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on Econometrics |
| By: | Jiawei Fu; Donald P. Green |
| Abstract: | How should researchers conduct causal inference when the outcome of interest is latent and measured imperfectly by multiple indicators? We develop a general nonparametric framework for identifying and estimating average treatment effects on latent outcomes in randomized experiments. We show that latent-outcome estimation faces two distinct noncomparability challenges. First, across studies, different measurement systems may cause estimators to target different empirical quantities even when the underlying latent treatment effect is the same. Second, within a study, different indicators may have different and possibly nonlinear relationships with the same latent outcome, making them not directly comparable. To address these challenges, we propose a design-based approach built around nonparametric bridge functions. We show that these bridge functions can be characterized and identified. Estimation relies on a debiasing procedure that permits valid inference even when the bridge functions are weakly identified. Simulations demonstrate that standard methods, such as principal components analysis and inverse covariance weighting, can generate spurious cross-study differences, whereas our approach recovers comparable latent treatment effects. Overall, the framework provides both a general strategy for causal inference with latent outcomes and practical guidance for designing measurements that support identification, comparability, and efficient estimation. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.08681 |
| By: | Lucas Girard; Elia Lapenta |
| Abstract: | We propose a novel procedure for estimating and conducting inference on average marginal effects in partially linear instrumental regressions using Reproducing Kernel Hilbert Space methods. Our procedure relies on a single regularization parameter. We obtain the consistency and asymptotic normality of our estimator. Since the variance of the limiting distribution has a complex analytical form, we propose a Bayesian bootstrap method to conduct inference and establish its validity. Our procedure is easy to implement and exhibits good finite-sample performance in simulations. Three empirical applications illustrate its implementation on real data, showing that it yields economically meaningful results. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.11393 |
| By: | Lixiong Li |
| Abstract: | Empirical researchers increasingly use upstream machine-learning (ML) methods to construct proxies for latent target variables from complex, unstructured data. A naive plug-in use of such proxies in downstream econometric models, however, can lead to biased estimation and invalid inference. This paper develops a framework for partial identification and inference in general moment models with ML-generated proxies. Our approach does not require restrictive assumptions on the upstream ML procedure, such as consistency or known convergence rates, nor does it require a complete validation sample containing all variables used in the downstream analysis. Instead, we assume access to two datasets: a downstream sample containing observed covariates and the proxy, and an auxiliary validation sample containing joint observations on the proxy and its target variable. We treat the proxy as a linking variable between these two samples, rather than as a literal noisy substitute for the latent target variable. Building on this idea, we develop a sharp identification strategy based on an unconditional optimal transport characterization and an inference procedure that controls asymptotic size using analytical critical values without resampling. Monte Carlo simulations show reliable size control and informative confidence sets across a range of predictive-accuracy scenarios. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10770 |
| By: | Jiyuan Tan; Jose Blanchet; Vasilis Syrgkanis |
| Abstract: | Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard instrumental variable (IV) assumptions when the instrument generates limited support in treatment propensity. Existing approaches typically optimize over marginal treatment response functions subject to moment restrictions and can discard identifying distributional information. We show that PRTE partial identification in the generalized Roy model can instead be formulated as a Constrained Conditional Optimal Transport (CCOT) problem. The resulting multidimensional CCOT problem reduces analytically to separable one-dimensional OT problems with product costs, yielding sharp closed-form bounds and avoiding direct solution of the original high-dimensional CCOT problem. We also develop estimation and inference procedures for these bounds: for discrete instruments, a Double Machine Learning (DML) approach based on Neyman-orthogonal scores that accommodates high-dimensional covariates while achieving the parametric $\sqrt{n}$ rate and asymptotic normality; for continuous instruments, we explicitly characterize the corresponding nonparametric convergence rates. The framework accommodates covariates, discrete and continuous instruments, and extensions to general treatment settings. In simulations and a bed-net subsidy application, the resulting bounds are substantially tighter than existing moment-relaxation methods. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12263 |
