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on Econometrics |
| By: | Gabriela Miyazato Szini |
| Abstract: | I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains asymptotically unbiased under sparsity, whether from the network structure or binarization at extreme thresholds. The second novelty is to establish the joint asymptotic distribution of the estimators across multiple thresholds with different convergence rates, and to develop simultaneous confidence bands and tests for equality of coefficients across thresholds. Monte Carlo simulations confirm small bias, valid inference, and correct simultaneous coverage under sparsity. An application to bilateral trade finds that coefficients vary substantially across the distribution, with equality rejected for key trade barriers. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04983 |
| By: | Xun Tang; Pei Yu |
| Abstract: | We study dynamic linear panel data models in which the lagged outcomes and strictly exogenous covariates carry individual-specific coefficients and the time-varying errors have a flexible covariance structure. With a fixed number of time periods, we point-identify the joint distribution of the random coefficients and the structural errors under a distributional form of strict exogeneity, and propose a closed-form, multi-step estimator based on the inverse Radon transform. We establish a uniform convergence rate for the estimator of the random coefficient density, as well as uniform consistency of the estimator for the conditional density of the time-varying structural errors. Monte Carlo simulations demonstrate good finite-sample performance of the estimators. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23988 |
| By: | Joseph Fry |
| Abstract: | Asymptotic normality approximations often fail to hold for extremum estimators when the true value of the parameter is at or close to the boundary of a parameter space. I analyze and develop tests using a quasi-unconstrained estimator, which is asymptotically normal even when the true parameter vector is near or at the boundary. These results generalize previous work with this estimator by allowing for more types of constraints and showing how the method can naturally be modified when a nuisance parameter is also high-dimensional. I show that variations of Wald, Likelihood Ratio, and Lagrange Multiplier tests can control size in a uniform sense, provided the initial constrained estimator is sufficiently accurate. Lastly, I apply the method to an application involving network estimation with panel data. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22002 |
| By: | Vladim\'ir Hol\'y |
| Abstract: | Trade durations in high-frequency foreign exchange data exhibit increased occurrence near integer values. To address this empirical phenomenon, we propose the granularity-adjusted autoregressive conditional duration (GA-ACD) model. It is based on a novel two-component mixture distribution consisting of a standard generalized gamma component for regular durations and a second component that locally redistributes probability mass around integer values to capture heaping. Conditional dynamics are modeled within a score-driven framework, allowing the scale parameter to vary over time in response to past durations, and enabling maximum likelihood estimation of all model parameters. A simulation study shows that ignoring heaping leads to biased parameter estimates and distorted inference regarding both the distribution and the dynamics of durations. An empirical analysis demonstrates that integer-duration clustering is pervasive across major currency pairs and that the GA-ACD model outperforms the standard generalized gamma ACD model. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.02660 |
| By: | Yanping Chen |
| Abstract: | Inference in linear regression commonly treats OLS residuals as proxies for unobserved errors. This approximation can fail when the regression projection is nonlocal relative to the error-dependence structure. Residualization then shifts covariance information across observations and clusters, while conventional heteroskedasticity-consistent (HC) and cluster-robust variance estimators (CRVE) retain only diagonal or within-cluster residual moments and may therefore understate sampling uncertainty. This paper develops a projection-geometry framework for robust variance estimation. The variance of the OLS estimator is represented exactly as a Riesz functional of latent covariance blocks, and observable residual moments are linked to the target through a linear operator determined by the full regression projection. This formulation reduces variance estimation to a linear inverse problem. I propose a Riesz variance estimator that combines within- and cross-cluster residual moments. Conventional HC and CRVE emerge as restricted approximations whose validity depends on negligible projection spillovers. The estimator remains well defined when cluster-specific leverage matrices are singular and is computed by an iterative algorithm that avoids explicit matrix inversion. Simulations show substantial undercoverage by conventional methods under projection spillovers, whereas the proposed estimator restores near-nominal coverage. In an application to colonial governor promotions, the correction changes the significance of four of five reported coefficients. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.01804 |
| By: | Carolina Caetano; Brantly Callaway; Stroud Payne; Hugo Sant'Anna |
