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on Econometrics |
| By: | Weisheng Zhang |
| Abstract: | I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent factors, and the key requirement is that these histories are sufficiently informative about those latent factors. I develop an identification strategy for the dynamic average treatment effect on the treated (ATT) and propose kernel-based doubly robust estimators for the dynamic ATT. I further combine double cross-fitting with undersmoothing and show that, under suitable regularity conditions, the proposed estimators are $\sqrt{n}$-consistent, asymptotically normal, and asymptotically unbiased. The simulation study demonstrates that the proposed method provides accurate inference across a wide range of data-generating processes. I illustrate the method with an application to the U.S. family planning program studied by Bailey (2012) and reestimate its effect on fertility rates. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.17170 |
| By: | Xiaojun Song; Haojiao Zhao |
| Abstract: | We propose a nonparametric test for unobserved treatment effect heterogeneity in regression discontinuity designs. Under the null of no unobserved heterogeneity, a transformed outcome that imputes treated potential outcomes for untreated units must have a continuous conditional distribution at the cutoff. We convert this implication into an integrated conditional-moment restriction using characteristic functions, thereby allowing the conditional local average treatment effect to be an unrestricted function of covariates. We derive the asymptotic distribution of the test statistics via a $U$-process and establish the validity of a multiplier bootstrap procedure for calculating critical values. Monte Carlo experiments show well-controlled size and increasing power. Two empirical applications illustrate how the test distinguishes between heterogeneity explained by observables and that explained by unobserved factors. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27691 |
| By: | Drew D. Creal; Marcelo C. Medeiros; Rodrigo Sarlo |
| Abstract: | We develop a class of linear state space models for matrix-valued time series data where the state is a latent matrix normal process. We derive matrix versions of the Kalman filter, log-likelihood, and smoother enabling estimation of the latent state matrix as well as the model's parameters. To conduct Bayesian inference, we provide algorithms that draw from the joint posterior distribution of the latent state matrices conditional on the observed data and parameters. We apply these methods to a large panel of U.S. macroeconomic time series across the 50 U.S. states. The proposed framework accommodates mixed-frequency data, heteroskedasticity, and outliers within a unified matrix-valued structure. Empirically, we find that a small number of latent factors captures the joint dynamics across states and variables, providing a parsimonious and scalable approach to modeling high-dimensional macroeconomic systems. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.18734 |
| By: | Ryoya Nakano; Takahiro Hoshino |
| Abstract: | In difference-in-differences (DiD), researchers may use pre-treatment trends to select a control group for which the parallel-trends assumption appears plausible, with the aim of estimating the average treatment effect on the treated (ATT). Our earlier paper, Nakano and Hoshino (2016), and the present paper jointly provide the first selective-inference approach to the ATT that explicitly accounts for this control selection. We generalize our exact Gaussian procedure with known covariance to allow the covariance matrix to be estimated from the same individual-level data used for control selection and DiD estimation. We use this estimate to compute the variance, conditioning direction, residual, and truncation set. With fixed numbers of regions and periods, we establish uniform conditional coverage for selection events with probabilities bounded away from zero, and marginal coverage of the selected target without that restriction. We allow unequal regional sample sizes, heterogeneous covariances, ties in population fit, and regional sample shares that converge to zero. We establish asymptotic equivalence between the plug-in and known-covariance interval endpoints and derive rates for interval length. For staggered adoption, the control pools may differ across cohorts and periods, controls may be not yet treated, observations may be reused, and treatment effects may be heterogeneous. We also construct inference conditional on unions of selection paths that leave the reported parameter unchanged, together with simultaneous confidence bands for finitely many event-time effects. Under parallel trends and the other identifying conditions, the coverage results apply to the ATT. We give sufficient sampling conditions for individual panels and independent repeated cross-sections. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01464 |
| By: | Benjamin O. Harrison; David T. Jacho-Chavez |
