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on Econometrics |
| By: | Irene Botosaru; Isaac Loh; Chris Muris |
| Abstract: | We develop a framework for identification, computation, and inference in econometric models with a linear-in-measures representation. These models express maintained restrictions as moment conditions linear in the joint probability measure of observed and latent inputs, and map that measure linearly to the distribution of outputs, even with nonlinear outcome equations. We construct an adversarial discrepancy function whose zeros characterize the identified set for structural and counterfactual parameters. With finite output support, finite linear programs compute the discrepancy function or provide certified bounds even when latent inputs have infinite support, and a penalized bootstrap yields confidence sets with uniform per-point coverage. We apply the framework to two open cases in binary choice panels with fixed effects and discrete covariates: sequential exogeneity with unspecified conditional marginal error distributions, and known conditional marginal error distributions with unrestricted serial dependence. In an entry game with multiple equilibria, the framework recovers the known sharp identification region. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.32076 |
| By: | 本田, 敏雄; HONDA, Toshio; ホンダ, トシオ |
| Keywords: | ATE, ATT, high-dimensional covariates, double robustness, asymptotic normality, confidence intervals, working models, generalized additive models, generalized varying coefficient models, B-spline basis, group Lasso |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:hit:econdp:2026-03 |
| By: | Hiroyuki Kasahara; Hirokazu Matsuyama; Katsumi Shimotsu; Shota Takeishi |
| Abstract: | Ignoring unobserved heterogeneity in duration models biases parameter estimates and invalidates inference, but testing for it is non-regular: the null hypothesis lies on the boundary of the parameter space and some parameters are unidentified under the null. These features render standard asymptotic theory inapplicable. This paper develops an EM test for unobserved heterogeneity in censored Weibull duration models, building on the EM approach of Li, Chen, and Marriott (2009). The test statistic has an asymptotic null distribution equal to the square of max{0, N(0, 1)}, hence critical values require neither simulation nor bootstrap, and the test accommodates covariate-dependent censoring of arbitrary form. Monte Carlo simulations compare the EM test with the likelihood ratio test (LRT) of Cho and White (2010), information matrix tests, and Lagrange multiplier tests. The EM test has empirical size close to the nominal level for sample sizes of 500 or more, where the LRT remains markedly conservative and the other tests over-reject. Its size-adjusted power is comparable to that of the LRT and higher than that of the other tests. Because size adjustment requires knowledge of the data-generating process and is unavailable in practice, the EM test attains higher power than the LRT in most designs as the tests would actually be applied. In an application to the Stanford Heart Transplant data, the EM test rejects homogeneity in every covariate specification, whereas the LRT's conclusion depends on a user-chosen set of admissible parameter values and on the specification. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.36824 |
| By: | Charisios Grivas (Aalborg University); Zacharias Psradakis (Birkbeck Business School); Marian Vavra (National Bank of Slovakia) |
| Abstract: | This paper considers tests for skewness, kurtosis and normality of the one-dimensional marginal distribution of a stationary fractional process, which may exhibit short memory, antipersistence or long memory. The autoregressive-sieve bootstrap is used to obtain Pvalues and critical values for tests based on studentized empirical coefficients of skewness and kurtosis. These studentized coefficients are constructed using variance estimators obtained by the same bootstrap procedure. Notably, such tests, as well as variants based on non-studentized statistics, can be implemented in a straightforward manner without requiring knowledge or estimation of the memory parameter of the process. Monte Carlo simulations show that the bootstrap-assisted tests perform well for sample sizes that are typical in applied settings. The methodology is further illustrated through an application to realized measures of stock-return volatility. |
| JEL: | C12 C22 C63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:svk:wpaper:1146 |
| By: | Mikihito Nishi; Takashi Yamagata |
| Abstract: | This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid two-point wild bootstrap as the serially independent case. The induced multiplier autocovariances determine the lag weights in a corresponding heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator, which coincides exactly with the conditional covariance of the bootstrap estimating-equation sum. We establish first-order bootstrap validity for asymptotically linear estimators by showing that the matched-HAC estimator consistently estimates the long-run covariance and that the bootstrap estimating-equation sum converges conditionally to the same Gaussian limit as its original-sample counterpart, yielding valid HAC-studentized $z$-tests and the corresponding Wald and Lagrange multiplier tests. Monte Carlo experiments in nonlinear generalized method of moments (GMM) and linear regression show that Rademacher DWB generally provides more accurate finite-sample size control for $z$-tests than the Mammen and Gaussian DWB. A GMM application to a nonlinear short-rate mean-reversion model illustrates the practical relevance of the proposed two-point DWB. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.32369 |