| Abstract: | This paper considers difference-in-differences identification strategies when the parallel trends assumption holds after conditioning on covariates that may themselves be affected by the treatment (often referred to as "bad controls"). We show that common approaches such as simply dropping bad controls are often ill-advised and develop two alternative approaches that allow bad controls to function as genuine controls despite being affected by treatment. First, we derive explicit conditions that rationalize conditioning only on pre-treatment values of the bad control, leading naturally to the Callaway and Sant'Anna (2021) estimator with pre-treatment values as covariates. Second, under a covariate unconfoundedness condition, we develop imputation and double/debiased machine learning estimators that recover the average treatment effect on the treated. We extend these results to staggered treatment adoption, provide pre-tests for the identifying assumptions, and apply the methods to study the effects of job displacement on earnings. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.03881 |
| By: | Keita Sunada |
| Abstract: | This paper studies the nonparametric identification and estimation of additively separable triangular models with continuous endogenous and instrumental variables, allowing for a nonseparable first-stage equation. Under the independence of instrumental variables and unobservables, we show that the outcome function possesses a closed-form expression as a functional of conditional cumulative distribution functions. The resulting plug-in estimators require no regularization and converge at the rate $n^{-m/(2m+1)}$, where $m$ is the order of smoothness the model imposes. Also, no estimator of the outcome function converges faster. We use the empirical bootstrap to construct a uniform confidence band that covers the outcome function at every point of a compact set simultaneously. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22605 |
| By: | Ulrich Hounyo |
| Abstract: | Empirical work often removes fixed effects, latent factors, or high-dimensional controls before estimating structural relationships. These transformations reduce confounding but may also remove identifying variation. We study linear panel IV after one equation-compatible nuisance projection under two-way dependence. The projected Jacobian determines which structural directions remain visible; the projected-score law determines their precision; and, on Gaussian fixed-rank strata, they combine in a Projected Information Matrix. We derive weak-identification limits with dimension-specific information accumulation, feasible factor-transfer conditions, identification-robust tests, bootstrap procedures for non-Gaussian interaction limits, and inference for the projected spectrum, rank, subspaces, and information matrix. Simulations show that a raw first-stage statistic above 500 can support the wrong sign while projected diagnostics reveal weak valid information. In an international monetary application, common projection substantially attenuates apparent foreign-output persistence, while Gaussian-reference Anderson--Rubin sets remain unbounded. Identification should therefore be assessed after nuisance removal. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.03847 |
| By: | Nichole Austin; Sunny R. Karim; Erin Strumpf; Matthew D. Webb |
| Abstract: | Policies with a common objective and implementation date may differ in details or context. We distinguish the aggregate average treatment effect on the treated (ATT) from sub-aggregate ATTs defined by implementation cohort, jurisdiction, period, or policy type. UN-DID and DID-INT, two DiD estimators that construct jurisdiction-by-time effects, estimate these ATTs under parallel-trends conditions matched to the aggregation. In CPS placebo-law simulations, randomization inference is generally well-sized, though some jurisdiction-specific tests are conservative. The jackknife can be undefined for sub-aggregate ATTs; when defined, it over-rejects with few treated or comparison jurisdictions. Estimands and inference methods should match the policy question and implementation setting. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.01467 |
| By: | Dobrislav Dobrev; Ernst Schaumburg |
| Abstract: | This paper develops a model-free measurement and inference framework for high-frequency cross-market trading activity and provides empirical evidence of its importance in financial markets. We represent trading activity in two markets as temporal point processes and count the number of time bins in which both processes register activity at a given lag. Under the null of cross-process independence, without global stationarity, a Chen–Stein approximation yields Poisson convergence in total variation for this simple bin-based measure of cross-activity under mild regularity conditions. A local-stationarity framework for marginal intensities, allowing finitely many jumps, then yields a feasible blockwise estimator of the null Poisson mean. The resulting feasible asymptotic theory leads to three easy-to-implement statistical tests for independence at a given lag. It further yields consistent score-driven identification of dependence lags caused by lagged common components in market activity, characteristic of linked order executions across markets. This novel identification framework exploits that common components induce a singular line mass and Poisson score divergence at latency-determined dependence lags. Monte Carlo experiments with nonstationary superpositions of Hawkes processes confirm satisfactory test size, power, and lag-identification performance in finite samples. Empirical validation using transaction data for U.S. Treasury and equity cash-futures markets from 2010 to 2024 reveals sharply localized cross-activity dependencies at lags matching microwave latency, while not rejecting independence at more distant lags. High-frequency cross-market trading generally intensifies during market stress episodes and exhibits pronounced intraday surges after FOMC announcements, confirming its role in information propagation and efficient price discovery across linked markets. |