| Abstract: | This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $\chi^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no convergence rate is assumed or estimated, permitting the condition number of the score's variance matrix to diverge. We further propose a delete-one-unit jackknife alternative that is positive semidefinite by construction. This jackknife statistic attains the same asymptotic limit under one additional condition on dyad multiplicity and remains asymptotically conservative when that condition fails. A supplement contains all proofs, Monte Carlo experiments featuring estimated coefficients that converge at heterogeneous rates, and an empirical gravity application to bilateral trade. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.16968 |
| By: | Bram Wouters; Cees Diks |
| Abstract: | We develop a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component contaminated by observational white noise. Under the assumption that the latent dynamics live in a low-dimensional linear dynamic subspace, we characterize the optimal linear projection onto the dynamic subspace and provide a geometric description of the residual error in terms of the relative orientation of the signal and noise spaces. We propose estimators for the dynamic subspace and the optimal projection based on lagged covariance matrices, bootstrap dimension selection, and a low-rank representation of the structured noise. Under mild conditions, the resulting denoised series is shown to converge to its population target at the usual parametric rate. Simulations show that the proposed method can substantially improve subspace estimation, reconstruction error, and one-step-ahead forecast accuracy compared with both orthogonal projection-based denoising and the raw data. The approach is illustrated by empirical applications to high-dimensional stock returns and to a 20-variate time series of macroeconomic indicators. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27614 |
| By: | Jung Hyub Lee |
| Abstract: | Instrumental variable analyses often rely on the assumption that instruments affect the outcome only through the endogenous regressor. In many applications, researchers can defend only a plausible range for direct effects of instruments, while conventional sensitivity analyses may be unreliable when instruments are weak. This paper proposes the profiled Anderson--Rubin (pAR) test, which considers all direct effects within a prespecified range and retains a candidate effect whenever at least one admissible direct effect is consistent with the data. Under the maintained sampling assumptions, the procedure controls false rejection for each compatible candidate without requiring strong instruments. The paper provides practical methods for constructing confidence sets and distinguishes substantive bounds from bounds tied to the realized instrument design. Simulations and applications to retirement saving and returns to schooling show that the procedure resembles conventional sensitivity analysis when instruments are strong but preserves substantially more uncertainty when identification is weak. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.18150 |
| By: | Firmin Doko Tchatoka (The University of Adelaide); Magnus Söderberg (Griffith University); Mohammad Abbas Hakeem (The University of Adelaide) |
| Abstract: | Efficiency analysis is crucial for evaluating the performance of entities that provide essential and other homogenized services. The Jondow et al.’s (1982) estimator is widely used but has been criticized for biasing inefficiency toward its mean, distorting the distribution, and misrepresenting the conditional distribution of inefficiency, particularly in cross-sectional settings. Zeebari et al. (2023) propose a regularization approach that aligns sample and theoretical moments but it is primarily suited to cross-sectional data. This paper introduces a penalized mode estimator for unit inefficiency in panel data, accounting for heteroskedasticity in both inefficiency and idiosyncratic errors. A closed-form expression for this estimator is derived, and Monte Carlo simulations demonstrate its superior performance compared to existing methods. An empirical study of electricity providers in Australia, Canada, and New Zealand further underscores its advantages. |
| Keywords: | Inefficiency, JLMS estimator, Heteroskedasticity, Restricted mode estimator |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:adl:wpaper:2025-01 |
| By: | Jun Ma; Yuya Sasaki; Zhengfei Yu |
| Abstract: | It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not generally extend beyond linear estimands. This paper proposes a novel entropy balancing reweighting approach for covariate adjustment within a general framework of RDDs and RKDs. While conventional regression-based covariate adjustment methods generally fail to deliver consistent estimation for nonlinear estimands such as quantile treatment effects, our reweighting approach achieves consistency while improving efficiency. Moreover, even in settings where the regression-based covariate adjustment method already improves efficiency, our approach can deliver additional efficiency gains. Simulation studies corroborate these theoretical findings. We present an empirical application in which our covariate adjustment yields statistically significant results that would not be obtained without covariate adjustment. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29249 |
| By: | Xunkang Tian; Nan Zhi |