| By: | Nan Liu; Yanbo Liu; Yuya Sasaki; Yuanyuan Wan |
| Abstract: | The maximum score method (Manski, 1975, 1985) is a powerful approach for binary choice models, yet it is known to face both practical and theoretical challenges. In particular, the estimator converges at a slower-than-root-$n$ rate to a nonstandard limiting distribution. We investigate conditions under which strictly concave surrogate score functions can be employed to achieve identification through a smooth criterion function. This criterion enables root-$n$ convergence to a normal limiting distribution. While the conditions to guarantee these desired properties are nontrivial, we characterize them in terms of primitive conditions. Extensive simulation studies support, the root-$n$ convergence rate, the asymptotic normality, and the validity of the standard inference methods. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.13399 |
| By: | Harold D. Chiang; Ahnaf Rafi |
| Abstract: | The maximum score estimator of Manski (1975) provides an elegant approach to estimate slope coefficient in binary choice models without requiring parametric assumptions on the error distribution. However, under i.i.d. sampling, it admits a non-Gaussian limiting distribution and exhibits cube-root asymptotics, which complicates statistical inference. We show that, under multiway dependence, the maximum score estimator attains asymptotic normality at a parametric rate. We obtain this surprising result through the development of a general M-estimation theory that accommodates non-smooth objective functions under multiway dependence. We further propose and establish the validity of a bootstrap procedure for inference. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10232 |
| By: | Anna Bykhovskaya; Nour Meddahi |
| Abstract: | This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics, accommodating nonlinearities, network interactions, and cross-sectional dependence in the multivariate case. Existence and uniqueness of a stationary solution is established via a coupling argument tailored to the discontinuities inherent in binary data. A key theoretical result, further supported by our empirical illustration on S&P 100 data, shows that, under a rare-events scaling, aggregates of such binary processes converge to a Poisson autoregression, providing a micro-foundation for this widely used count model. Maximum likelihood estimation is proposed and illustrated empirically. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14394 |
| By: | Marcell T. Kurbucz |
| Abstract: | We study identification of a structural group effect when the group indicator $G\in\{0, 1\}$ is unobserved but the analyst observes a calibrated probability score $p\in[0, 1]$ satisfying $\mathbb{E}[G|p, X]=p$. Under a constant-coefficient structural mean model, the latent-group coefficient $\tau$ is point-identified from the joint law of observables $(Y, X, p)$ by a simple ratio of weighted moments: the covariance of the signed score $2p-1$ with the covariate-partialled outcome, divided by twice the residual variance of the score after conditioning on covariates. Identification fails if and only if the score is a deterministic function of $X$; we establish this by constructing an explicit continuum of observationally equivalent models indexed by arbitrary values of $\tau$. The identified coefficient differs from the marginal latent mean gap by a compositional term that is unidentified without further assumptions; we give a necessary and sufficient condition for the two to coincide. The oracle estimator is $\sqrt{n}$-consistent and asymptotically normal with a closed-form sandwich variance. Under calibration error bounded uniformly by $\delta$, the bias is bounded by $|\tau|\, \mathbb{E}[|2p-1|]\, \delta\, (2V^*)^{-1}$, a bound that is sharp over all calibration error functions of that magnitude. Hard-threshold classification at $p=1/2$ attenuates the estimated gap by a factor strictly less than one. Monte Carlo experiments confirm the asymptotic theory, trace the divergence of RMSE as $V^*\to 0$, illustrate the attenuation bias of hard-threshold classification, and verify identification of the variance-weighted estimand under heterogeneous effects. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.08798 |
| By: | Michael C. Knaus; Henri Pfleiderer |