| By: | Graetz, Georg (Department of Economics, Uppsala University); Nordin, Mattias (Department of Statistics, Uppsala University) |
| Abstract: | We present a new approach to nonparametric inference in regression discontinuity designs where the running variable is discrete and may have few support points. The approach is based on an intuitive and comparatively weak identification assumption: In the absence of treatment, the difference in population mean outcomes at the cutoff is not the largest nor the smallest among differences at any two adjacent support points. Test statistic and confidence interval are constructed from a set of t-statistics based on double differences of the outcome variable, eliminating the need to estimate a conditional expectation function. Simulation evidence suggests that statistical power compares favorably to existing methods. We illustrate our approach by revisiting two studies from the economics of education literature. |
| Keywords: | Regression discontinuity design; causal inference; policy evaluation; economics of education |
| JEL: | C12 C13 C14 I21 |
| Date: | 2026–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ifauwp:2026_019 |
| By: | Jinyuan Chang; Jing He; Weiming Li; Chen Lin |
| Abstract: | We propose a new $L_2$-type test for white noise which allows the dimension $p$ of the time series to either (i) be a fixed constant, or (ii) diverge with the sample size $n$. The proposed test statistic exhibits an interesting phase transition, following two different regimes of behavior: $p$ is fixed, and $p\rightarrow\infty$. Because identification of the operable regime is difficult, if not impossible in practice, we devise a novel adaptive bootstrap method to construct unified testing procedure across different phases. Numerical experiments confirm the good finite sample performance of the proposed adaptive $L_2$-type test in comparison to the existing methods in the literature. The proposed testing procedure has been implemented in R package HDTSA. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.33418 |
| By: | Fernando Roders (a.s.r. Asset Management); Siem Jan Koopman (Vrije Universiteit Amsterdam); Karim Moussa (Vrije Universiteit Amsterdam) |
| Abstract: | In this study, we propose to estimate the parameters of stochastic volatility models with stable disturbances using methods based on linearization. Specifically, we obtain a linear state space form of the model by taking the logarithm of the squared financial returns, which, together with analytical expressions for logarithmic and fractional lower order moments of the stable distribution, forms the basis of the proposed quasi-maximum likelihood and generalized method of moments estimators. The methods are straightforward to implement and do not require simulation. We provide details of the implementation and study the finite-sample performance of the two estimation methods in a Monte Carlo study. The empirical relevance is illustrated by studying model-implied volatility estimates from time series of daily returns from stock indices, exchange rates, and cryptocurrencies. |
| JEL: | C32 C58 G15 |
| Date: | 2026–10–05 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260082 |
| By: | Yaroslav Korobka; Vira Semenova |
| Abstract: | We study estimation and inference for a partially identified parameter whose identified set depends on a first-stage nuisance parameter that must itself be estimated. Combining the criterion-function approach with the theory of Neyman-orthogonal moments that underlies double/debiased machine learning, we propose a two-step procedure: the point-identified nuisance is estimated by flexible machine-learning methods, and the set-identified target is recovered as a level set of a sample criterion built from orthogonal moment inequalities with cross-fitting. When the contour level is bounded, we show that the resulting set estimator converges in Hausdorff distance at the parametric rate of the infeasible criterion built on the true nuisance. We further develop a subsampling procedure that delivers asymptotically valid coverage, provided the product of the first-stage estimation errors is $o(N^{-1/2})$. We illustrate the method on bounds for the wage distribution and the interquantile range under selection into employment and on the gender wage gap with an interval-censored wage. The empirical application studies the gender wage gap using the March supplement of the 2015 Current Population Survey. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.37412 |
| By: | Tomoki Toyabe (Kanazawa Gakuin University); Takahiro Hoshino (Keio University); Taisuke Otsu (London School of Economics and Political Science) |