| Abstract: | This paper studies identification and inference in a triangular system with an endogenous regressor when exclusion restrictions are unavailable and the dependence between structural disturbances is modeled through an unrestricted control function. In this setting, standard orthogonality conditions do not deliver point identification, as the unknown control function can rationalize a wide range of structural coefficients. We show that identifying information can be extracted from distributional shifts in the first-stage disturbance induced by an auxiliary variable that may directly affect the outcome. Imposing a local restriction on the log density ratio, together with an explicit bound on the sieve approximation error of the control function, we derive moment inequalities that restrict the structural parameter. We develop a practical inference procedure based on test inversion and multiplier bootstrap that accommodates generated regressors, cross-fitted sieve estimation, and locally estimated density-ratio nuisances. The results clarify how identification can be recovered from weak local distributional structure in the absence of classical instruments. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.13026 |
| By: | Fangzhou Yu |
| Abstract: | Estimating continuous treatment effects is hard because average-derivative estimators rely on an ill-posed conditional-density score. Recent work makes a bounded outcome weight the primitive, characterizing a class of weighted average derivative effects without density estimation. In this paper, we develop the identification and estimation theory for the optimally efficient estimands of this class under homoskedasticity and heteroskedasticity. On identification, we show that these estimands relax standard conditions, remaining valid at sharp boundaries and at interior treatment deserts that the strict overlap and density-smoothness conditions of classical theory rule out. On estimation, we derive the efficient influence function, and develop Debiased Machine Learning estimators. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.19031 |
| By: | Arturas Juodis; Ovidijus Stauskas; Sander Tromp |
| Abstract: | We study recursive-design wild bootstrap inference for dynamic panel data models with unobserved common factors estimated by Common Correlated Effects. In the large N, T setting, the bootstrap reproduces the biased limiting distribution in pure autoregressive models, but fails to capture all bias and factor-estimation variance components in models with additional regressors, particularly under weak exogeneity. We trace this failure to holding regressors fixed across bootstrap replications. We propose to combine bootstrap procedure with available bias-correction methods to conduct adjusted inference. Monte Carlo evidence shows substantial improvements over conventional strategies of using bias-correction paired with cross-sectional bootstrap methods. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.31442 |
| By: | Emmanuel Guerre; Yuting Wang |
| Abstract: | Consider an increasing number of consistent estimators to be averaged when only estimated weights are available. The underlying parameter of interest can be identical across estimators (homogeneity) or not (heterogeneity). The contribution of the paper is threefold. First, it is shown that the interaction of the estimated weights with the estimators can generate specific bias terms. This constrains the number of estimators that can be aggregated when weight estimation is ignored in inference. Second, the paper proposes estimated adaptive weights, which allow for standard Gaussian inference in a uniform manner and are asymptotically optimal both under homogeneity and heterogeneity. Third, conditions ensuring the validity of the Cochran (1937) Q test of homogeneity with estimated variance are given. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.19415 |
| By: | Agnes Norris Keiller |
| Abstract: | I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.30007 |
| By: | Karolina Gliszczy\'nska-Schroeder |
| Abstract: | Studying heterogeneous treatment effects has become essential in experimental and observational studies. A critical assumption for obtaining reliable treatment effect estimates is overlap, which requires that treated and control units have sufficiently similar covariate distributions. Poor overlap may limit the effectiveness of estimators, especially those based on propensity scores, potentially leading to unreliable results. We investigate the effectiveness of kernel balancing (KBal) (Hazlett, 2020) as an alternative to propensity score methods for conditional average treatment effect (CATE) estimation, particularly in settings with overlap violations. Building on optimization-based balancing approaches, we integrate KBal weights into tree-based methods, specifically, causal forests (Athey et al., 2019) and the X-Learner (XRF) (K\"unzel et al., 2019), to assess their impact on bias reduction and estimation precision. Monte Carlo evidence shows that KBal achieves near-exact balance in a transformed feature space, thereby improving treatment effect estimation in cases where traditional reweighting methods struggle due to extreme weights, finite-sample bias, or insufficient removal of pre-existing confounding bias. We apply the proposed methods to the semi-synthetic IHDP benchmark dataset. Overall, the results indicate that KBal leads to performance improvements, especially in settings with nonlinear treatment effects and limited overlap, making it a useful alternative to propensity score methods. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29440 |