| Abstract: | Difference-in-Differences (DiD) is a widely used research design that often relies on a conditional parallel trends (CPT) assumption. In contrast to settings with unconfoundedness, where causal graphs provide powerful frameworks for reasoning about valid conditioning variables, general-purpose graphical tools for CPT are missing. We introduce transformed Single World Intervention Graphs (SWIGs), the $\Delta$-SWIGs, and prove that they enable us to read off conditional independencies via $d$-separation that imply CPT. Using $\Delta$-SWIGs, we study valid conditioning strategies for DiD in complex settings with multiple periods and time-varying covariates. We show that when time-varying covariates affect the outcome, controlling for post-treatment variables is required for identification. However, even when such controls are included, pre-treatment parallel trends are only informative about a subset of the assumptions required for unbiased post-treatment effects, highlighting the limitations of purely empirical justifications of CPT. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12818 |
| By: | Bidoia, M.; Harvey, A.; Palumbo, D. |
| Abstract: | Data on maxima and minima arise in climate and environment, as well as in economics and finance. Specific examples include rainfall, river level and air quality. This article proposes a new score-driven time series model for dealing with such data. A modification, called the composite score, is used to guarantee invertibility. The statistical properties of the maximum likelihood estimator are investigated and applications to river flow and temperature shows that the model works well in practice. The composite score technique may well prove useful in other situations. |
| Keywords: | Frechet Distribution, Gumbel Distribution, Invertibility, Maximum, River Flow, Score |
| JEL: | C22 |
| Date: | 2026–03–07 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2620 |
| By: | Ecenur Oguz; Robert L. Bray |
| Abstract: | We develop the first general-purpose estimator for infinite-horizon dynamic discrete choice models whose estimation problem, after pre-computation, is unencumbered by large systems of linear equations -- either imposed as constraints, or embedded in the objective function. Our unnested fixed point (UFXP) and optimal unnested fixed point (OUFXP) estimators exploit a dual representation of Bellman's equation to separate the utility parameters from the dynamic programming fixed point. We establish the consistency and asymptotic normality of UFXP and OUFXP, as well as the efficiency of the latter. Our estimators enable researchers to model utility functions non-parametrically via flexible neural-network approximations. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.09736 |
| By: | Daouia, Abdelaati; Laurent, Thibault |
| Abstract: | This chapter discusses the current state of development of robust measures for evaluating firms’ production performance, focusing on two prominent approaches: (i) partial order-m frontiers and related efficiency scores based on probability-weighted moments, and (ii) their competing order-α counterparts, which rely on quantiles. It provides a structured overview of the original concepts and their recently introduced robustified versions, analyzing their strengths and weaknesses in terms of axiomatic properties, estimation methods, and robustness. The frontiles package offers various functions for computing both order-α and order-m frontiers and efficiency scores, including their robustified analogs, in the general setting with multiple inputs and outputs. It supports different performance measurement directions, namely input, output and hyperbolic orientations. Additionally, frontiles includes procedures for inference and robustness assessment, notably through confidence intervals, gross-error sensitivity and breakdown points. It also provides diagnostic checks to assess the presence of outliers in the data and, accordingly, to guide the choice of suitable trimming levels. It further enables the visualization of robust surface estimators in three-dimensional settings involving two inputs and one output. The use of this package is illustrated with a number of empirical applications. |
| Date: | 2026–04–02 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:131658 |
| By: | Dolling, Carl |
| Abstract: | This paper provides empirical guidance to researchers when choosing an adjustment method during multiple hypothesis testing. It is shown that methods vary greatly in their false positive rate, not just among each other but also when the p values of the tests stem from a different distribution. It is recommended that researchers carefully choose their adjustment methods, as the choice significantly affects the interpretation of findings. When the aim of the adjustment is to control Family-Wise Error, strict and simple methods like the Bonferroni correction or Union-Intersection tests offer great practical applicability. When controlling False-Discovery Rate, more powerful methods like the Benjamini-Hochberg or Simes-Hochberg methods are more appropriate. |