| Keywords: | High-frequency trading; cross-market activity; temporal point processes; nonstationarity; Chen–Stein method; Poisson approximation; lead–lag identification; market microstructure. |
| JEL: | C12 C14 C32 C41 C46 C58 G12 G14 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-011 |
| By: | Jean-Marie Dufour; Mame Astou Diouf |
| Abstract: | We study from a finite-sample viewpoint the problem of building tests and simultaneous confidence bands for cumulative distribution functions (CDFs), continuous or discrete. We emphasize procedures based on reweighted empirical distribution function (EDF) with shrinking bandwidths in the tails of the distribution. Since weighted statistics may have a problematic behavior when scaling factors are small (or zero), we propose to use regularized statistics. We consider a wide class set of modified EDF-type statistics, and give general characterizations of their distributions in the case of i.i.d. observations, so that the relevant distributions can be simulated in finite samples. We show that test criteria in the class studied can be implemented through the technique of Monte Carlo tests (MCT), so that the level is fully controlled in finite samples, irrespective of whether the tested distribution is continuous or discrete, without the need to establish an asymptotic distribution. Standard criteria such as the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), Eicker (E), and Berk– Jones (BJ) statistics are covered as special cases. Confidence bands are built by inverting sup-type goodness-of-fit test statistics. We show that the bands based on regularized AD-type and E-type statistics have closed forms which are especially easy to compute, without nonlinear optimization. For continuous variables, the null distributions of the statistics do not depend on the CDF tested. For noncontinuous distributions, we show that the MCT approach transparently controls test levels irrespective of the distribution tested. We also establish monotonicity properties (based on nesting image sets) and a general dominance result, so continuous critical values are valid (conservative) critical points. We show in Monte Carlo simulations that the proposed regularized goodness-of-fit tests and confidence bands are numerically tractable, reliable and yield power and precision improvements over standard procedures. The proposed methods are applied to the distribution of households’ consumption in Kenya. Nous étudions, dans une perspective d'échantillons finis, le problème de la construction de tests et de bandes de confiance simultanées pour les fonctions de distribution cumulées (FDC), qu'elles soient continues ou discrètes. Nous mettons l'accent sur les procédures basées sur la fonction de distribution empirique (FDE) repondérée, avec des largeurs de bande décroissantes dans les queues de la distribution. Étant donné que les statistiques pondérées peuvent présenter un comportement problématique lorsque les facteurs d'échelle sont faibles (ou nuls), nous proposons d'utiliser des statistiques régularisées. Nous considérons une vaste classe de statistiques de type EDF modifiées et donnons des caractérisations générales de leurs distributions dans le cas d’observations i.i.d., de sorte que les distributions concernées puissent être simulées dans des échantillons finis. Nous montrons que les critères de test de la classe étudiée peuvent être mis en œuvre à l’aide de la technique des tests de Monte Carlo (MCT), de sorte que le niveau de confiance soit entièrement contrôlé dans des échantillons finis, que la distribution testée soit continue ou discrète, sans qu’il soit nécessaire d’établir une distribution asymptotique. Les critères standard tels que les statistiques de Kolmogorov-Smirnov (KS), d’Anderson-Darling (AD), d’Eicker (E) et de Berk–Jones (BJ) sont traités comme des cas particuliers. Les intervalles de confiance sont construits en inversant les statistiques de test d’adéquation de type sup. Nous montrons que les bandes basées sur les statistiques régularisées de type AD et de type E ont des formes fermées particulièrement faciles à calculer, sans optimisation non linéaire. Pour les variables continues, les distributions nulles des statistiques ne dépendent pas de la fonction de distribution cumulative (FDC) testée. Pour les distributions non continues, nous montrons que l’approche MCT permet de contrôler de manière transparente le niveau de signification, quelle que soit la distribution testée. Nous établissons également des propriétés de monotonie (basées sur des ensembles d’images imbriquées) et un résultat général de dominance, de sorte que les valeurs critiques continues constituent des points critiques valides (conservateurs). Nous montrons, à l’aide de simulations de Monte Carlo, que les tests de bon ajustement régularisés et les intervalles de confiance proposés sont numériquement gérables, fiables et permettent d’améliorer la puissance et la précision par rapport aux procédures standard. Les méthodes proposées sont appliquées à la distribution de la consommation des ménages au Kenya. |
