $V$ is the union of the facets of the donor hull that are visible from the treated unit. For diagonal $V$, a sign-compatibility test identifies the attainable set. When the treated unit lies inside the hull, exact matches exist. We select the one that minimizes the average of the pairwise matching discrepancies between the treated unit and each of the donors contributing to the synthetic control. These characterizations replace bi-level optimization with a fast enumeration method that is guaranteed to deliver the global optimum over the attainable set. Moreover, the Karush-Kuhn-Tucker conditions for the selection of donor weights characterize the set of positive definite predictor weight matrices that rationalize any given set of donor weights. Applications to the California tobacco control program and to the economic effects of German reunification illustrate the advantages of the proposed procedure over existing practice. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.26576 |
| By: | Hoang Giang Pham; Tien Mai |
| Abstract: | Nested logit is widely used in operational demand models, yet jointly estimating its utility coefficients and nest dissimilarity parameters requires nonconvex optimization. Standard estimation routines offer no certificate of global optimality, leaving a gap between globally optimizing decisions under nested-logit demand and reliably estimating that demand model. We develop, to our knowledge, the first globally convergent optimization framework with an explicit global-accuracy guarantee for joint nested-logit estimation. Our central complexity result confines all exponential dependence to the number of free nest dissimilarity parameters: computation is polynomial in the numbers of observations, alternatives, and utility coefficients. Consequently, under the stated compact-domain and relaxation-error conditions, fixing the number of nests yields an additive polynomial-time approximation scheme (PTAS). The framework exploits an exact exponential-cone formulation for fixed dissimilarities and a spatial branch-and-bound algorithm that searches only over the free dissimilarities. Valid likelihood bounds provide an anytime certificate and finite termination at any prescribed additive average-log-likelihood tolerance. Across eight benchmark datasets and 240 estimation instances, the method certifies every reported solution and exposes inferior local estimates despite normal solver termination. These results establish a computational foundation for globally certified nested-logit estimation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25038 |
| By: | Yanlin Zhang; Sungyong Chung; Alireza Talebpour |
| Abstract: | Synthetic control methods estimate counterfactual outcomes by weighting untreated donor units to match a treated unit before treatment. In recent applications, however, the donor pool can be large, highly correlated, and short in pre-treatment history, yielding many near-equivalent solutions with unstable and difficult-to-interpret donor weights. We propose Graph-Regularized Synthetic Control (Graph-SC), a convex estimator that augments the classical pre-treatment matching objective with a graph Laplacian penalty. The graph encodes similarity among donor trajectories, encouraging coherent weights over structurally related donors while preserving the transparency of synthetic controls. We provide theoretical results characterizing the bias-variance tradeoff induced by graph regularization and establish efficient convergence guarantees with an accelerated proximal gradient method. Across a controlled simulation under donor redundancy, the California Proposition 99 benchmark, and observational counterfactual analyses on the Waymo Open Motion Dataset, Graph-SC improves weight stability, reduces sensitivity to redundant donor pools, and maintains competitive pre- and post-treatment accuracy relative to classical and regularized synthetic control baselines. Our results suggest that graph regularization is most valuable not as a universal accuracy booster, but as a principled mechanism for selecting stable and interpretable counterfactuals from near-equivalent synthetic controls with comparable pre-treatment fit. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24113 |
| By: | Alexandre Jacquemain (UCLouvain); Cédric Heuchenne (UCLouvain, University of Liège); Philippe Van Kerm (University of Luxembourg) |
| Abstract: | Inequality of opportunity (IOp) is typically operationalized as explained inequality: inequality in the conditional expectation of an advantage given circumstances. Existing methods work in two stages, first estimating a model for the outcome, and then applying an inequality index to the fitted values. Any bias in the first stage can therefore propagate to the second stage. By contrast, this paper builds instead on Lorenz regressions which optimize the object of interest directly -- explained inequality -- under a flexible single-index model. We show that this provides a robust and unified framework adapted to both continuous and binary outcomes and illustrate the method for income- and education-based IOp, where it integrates naturally into existing work while improving estimation stability, interpretability, and robustness. |
| Keywords: | Explained inequality; Gini coefficient; Single-index model; Variable selection; Shapley value decomposition; Inequality of opportunity |
| JEL: | C14 C52 D31 D63 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:inq:inqwps:ecineq2026-699 |
| By: | Chun Pang Chow (Vancouver School of Economics, University of British Columbia (E-mail: alexccp@student.ubc.ca)); Hiroyuki Kasahara (Vancouver School of Economics, University of British Columbia (E-mail: hkasahar@mail.ubc.ca)); Atsuki Kotani (Department of Economics, Boston University (E-mail: atsuki@bu.edu)); Yoichi Sugita (Faculty of Business and Commerce, Keio University (E-mail: ysugita@fbc.keio.ac.jp)) |
| Abstract: | Conventional productivity estimation treats unobserved heterogeneity as a single efficiency term, conflating product quality with physical output efficiency. This paper shows that unobserved quality and quantity-based productivity (TFPQ) can be separately identified from panel data on prices, quantities, and inputs when the two latent variables exhibit differential persistence. A constructive nonparametric identification argument based on transformation models is developed, alongside a parametric estimator for implementation. Applied to Japanese cotton spinning (1914-1920), instrumenting for electric motor adoption with geographic variation in electricity grid access, quality increased by 1.5 percent per percentage point increase in electric motor share while TFPQ remained unchanged. Full electrification raised quality by nearly 60 percent relative to no adoption. |
| Keywords: | Production function, quality, productivity, electrification |
| JEL: | D24 L11 N65 O33 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:26-e-13 |
| By: | Tu Anh Doan Nguyen; Alessandro Bucciol |
| Abstract: | VAR-LiNGAM recovers a contemporaneous structure from reduced-form innovations under mutual independence of the underlying structural shocks. Recovery itself and bootstrap stability of the recovered edges are natural post-estimation outputs, but neither directly assesses the identifying independence assumption. We organize post-recovery validation as an ordered, non-compensatory protocol with four layers (reduced-form admissibility, distributional informativeness, independence support and edge persistence) and establish, by a constructive population result, a Monte Carlo experiment under known ground truth and a firm-level Compustat application, that the last two layers are non-nested: an edge can persist under resampling while the recovered shocks remain dependent, and independence can be supported while the leading recovered edge fails to persist. Independence support is classified operationally by a block-permutation dHSIC diagnostic on the recovered shocks, with mutual information as a continuous measure of residual dependence and tail co-exceedance as a complementary extreme-dependence diagnostic. Empirically, the conjunction of no detected independence violation and a reportable persistent leading edge occurs only in a small subset. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25238 |