| By: | Peter H. Egger; Ting Ji; Yulong Wang |
| Abstract: | The Poisson pseudo-maximum likelihood (PPML) estimator is widely used for estimating bilateral gravity equations. Its consistency requires only a correctly specified conditional mean. Conventional inference, however, also requires finite-variance scores and Gaussian limits. We show that these conditions fail: bilateral flows are Pareto-tailed, PPML scores have a stable limit under a structural gravity data-generating process, and sandwich confidence intervals are too narrow. We retain PPML for point estimation but replace sandwich inference with an m-out-of-n bootstrap robust to heavy tails. Across three bilateral data settings, the correction is large and overturns conventionally significant gravity coefficients. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.18750 |
| By: | Hubeyb Gurdogan; Georg Menz |
| Abstract: | We establish a sharp pathwise signal-to-noise criterion for quasi-maximum-likelihood (QML) estimation of a dominant breakpoint in the second-moment structure of a multivariate time series: the QML estimator is consistent whenever the between-regime contrast exceeds the within-regime fluctuation by an explicit factor, and below this threshold global recovery can fail. Two innovations drive the result. First, the framework is pathwise: no stochastic model is imposed on the data. Second, the log-determinant objective carries an additive ridge regularization: it removes the endpoint boundary layers, so no trimming of the candidate set is required, and tuning the ridge weakens the consistency condition. As an application of the main theorem, we establish consistency of QML breakpoint estimation in pervasive factor models whose dimension diverges with the sample size, for completely general error terms -- not necessarily independent, idiosyncratic, or even random. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12271 |
| By: | Bruno N. Costa; Florian F. Gunsilius |
| Abstract: | In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest. In such cases, one can only identify sets which are guaranteed to contain the true parameters. These are often characterized through linear programs that optimize over models compatible with the observed data. These programs can be infinite-dimensional in the optimizer and the number of constraints. We provide a unified way to characterize and solve such optimization problems by phrasing them as optimal transport problems on path spaces. This allows us to regularize the problem with an entropy penalty, recasting it as a multi-marginal entropic optimal transport problem, which can be solved efficiently via Sinkhorn iterations. In addition, it allows us to establish convergence of the regularized value to the sharpest bound, derive consistency rates for a plug-in estimator, and obtain asymptotic distribution for approximate bounds. The method is general and accommodates settings ranging from instrumental variable models with continuous variables to welfare estimation in heterogeneous demand models. We verify the statistical and computational properties in simulations and provide an application to demand estimation. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.40156 |
| By: | Hammed A. Olayinka; Saheed O. Olayemi |
| Abstract: | We develop a Dynamic Spatial Panel Bayesian Additive Regression Trees model with Horseshoe shrinkage (DSP-BART-HS) for high-dimensional spatio-temporal panel data. We jointly evaluate the model against a comprehensive suite of structural spatial econometrics, non-parametric machine learning methods, and small-area estimators across nine data-generating scenarios spanning 200 replicates each, including irregular spatial topologies, dense policy effects, and non-linear individual-level interactions. DSP-BART-HS is the best or statistically indistinguishable from the best estimator in every scenario. Conventional region-time-aggregate comparators suffer severe performance degradation -- trailing by a factor of three or more -- whenever individual-level non-linearity drives outcome variance, a limitation this framework's tree-ensemble design directly addresses. The model also maintains strong predictive accuracy under a zero-training-region spatial holdout via its spatial diffusion mechanism. We demonstrate practical utility across two U.S. county-level panel applications -- intergenerational economic mobility and geographic income inequality -- under genuine unseen-region, random, and temporal holdouts, with results validated through repeated-split uncertainty quantification and paired significance testing against every comparator. One clear limitation emerges: under temporal extrapolation specifically, a lighter per-region autoregressive specification (MTS-CAR-X) achieves a robust, and consistent advantage. DSP-BART-HS nonetheless establishes strong predictive performance, automated variable selection, and flexible inference for hierarchical spatio-temporal panel data. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.00072 |