| Keywords: | Nonparametric inference, empirical distribution function, confidence band, regularization, weighted statistics, Kolmogorov-Smirnov statistic, Anderson-Darling statistic, Eicker statistic, Berk-Jones statistic, inférence non paramétrique, fonction de distribution empirique, intervalle de confiance, régularisation, statistiques pondérées, statistique de Kolmogorov-Smirnov, statistique d'Anderson-Darling, statistique d'Eicker, statistique de Berk-Jones |
| JEL: | C3 C12 C33 C15 G1 G12 G14 |
| Date: | 2026–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2026s-15 |
| By: | Irene Botosaru; James L. Powell |
| Abstract: | We study identification and estimation of moments of random coefficients in short linear panels, allowing the number of heterogeneous coefficients to exceed the number of equations observed for each unit. Under moment homogeneity, different regressor histories impose restrictions on the same moment vector. We give necessary and sufficient conditions for these restrictions to identify moments of a given order, stated in terms of the row spaces generated by the regressor support. The results show that moments may be identified even when the coefficients cannot be recovered for any individual, and, for two full-row-rank histories, give the exact loss of independent restrictions caused by overlap of their row spaces. When the support condition fails, we establish nonidentification in the maintained model and characterize the sharp identified set implied by these conditional moments. At second order, the identified set is determined by positive-semidefinite covariance restrictions and is also sharp relative to the full joint distribution of outcomes and regressors. Under the support condition, weighted minimum-distance estimators are root-$N$ asymptotically normal; under conditional nondegeneracy, oracle generalized-inverse weighting attains the Chamberlain (1987) efficiency bound for the maintained conditional-moment model. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.31085 |
| By: | Jean-Marie Dufour; Tianyu He |
| Abstract: | This paper proposes asymptotically distribution-free inference methods for comparing estimators which admit asymptotically linear Gaussian functional representations across dependent samples. The framework applies to a broad range of welfare indices used in inequality, poverty, and risk analysis. Two distinct situations are considered. First, we propose asymptotic and bootstrap in- tersection methods which are valid under arbitrary dependence between two samples. 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 meth- ods for comparing indices. We derive consistent estimates for asymptotic variances using the influ- ence function approach. We study the finite-sample performance of the proposed methods through Monte Carlo simulations and find that confidence intervals based on overlapping samples exhibit satisfactory coverage rates and reasonable precision. In contrast, conventional methods based on the assumption of independent samples perform poorly 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 fails to exist. The intersection method yields reliable results with arbitrary dependent samples, including settings in which the overlapping-sample assumptions do not hold. We demonstrate the practical applicability of our proposed methods in analyzing changes in household financial inequality in Italy over time. Cet article propose des méthodes d’inférence asymptotiquement indépendantes de la distribution pour comparer des estimateurs qui admettent des représentations fonctionnelles gaussiennes asymptotiquement linéaires sur des échantillons dépendants. Ce cadre s’applique à un large éventail d’indices de bien-être utilisés dans l’analyse des inégalités, de la pauvreté et des risques. Deux situations distinctes sont examinées. Premièrement, nous proposons des méthodes d’intersection asymptotiques et par bootstrap qui sont valables en présence d’une dépendance arbitraire entre deux échantillons. Ensuite, nous nous concentrons sur le cas courant des échantillons qui se chevauchent — une forme particulière d’échantillons dépendants où la dépendance résulte uniquement de paires appariées — et proposons des méthodes asymptotiques et de bootstrap pour comparer les indices. Nous dérivons des estimations cohérentes des variances asymptotiques à l’aide de l’approche par la fonction d’influence. Nous étudions les performances en échantillon fini des méthodes proposées à l’aide de simulations de Monte Carlo et constatons que les intervalles de confiance basés sur des échantillons chevauchants présentent des taux de couverture satisfaisants et une précision raisonnable. En revanche, les méthodes conventionnelles reposant sur l’hypothèse d’échantillons indépendants affichent de mauvaises performances en termes de taux de couverture et de largeurs d’intervalle. L’inférence asymptotique peut s’avérer moins fiable lorsqu’il s’agit de distributions à queues épaisses, tandis que la méthode du bootstrap offre une solution viable, à moins que la variance ne soit importante ou n’existe pas. La méthode d'intersection fournit des résultats fiables pour des échantillons dépendants arbitraires, y compris dans les cas où les hypothèses relatives au chevauchement des échantillons ne sont pas vérifiées. Nous démontrons l'applicabilité pratique des méthodes que nous proposons en analysant l'évolution des inégalités financières entre les ménages en Italie au fil du temps. |