| Abstract: | We study survival analysis with positive–unlabelled event-time data, where censoring times and covariates are observed for all subjects but event times only for labelled positives. Under conditionally independent censoring and allowing the labelling probability to depend on censoring time and covariates, conditioning on these variables eliminates the unknown labelling probability from the event-time density of labelled positives. We show that this conditional density identifies the reverse hazard and the relative event-time distribution over the observed follow-up range, while absolute event probabilities require additional information or restrictions. We develop parametric and semiparametric estimators based on the conditional likelihood and establish their large-sample properties. In regular parametric models, the estimator is semiparametrically efficient when the labelling mechanism is locally unrestricted. We also show that information about scale parameters can vanish under limited follow-up and propose a sensitivity analysis for event-timedependent labelling. In simulations, we examine finite-sample performance under varying follow-up and label selection. In an empirical application, we study the time from clinical-trial completion to the first results disclosure recorded through a registry posting or qualifying journal publication. |
| Keywords: | conditional likelihood; positive–unlabelled learning; reverse hazard; semiparametric efficiency |
| JEL: | C41 C14 C24 |
| Date: | 2026–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2026-021 |
| By: | Anna B. Schmidt |
| Abstract: | This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from information across categories and improves its predictive performance as the dimensionality of the choice environment increases, whereas the mixed logit model does not exhibit the same pattern. MCMC delivers the highest predictive accuracy but is computationally intensive. VI achieves slightly lower predictive performance while substantially reducing runtime. In the empirical application, VI also outperforms the mixed logit benchmark. These findings highlight a trade-off between accuracy and computational feasibility in multi-category demand estimation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08409 |
| By: | Joachim Freyberger; Julius Kappenberg |
| Abstract: | Economic theory frequently implies linear inequality restrictions on parameters or functions of interest. A common way to impose such restrictions is to project an unrestricted estimator onto the feasible set. Projection estimators arise naturally from constrained least squares, instrumental variables, generalized method of moments, maximum likelihood, and related extremum procedures. When the sampling covariance, loss function, and projection criterion induce different geometries, the choice of projection geometry can substantially affect risk. We study this choice in a fixed-dimensional local Gaussian experiment under quadratic loss. At exact-boundary configurations where only one maintained inequality binds, inverse-covariance projection is pointwise optimal. When at most two inequalities are locally relevant, it weakly improves on the unrestricted estimator throughout the corresponding local experiment and is minimax over exact-boundary configurations. For an arbitrary number of inequalities, we provide a sufficient condition for boundary minimaxity, but show by counterexample that inverse-covariance projection need not be boundary minimax once three inequalities can bind. Motivated by these results, we propose selecting the projection geometry to minimize worst-case boundary risk subject to a local no-harm condition relative to unrestricted estimation. We develop a feasible implementation and study its finite-sample performance in simulations and an application to gasoline demand. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.32725 |
| By: | Ulrich Hounyo |
| Abstract: | Staggered distributional difference-in-differences produces cohort-specific potential-outcome distributions, but applied work typically wants one overall quantile treatment effect. Two natural summaries---averaging cohort quantile treatment effects (QTTs) and mixing cohort distributions before inversion---use the same policy weights yet answer different target-population questions and can disagree even in sign. We derive the exact sharp interval for their gap conditional on cohort quantiles and weights, a globally sharp range-only envelope, and a locally sharp density-tilt representation. We develop joint smooth and mass-point-safe inference and show that neither estimator is uniformly more precise, even when the estimands coincide. In a same-object reconstruction of a public staggered-QTT application, holding data, identification, distributions, and weights fixed while changing only aggregation order reverses reported signs at several quantiles. Aggregation order is therefore part of the estimand and must be chosen before inversion. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27944 |
| By: | Karolina Gliszczy\'nska-Schroeder |