| By: | Lucas Harlaar (Maastricht University); Jan van den Brakel (Maastricht University); Siem Jan Koopman (Vrije Universiteit Amsterdam) |
| Abstract: | We develop a multivariate time series model for the complete national accounts dataset which typically consists of at least nine variables. The dynamic features of the variables are characterized by a set of unobserved components that evolve stochastically over time and may represent trend, cyclical, and seasonal effects. The components can be uniquely associated with a single variable or can be common to multiple variablesThe model treats expenditure and production variables jointly, subject to accounting-identity constraints, ensuring that their gross domestic product aggregates are consistent in both mean and variance by construction. The model constraints can be treated naturally within the Kalman filter. Parameter estimation is based on exact maximum likelihood which is feasible despite the high-dimensional parameter vector. This model-based framework allows for synchronized nowcasting, forecasting, seasonal adjustment, trend and cycle extraction for national accounts variables, including gross domestic product. We show that the statistical precision of signal extraction and forecasting improves when our framework is used instead of univariate and unconstrained model alternatives. When the model includes a common cycle component for all national accounts variables, it can be interpreted as a macroeconomic cycle indicator at current prices. We empirically illustrate our model-based methodology for data from national accounts of Germany, Italy, and the Netherlands, and show its significance for both official statistics and macroeconomic policy-making. |
| JEL: | C32 C54 |
| Date: | 2026–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260073 |
| By: | Xinbing Kong; Zeyu Li; Junfan Mao; Bin Wu |
| Abstract: | Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of missing control outcomes. We develop \emph{Counterfactual Tucker Diffusion} (\CFTDiff), which integrates the treatment mask and latent Tucker structure into conditional diffusion to recover this distribution given observed control outcomes through efficient nonlinear score learning in a low-dimensional core. The masked Tucker score preserves dependence across tensor modes while reducing the dimension of nonlinear score learning from the product of mode dimensions to the much smaller product of Tucker ranks. We establish high-probability error bounds for conditional score estimation that depend on the Tucker ranks, largest mode dimension, and the factor-strength-adjusted number of missing outcomes, and show how these bounds translate into recovery guaranties for the conditional distribution of the missing control outcomes. Across missing rates, simulations show more accurate point recovery than common causal panel and matrix/tensor completion methods; comparisons with nested diffusion specifications further demonstrate the gains from masked conditioning and Tucker dimension reduction. In Norway's iFlex experiment, \CFTDiff recovers missing outcomes more accurately than competing methods; when applied to causal analysis, its estimated conditional distributions yield counterfactual prediction intervals and target-attainment probabilities, allowing pricing interventions to be evaluated by demand-reduction magnitude and reliability. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25924 |
| By: | Oriol González-Casasús |
| Abstract: | Applied macroeconomists favor parsimonious VARs, which selection criteria and specification pre-tests routinely endorse. I show that weak, long-lived omitted dynamics escape those diagnostics and cannot be consistently estimated, yet the bias they induce in impulse responses and forecasts admits valid confidence intervals. Researchers can then keep the parsimonious VAR and state the direction and magnitude of the distortion it carries. In US data, the bias in the price response to a monetary contraction is significantly positive at several horizons, accounting for part of the price puzzle without expanding the cross-sectional information set. |
| Keywords: | dynamic mis-specification, price puzzle, robust inference, vector autoregression |
| JEL: | C12 C32 C52 E52 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1595 |
| By: | Han Xiao; Yuefeng Han; Rong Chen; Ama Ampadu-Kissi |
| Abstract: | Matrix time series is a series of matrix data observed over time. Analytical tools for such time series is needed in many applications in finance, economics, engineering and many other fields. To avoid the use of vectorization of the matrices which loses the column and row information, and the vector autoregression framework in traditional time series analysis, \cite{chen2021autoregressive} proposed the Matrix Autoregressive (MAR) Model. The model maintains and utilizes the matrix structure, leading to a substantial dimensional reduction and admitting explicit interpretations, comparing with the vector autoregressive model on the vectorized data. However, the MAR model still encounters difficulties in dealing with large dimensional matrix time series as the coefficient matrices in MAR models are also large. In this paper we propose to achieve further dimension reduction through reduced-rank constraints of the coefficient matrices in the MAR model. Estimation and rank determination procedures are studied. Theoretical investigation and empirical examples show that the reduced-rank constraint can achieve higher statistical efficiency than the MAR model. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.28841 |