| Keywords: | inequality measures, poverty measures, influence function, asymptotic inference, intersection method, bootstrap, confidence interval, overlapping samples, dependent samples, mesures d'inégalité, mesures de pauvreté, fonction d'influence, inférence asymptotique, méthode d'intersection, bootstrap, intervalle de confiance, échantillons chevauchants, échantillons dépendants |
| JEL: | C01 C1 C12 C14 C15 D6 D63 G5 I3 I32 |
| Date: | 2026–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2026s-14 |
| By: | Vance Martin; Yoshihiko Nishiyama; John Stachurski; Yiran Xie |
| Abstract: | We introduce a new density-based goodness of fit test for ergodic Markov processes. Our test compares the data against the class of models specified in the null hypothesis, and rejects if no model in the class yields a stationary density that matches with the data. No alternative needs to be specified in order to implement the test. Although our test compares densities, estimation of smoothing parameters is not required, and the test has nontrivial power against $1/\sqrt{n}$ local alternatives. The test provides new perspectives on some existing problems in econometric and financial modeling. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.03088 |
| By: | Sahil Loomba; Dean Eckles |
| Abstract: | In the presence of interference, where the treatment assigned to one unit can affect the outcomes of others, many causal estimands depend on the treatment-assignment policy under which the experiment is conducted. This policy dependence creates a fundamental challenge for off-policy estimation, where the goal is to estimate causal quantities under a hypothetical intervention policy different from the one used to collect data. We study this problem of off-policy estimation of causal effects for heterogeneous Bernoulli policies. By representing exposure-weighted potential outcomes in the biased Fourier basis of the experimental design, we construct, for any prespecified Fourier subspace encoding the assumed interference structure, the unique minimum-$L^2$ weight that transports every function in that subspace. Global and local inverse-probability weights, linear-interference weights, and no-interference weights are special cases. The weight variance is a structured chi-square distance between the experiment and target policies. When the assumed interference structure is misspecified, the introduced bias couples the omitted outcome spectrum with the corresponding policy-shift coefficients, yielding a sharp robustness bound and a bias-variance trade-off. A Fourier-neighborhood-overlap condition gives consistency under structured interference, and we state a Doob-martingale central limit theorem for off-policy estimators. As the variance is not identified, we derive identifiable bounds and associated conservative estimators of the variance. Simulations illustrate these theoretical results for the design and analysis of experiments under network interference and design mismatch. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.02756 |
| By: | Dawis Kim; Tao Zha |
| Abstract: | We develop a unified framework that combines shock volatility with sign and narrative restrictions and provides the theoretical foundation for the computationally efficient sampler HARS. HARS preserves the heteroskedastic likelihood and can be combined with any posterior simulator for the heteroskedastic model. In monetary policy, oil market, and fiscal policy models, the same restrictions deliver substantively different economics once shock heteroskedasticity is accounted for. Homoskedastic SVARs put uncertainty in the wrong place, pushing shock-scale variation into impulse-response uncertainty. Heteroskedasticity sharpens dynamic responses, alters economic conclusions, and restores 90% credible intervals as a practical standard for economic inference. |
| JEL: | C11 C32 E52 E62 Q43 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35483 |
| By: | Nathan Canen; Ted Enamorado |
| Abstract: | Prediction-based methods, including Large Language Models (LLMs) and other machine learning techniques, are often used to construct measures of political phenomena that are difficult to quantify directly, such as policy positions in manifestos or emotions expressed on social media. In many applications, these prediction-generated measures are used as explanatory variables in regression models, even though they are measured with error. This leads to biased estimates. In this paper, we propose a simple solution to these biases: instrumental variables constructed from multiple measures created on independent splits of the original data. This approach is theoretically valid, easy to implement, and does not require new data. Through simulations, we show that this approach recovers estimates close to the true values, even in relatively small samples, while the standard approach can produce substantial bias in practice. We illustrate the method by revisiting two applications: whether gendered speech affects legislative outcomes in the German Parliament, and whether political risk influences poverty alleviation programs in China. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.02909 |
| By: | Florian Huber; Aubrey Poon; Dan Zhu |