| Date: | 2026–04–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:metaar:d3svb_v1 |
| By: | Johannes Bleher (Department of Econometrics and Empirical Economics & Computational Science Hub, University of Hohenheim); Claudia Tarantola (Department of Economics, Management and Quantitative Methods, University of Milan) |
| Abstract: | When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli trials and accumulates evidence for variable relevance through a likelihood-ratio process admitting an approximate Bayes-factor interpretation. The procedure provides both a variable inclusion criterion and a stopping rule that eliminates the need to fix the number of bootstrap-imputation iterations ex ante. A Monte Carlo study across 126 scenarios and an empirical illustration demonstrate the method's performance relative to existing aggregation approaches. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12783 |
| By: | Younghoon Kim; Changryong Baek |
| Abstract: | This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel (ScBM) into the transition matrices of VAR-type systems, we separate sending and receiving roles at the node level and summarize complex directional dependence in an interpretable low-dimensional form. Our method integrates directed spectral co-clustering with eigenvector smoothing to track how directional groups split, merge, or persist over time. This framework accommodates both periodic VAR (PVAR) models for cyclical seasonal evolution and generalized VHAR models for structural transitions across ordered dependence horizons. We establish non-asymptotic misclassification bounds for both procedures and provide supporting evidence through Monte Carlo experiments. Applications to U.S.\ nonfarm payrolls distinguish a recurrent business-centered core from more mobile, seasonally sensitive sectors. In global stock volatilities, the results reveal a compact U.S.-centered long-horizon block, a Europe-heavy developed core, and a more dynamic short-horizon reallocation of peripheral and bridge markets. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12563 |
| By: | Hilde C. Bjornland; Nicolas Hardy; Dimitris Korobilis |
| Abstract: | We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5\% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25\% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12927 |
| By: | Paul Goldsmith-Pinkham |
| Abstract: | How far has the credibility revolution spread beyond applied microeconomics? I update Currie, Kleven, and Zwiers (2020b) using approximately 44, 000 papers—31, 500 NBER working papers (1982–2025) and 12, 300 articles from eleven top economics and finance journals (2011–2024)—measuring mentions of empirical methods through keyword matching. Three findings emerge. First, finance and macro/other fields differ substantially from applied micro in their mention of credibility revolution methods: as of 2024, 63 percent of applied micro papers mention experimental or quasi-experimental methods, compared to 47 percent in finance and 39 percent in macro/other. The current levels in finance and macro/other are comparable to where applied micro was in 2008–2010, though the long-run trajectories may differ. Second, growth outside applied micro is driven overwhelmingly by difference-in-differences; including DiD raises the share of finance papers mentioning any experimental or quasi-experimental method by roughly 55 percent versus 30 percent for applied micro. Other quasi-experimental methods—instrumental variables, regression discontinuity, experiments—have seen far less growth. Third, I document a striking gap between the methods studied in the Journal of Econometrics—where nonparametric estimation and asymptotic theory dominate—and those used by applied researchers, where DiD and identification strategies dominate. Published journal articles confirm these patterns are not artifacts of the NBER sample. |
| JEL: | B40 C01 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35051 |
| By: | Deniz Dutz; Morten Håvarstein; Magne Mogstad; Alexander Torgovitsky |
| Abstract: | We study the identification of labor supply elasticities from kinked budget sets in a model with income effects and individual heterogeneity in the elasticities. We provide point and partial identification results for compensated elasticities, uncompensated elasticities, and income effects. We use administrative data to apply our results to the Norwegian tax system, which exhibits a kink for the self-employed. There is clear bunching around the kink point, suggesting that the self-employed respond to the change in incentives created by the kink. We find that the bounds are often tight even under weak assumptions. Our results show that uncompensated elasticities are close to zero and compensated elasticities are sufficiently small to conclude that the excess burden of taxation is low. |
| JEL: | C14 C18 H21 H24 J22 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35047 |