| Abstract: | We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method, we introduce a two-step, model-agnostic approach that allows any suitable machine learning method to be used for the CCACE estimation in the first step. Specifically, we implement two non-Bayesian tree-based methods, both using forest-based learners: DRRF-IV, a debiased transformed-outcome regression-forest approach, and a GRF-based IV adaptation of the generalized random forest framework (Athey et al., 2019). Through a simulation study, we assess the precision, bias, and the ability to correctly identify the underlying subgroup structure of the proposed methods relative to BCF-IV. The results show that the non-Bayesian methods perform competitively across the considered simulation settings, with performance improving for larger sample sizes and moderately large treatment effects, while reducing computational runtime. We apply our new methods by revisiting an empirical study that examines the effect of prompt admission to intensive care units (ICU) on 28-day mortality across 48 UK National Health Service hospitals. While previous work finds no significant overall treatment effect, we investigate whether subgroups of patients may benefit more from prompt ICU admission. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.31769 |
| By: | Black, Nicole (Monash University); Ma, Bernice (Monash University); Mai, D Tung (University of Melbourne); Skeels, Christopher (University of Melbourne); Staub, Kevin (University of Melbourne) |
| Abstract: | We develop a fixed-effects estimation framework for fractional-outcome panel models with a logit conditional mean. Pairwise quasi-differencing eliminates individual effects from the moment conditions, yielding a generalized method of moments estimator that is consistent under the stated moment restrictions even when the number of time period is finite and fixed. The framework allows unrestricted dependence between individual effects and covariates, accommodates endogenous regressors through instrumental variables, and extends to multivariate shares that sum to one. Boundary observations require no adjustment, and average semi-elasticities can be estimated without recovering the individual effects. We apply the framework to Australia's National Disability Insurance Scheme (NDIS), using HILDA (Household, Income and Labour Dynamics in Australia) survey data for 2011-2023 and instruments based on the scheme's staggered geographic rollout. We examine how NDIS receipt affects the allocation of time across paid work, housework, disability care, and remaining activities for recipients and other household members. The estimates indicate that recipients reduce work and disability care, while other household members increase disability care. |
| Keywords: | panel data, fixed effects, fractional responses, share data, time use, disability support |
| JEL: | C23 C24 I18 J22 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18969 |
| By: | Bernard Herskovic; Gill Segal |
| Abstract: | Time fixed effects are not a general-equilibrium control. In network economies, aggregate shocks do not enter firm outcomes uniformly: they propagate through equilibrium prices, costs, and demand, leaving a residual network gradient after time demeaning. When an instrument, treatment, or exposure design even partially aligns with this gradient, standard panel estimators recover the direct effect plus an endogenous network-leakage component. We develop a tractable framework that signs and quantifies this Network-SUTVA bias in closed form, showing how network topology governs its magnitude. Weak instruments magnify the leakage. Difference-in-differences designs are similarly contaminated because controls can be untreated by assignment but treated in equilibrium. Calibrated to the U.S. production network, the distortion can exceed 130% of the true direct effect. We propose observed- and hidden-network diagnostics and remedies, and test them on canonical difference-in-differences designs, showing that standard estimators can confound direct policy responses with systemic network leakage. |
| JEL: | C23 D85 G30 L14 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35807 |
| By: | Marcelo J. Moreira; Whitney K. Newey; Mahrad Sharifvaghefi |
| Abstract: | This paper characterizes the global minimum of the continuously updating generalized method of moments (CU-GMM) objective in linear instrumental variables models. We allow optimal weighting matrices under heteroskedasticity, autocorrelation, or clustering. We show that the objective is a ratio of polynomials. For one endogenous regressor, stationary points of CU-GMM objective function are real eigenvalues of a companion matrix. Comparing their objective values with the value at infinity gives the global minimum, extending the classical eigenvector approach to limited information maximum likelihood. With multiple endogenous regressors, algebraic elimination and checks for real solutions identify the minimum among finitely many candidate objective values, including boundary values. Galois theory rules out general formulas by radicals even with one endogenous regressor and two instruments, while numerical root finding remains possible. Finding the global minimum allows us to compute overidentification and likelihood ratio tests, including the conditional likelihood ratio (CLR) test. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.36445 |
| By: | Julien Monardo |
| Abstract: | The answers to many economic questions depend on the slope and curvature of demand. Estimating demand for differentiated products entails a trade-off between flexibility and scalability. Existing nonparametric approaches face a curse of dimensionality in the number of products. I develop a scalable nonparametric approach using market-level data that overcomes this curse through economically motivated restrictions embedded in many standard models. Simulations and an application to the U.S. beer market demonstrate flexibility in demand slope and curvature, with consequential differences in counterfactual price responses. Estimation in the application takes seconds, versus several hours for a random coefficient nested logit model. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08467 |