| Abstract: | We develop a hierarchical Bayesian panel quantile regression model in which unit-specific coefficient paths are smoothed across quantiles by Gaussian processes, while a common time effect absorbs aggregate shocks. Componentwise-monotone Bernstein polynomials, perturbed by unit-specific deviations, deliver soft noncrossing, and we provide identification conditions together with a bound on the crossing probability. Applying the model to 33 countries over 1979--2023, we find that global temperature shocks generate a systemic, non-diversifiable downside risk to output growth. This risk is concentrated in the lower tail and disproportionately affects emerging markets. Finally, we apply our framework to risk analysis and show that the model reduces out-of-sample tail-risk forecast loss by roughly one-third relative to country-specific quantile regressions. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04664 |
| By: | Alejandro Puerta-Cuartas |
| Abstract: | Measuring the intergenerational transmission of lifetime economic status is complicated by researchers often only observing snapshots of income at specific ages. Consequently, standard practice estimates intergenerational mobility using income averages, introducing life-cycle bias that compromises reliability and comparability across studies, time, and place. I develop a missing data framework that exploits available income data and observable characteristics to eliminate life-cycle bias. This method combines nonparametric identification with Neyman-orthogonal moments to construct debiased machine learning estimators for intergenerational income mobility measures under plausible missing-at-random and testable independence assumptions. I apply this framework to estimate the intergenerational elasticity for the U.S. using the Panel Study of Income Dynamics across birth cohorts from 1954 to 1977 with rolling 10-year windows. While existing approaches estimate values between 0.41 and 0.54, the proposed method yields substantially higher estimates ranging from 0.6 to 0.7, averaging 0.64. These results align closely with recent evidence using long time averages over mid-career periods, reinforcing high U.S. intergenerational persistence. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04994 |
| By: | Todd Clark; Florian Huber |
| Abstract: | Estimating the dynamic effects of economic shocks in short and very short samples is impeded by a lack of degrees of freedom. We offer a solution based on a Bayesian hierarchical framework for estimating local projection (LP) impulse response functions across a panel of related time series. The framework explicitly accommodates unbalanced panels in which some series are substantially shorter than others, allowing the short series to borrow information from longer ones at horizons where the short series carry little or no own data. Since series might exhibit heterogeneous dynamics, we develop a sparse finite mixture pool that clusters units by similarity of their impulse response profiles. We show in simulations that our approach substantially improves LP estimation accuracy relative to the standard approach if the time series are short while producing similar LPs for longer time series. Using a US price dataset, augmented with survey responses, we find that supply-chain and oil shocks trigger heterogeneous reactions of different price measures, with headline price indices responding more sharply than their core counterparts and goods prices changing more than services prices. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04631 |
| By: | Bob Wilson |
| Abstract: | We give an exact randomization-based confidence set for the average treatment effect (ATE) in matched-pair studies with a binary outcome, requiring neither monotonicity nor any distributional assumption beyond the within-pair coin flip. At its core is an analytic solution to the worst-case allocation of attributable effects: two binomial-symmetry lemmas identify the pattern hardest to reject as a single boundary corner, so testing null hypotheses needs no integer program and no numerical search. Inverting the test via binary search yields a prediction set for the attributable effect in O(log S) Binomial tail calculations; the Bonferroni proposition of Rigdon and Hudgens (2015) produces the ATE confidence set at the same computational cost. The same corner extends without further machinery to a sensitivity analysis for matched observational studies under Rosenbaum's $\Gamma$-model. A simple formula for the design sensitivity illuminates when an observational study can hope to provide evidence for an effect. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.03227 |
| By: | Sara Casella; Jesús Fernández-Villaverde; Stephen Hansen; Ryohei Oishi; Minchul Shin |
| Abstract: | Standard macroeconomic data do not cleanly separate the systematic and nonsystematic components of monetary policy. We show that incorporating unstructured text data into the structural estimation of a DSGE model can sharpen this distinction. We augment a standard state-space model with a non-core measurement block that links structural shocks to time series derived from FOMC transcripts, using a spike-and-slab prior to let the data select which series are informative. In a medium-scale New Keynesian model for the U.S., incorporating text improves predictive performance and materially alters structural inference: the new model estimates a lower response of the policy rate to inflation, higher price stickiness and lower price indexation, implying a flatter and less backward-looking price Phillips curve. |
| JEL: | C11 C32 C55 E37 E52 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35487 |
| By: | Serena Ng; Nikolay Gospodinov |
| Abstract: | Many empirical investigations of long-run relations are based on cross-section regressions in averaged or long differenced data that effectively have the time dimension of a T × N panel compressed. We analyze a class of time-compressed I(1) data and show that they have magnified variability stemming from the fact that the cross-section variance of a non-stationary panel ‘fans out’ with time. Cross-section regressions in time compressed data can potentially yield estimates that are super-consistent and asymptotically normal, whether the regressors are stationary, non-stationary, or highly persistent. The fastest convergence rate of √NT requires a compression scheme that not only magnifies the non-stationary signal, but also dilutes the regression noise. Omitted fixed effects preclude noise dilution but the estimates remain superconsistent. However, the fanning out effect can be weakened when the data have a strong force for mean-reversion or convergence, a problem that seems relevant for temperature data. We consider three applications and find that the long-run relation between consumption and income, and between growth/inflation and demographic variables are reasonably well determined, but the estimated relation between growth and warming temperature is fragile. |
| JEL: | C01 O5 Q5 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35700 |
| By: | Daniyal Ali Hameedi |
| Abstract: | In this paper we propose a new formulation of the Bayesian Filter as used in the discrete-time Markov-Switching-Multifractal (MSM) model of volatility based on existing permutation symmetry within the likelihood structure. We show both analytically and empirically that such a formulation leads to a reduction in time complexity from $O(D^k)$ to $O(k^D)$ thereby significantly reducing the computational bottleneck associated with dimensionality. We compare the agreement between the naive and sector filters and find that while there are significant disagreements, the ground-truth recovery of the latter seems to improve on the former. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22864 |
| By: | Christian Bongiorno; Lorenzo Villassero |
| Abstract: | Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substantial fraction of the available information. Pairwise-complete estimation preserves the longest overlap for each asset pair, but the resulting correlation matrix can be indefinite because its entries are computed on different samples. This prevents direct use in Markowitz optimization and falls outside the assumptions of standard random-matrix shrinkage. We adapt a rotation-invariant neural covariance estimator to this setting. The model computes mask-aware marginal moments and a pairwise correlation matrix proxy, processes its signed spectrum, and uses a bidirectional gated recurrent unit conditioned on factor-aligned effective sample lengths derived from the overlap matrix and eigenvector loadings. It maps all eigenvalues, including negative ones, to a positive inverse spectrum. The reconstructed covariance is positive definite and is trained end-to-end to minimize five-day realized global-minimum-variance risk. We evaluate 26 expanding-window models from 2000 to 2025 on up to 1, 500 U.S. equities in a closing-auction simulator with point-in-time selection, commissions, financing, corporate actions, and market impact. Across the 26-year out-of-sample period, the neural estimator reduces annualized five-day volatility by approximately 20\% and increases the Sharpe ratio by approximately 40\% relative to the next-best covariance estimator. These improvements are consistent across realized risk, risk-adjusted performance, and drawdown control, remain after the modeled execution frictions, and are supported by a 99.9\% Model Confidence Set that retains only the neural estimator. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.30446 |
| By: | Canavire Bacarreza, Gustavo (World Bank); Puerta-Cuartas, Alejandro (Banco de España); Rodriguez Castelan, Carlos (World Bank); Velez-Ospina, Carolina (World Bank) |
| Abstract: | This paper proposes a nonparametric simulation framework to estimate poverty vulnerability under climate shocks. We formalize vulnerability estimation as an out-of-sample prediction problem and show that flexible, regularized machine learning methods for estimating the conditional mean of welfare offer a powerful alternative to conventional linear models. The framework simulates future welfare distributions using historical realizations of climate shocks and household characteristics, enabling the estimation of vulnerability measures and related functions without imposing restrictive parametric assumptions. To interpret the model and quantify heterogeneous impacts, we employ SHapley Additive exPlanations, which decompose predicted vulnerability into contributions from climate shocks and household characteristics. An application to Ecuador reveals a strong geographic concentration of vulnerability and shows that climate shocks act as localized triggers that push marginal households, particularly low-educated informal rural workers into poverty. |
| Keywords: | Poverty Vulnerability, Climate Shocks, Machine Learning. |
| JEL: | I32 I38 C14 C15 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18893 |
| By: | David Tan |