| By: | Michael Pfarrhofer; Anna Stelzer |
| Abstract: | We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macro-financial variables with quarterly bank lending survey data, and identifies the dynamic responses from high-frequency policy surprises. Sign asymmetry dominates; a tightening produces monetary policy transmission mostly in line with the theoretical predictions, whereas easing of any size produces mostly insignificant responses. Peak effects vary with initial conditions, while several common, predefined regime splits do not yield considerable differences in the propagation of the shocks. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.37168 |
| By: | Justin Whitehouse; William Betz; Yan Zhang; Peter Coles; Ramesh Johari; Vasilis Syrgkanis |
| Abstract: | Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer purchasing behavior reveal possession of a gift card only when a customer makes a purchase, thus leaving a customer's treatment status systematically censored. In this paper, we develop a novel data fusion approach to overcome this missing data challenge. We identify and estimate incrementality by combining a large observational dataset with a smaller experimental dataset from a different population. Our approach relies on a mild transferability condition, which posits that the conditional relative treatment effect of gift card receipt on the decision to purchase is invariant across the two populations. We develop a flexible, machine learning-based estimator for the incremental revenue and establish its asymptotic normality. We apply our estimator across both first- and third-party channels through which Airbnb distributes gift cards, finding heterogeneity in incrementality across segments of the population. In particular, we find not only that third-party channels are more incremental than first-party ones, but also that "self-gifters" (i.e., customers likely to have purchased their own gift cards) are more incremental than the broader population. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08558 |
| By: | Feras A. Saad; Todd B. Walker |
| Abstract: | Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.36280 |
| By: | Shunsuke Imai; Koshi Nishida; Yuta Okamoto |
| Abstract: | Many policies assign treatment according to whether a score crosses a cutoff. Regression discontinuity (RD) designs identify the effect of treatment assignment, but the threshold policy itself may also reshape individuals' incentives, inducing behavioral responses that affect outcomes even holding treatment status fixed---a channel that conventional RD designs cannot capture. We develop a framework that exploits randomized variation in policy thresholds, together with rank-invariance-type restrictions, to identify the treatment-assignment and incentive-response effects. We illustrate the empirical relevance of this distinction using data from a merit-based scholarship experiment in Malawi. In this illustration, the conventional RD estimate is positive, while the incentive-response effect is negative and more than twice as large in magnitude. These findings suggest that threshold policies may generate unintended adverse behavioral responses, potentially reflecting discouragement induced by demanding thresholds, and caution against relying solely on conventional RD estimates when evaluating threshold policies. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.02938 |
| By: | Virginie Terraza (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier, uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg); Aslı Boru İpek (Kütahya Dumlupinar University = Kütahya Dumlupınar Üniversitesi [Kütahya, Turquie]) |
| Abstract: | This study proposes a multi-stage approach to modelling dependencies using Vector Autoregression (VAR), Nonlinear Autoregressive Neural Network (NAR-NN) models, and copulas. This paper aims to assess the dynamic dependency structure between cryptocurrencies and traditional financial assets, considering regime changes and safe-haven properties. The proposed methodology integrates change point detection with copula-based conditional correlation Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to identify structural breaks and nonlinear dependencies between regimes. A sequential change point procedure based on fast signal segmentation is employed to detect structural changes. According to this study, cryptocurrencies and traditional assets exhibit regimedependent dependence. It was also determined that during periods of market stress, this dependency significantly increases. This paper has significant implications for portfolio diversification, risk management, and the role of digital assets in financial markets. Furthermore, this proposed methodology contributes to the literature by capturing nonlinear, time-dependent, and regime-dependent dependency structures. |
| Keywords: | 62M10, 68T07, neural network MSC: 91G15, DCC-GARCH family models, VAR, copula, change point detection, financial markets, interconnections, interconnections financial markets change point detection copula VAR DCC-GARCH family models neural network MSC: 91G15 62M10 68T07 |
| Date: | 2026–09–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05752791 |