| Abstract: | Claims of the form "we find no evidence that X affects Y" appear throughout the applied finance literature, yet whether such a claim contains evidence of absence or absence of evidence depends entirely on its confidence interval. The term "statistically insignificant" is routinely read to mean zero economic effect. However, a more honest description is that zero could not be rejected along with a range of other coefficient effect sizes. The crucial question is whether effect sizes in that range are consequential. This note distinguishes two kinds of insignificant results that are indistinguishable in a standard regression table: bounded null claims where the intervals reject effect sizes of consequence and thus represent a genuine finding, and vacuous null claims where even consequential effects remain unrejected and therefore establish nothing. I propose a minimal reporting standard for regression results in applied finance, where the smallest consequential effect size is stated (in the units of the decision, per a named increment of the regressor) alongside the descriptive statistics and compared with the relevant edges of the confidence intervals of null claims. Using only the reported coefficient and standard error, authors can distinguish bounded (informative) null claims that reject consequential effect sizes from vacuous null claims that establish no information, perhaps due to deficiencies in data or the identification strategy. The symmetric phrase "no effect" conceals, in particular, the frequent split verdict: bounded in one direction, vacuous in the other. In ongoing work, I apply this framework to published null claims in leading finance journals, beginning with my own. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.30490 |
| By: | Naveed Javed; James Morley |
| Abstract: | We consider empirically how fiscal stance influences the potency of monetary policy. New Zealand provides a compelling laboratory to study this form of monetary-fiscal interaction given its stable history of inflation targeting and substantial changes in its fiscal stance due to global forces acting on a small open economy (SOE). A simulation-based Bayesian Local Projection (BLP) framework is developed to estimate the macroeconomic effects of a narrative measure of monetary policy shocks when there are many possible omitted variables, especially in this SOE setting. Our BLP approach incorporates a novel shape prior on impulse response functions to help manage the substantial bias-efficiency tradeoffs given the relatively small effective sample sizes when considering nonlinearities inherent in policy interactions. For a smooth-transition regime-switching model with an endogenously-estimated threshold parameter, we find that monetary policy is clearly more potent, especially in terms of out-put and inflation, when there is a high degree of fiscal consolidation. Consistent with the fiscal theory of the price level, our results for a more general model that also allows for sign asymmetries suggest the effects of expansionary versus contractionary monetary policy shocks on output, inflation, and the exchange rate are actually reasonably symmetric, implying that the potency of monetary policy is more related to monetary-fiscal dominance than coordination. |
| Keywords: | monetary-fiscal interactions, Bayesian local projections, fiscal theory of the price level |
| JEL: | C32 E52 E58 E63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-75 |
| By: | Tobias Adrian; Domenico Giannone; Matteo Luciani; Mike West |
| Abstract: | Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation. Treating baseline forecasts and scenarios as conditional predictive densities, and distributional forecasts as reference predictive distributions, places both within a common framework and clarifies their roles. The Scenario Synthesis assigns weights to scenarios consistent with the reference distribution, offering a practical and reproducible tool for risk assessment and policy deliberation under deep uncertainty. |
| Keywords: | scenarios; fan charts; growth-at-risk; model uncertainty; Bayesian predictive synthesis |
| JEL: | C1 C11 C53 E32 E37 E58 |
| Date: | 2026–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103751 |
| By: | Marcus Buckmann (Bank of England); Galina Potjagailo (Bank of England) |
| Abstract: | This paper discusses how economic theory can be integrated into machine learning (ML) models to enhance their interpretability and applicability for policy analysis. While ML methods offer considerable flexibility and strong predictive performance, they are often criticised for their 'black box' nature and lack of economic transparency. A growing body of research addresses this limitation by introducing structure into ML models − most notably through Block-Additive Models (BAMs) and theory-consistent monotonicity constraints. BAMs group predictors into economically meaningful blocks and impose additivity across blocks, while permitting non-linearities and interactions within them. This architecture enables clear attribution of each block’s contribution to the model’s predictions. Monotonicity constraints further improve interpretability by aligning the model’s directional responses with economic theory, allowing for the separation of opposing effects − such as distinguishing between supply- and demand-driven components of inflation. Empirical evidence shows that these structured ML approaches retain strong predictive performance while yielding economically meaningful narratives. |
| Keywords: | Interpretable machine learning;theory-aligned constraints;macroeconomic analysis |
| JEL: | C10 C14 C53 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023266 |