| By: | Anindya Banerjee; Josep Lluís Carrion-i-Silvestre |
| Abstract: | A panel data cointegration testing methodology in the presence of unobserved common factors is proposed, focusing first on a bounds-based strategy proposed by Pesaran et al. (2001), and then moving to a comprehensive analysis of the idiosyncratic and common components using PANIC-based approximations as suggested by Bai and Ng (2004). We call this latter approach PANICO (PANIC Analysis of Cointegration). We are thus able to derive an extension to the PANIC testing strategy for unit roots in panels to cointegration in panels and to o¤er detailed comparisons between the Pesaran and Bai-Ng classes of tests, the former based on cross-section averaging, the latter relying instead on factors extracted by principal components. |
| Keywords: | Common factors, cross-section dependence, bounds testing, principal components, common correlated effects |
| JEL: | C23 C12 C38 |
| Date: | 2026–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:26/08 |
| By: | Xinyu Song |
| Abstract: | We introduce a network realized GARCH-It\^o model in which volatility transmission is a dynamic relation among the latent daily integrated volatilities of multiple assets. An unknown directed and signed network is embedded in a continuous-time variance process and appears in the resulting exponential daily recursion. Intraday returns identify integrated volatility and hence the network that governs its propagation. For a fixed network template, feasible estimation based on realized volatility is first-order equivalent to estimation based on latent integrated volatility. For an unknown network, regularization selects the relevant structure, and, under oracle conditions, a fixed-rank local refit provides conditional inference for model-implied response-based connectedness. In an application to nine U.S. sector ETFs from 2007 to 2025, the model attains the lowest average out-of-sample QLIKE among the reported recursive forecasts, although its advantage over HAR is not statistically significant. In the full sample the weighted LASSO selects an empty sparse support, so inference concerns the rank-one projected factor component; this conditional analysis identifies Energy as a net volatility transmitter. Aggregate connectedness is more stable across volatility measures than individual sparse channels. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29515 |
| By: | St\'ephane Bonhomme; Koen Jochmans; Martin Weidner |
| Abstract: | Many models, such as fixed-effect models for panel or network data, are hard to estimate because they feature nuisance parameters that are both numerous and estimated imprecisely. This, in general, causes an incidental-parameter problem in the estimator of the parameters of interest. The problem can be alleviated by working with an estimating equation whose expectation is insensitive to the value of the nuisance parameters. We discuss and contrast three notions of insensitivity, also called orthogonality, in the context of likelihood models: Neyman orthogonality, Neyman orthogonality to order q, and full orthogonality. Orthogonal moments are obtained by projecting the estimating equation on nested subspaces, which are spanned by, respectively, the scores of the nuisance parameters, the first q derivatives of the likelihood ratio with respect to the nuisance parameters, and all likelihood ratios of the model. We give explicit constructions in binary-choice, count-data, and nonlinear regression models. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.31561 |
| By: | Aidan Wardak (University of Florida); Sayar Karmakar (University of Florida) |
| Abstract: | We derive the limiting distributions of the $M$-test family of unit root statistics in the nearly integrated nearly white noise (NINW) framework introduced by Nabeya and Perron (1994) in the case of an unknown linear time trend. In the case of known long run variance (LRV), the limiting distributions of the $M^{GLS}$ tests are contaminated by additional noise terms as a result of quasi differencing whereas these terms are less present in the $M^{OLS}$ limiting distributions, both of which display conservative properties under conventional critical values. Furthermore, we prove the Gaussian power envelope in the NINW model is asymptotically equivalent to the standard envelope of Elliott, Rothenberg, and Stock (1996), and that the oracle $M$-tests have inefficient power relative to this benchmark. We then derive the limiting distributions of the feasible statistics and show that the autoregressive estimate of the LRV commonly used overestimates the LRV, creating altered limiting distributions. Finally, finite sample simulations illustrate that, of the procedures considered, no uniformly satisfactory solution exists for handling a series with a large negative moving average coefficient. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.30504 |
| By: | Carl Bonander |
| Abstract: | Policy reforms are sometimes accompanied by detailed individual-level data in the implementing jurisdiction, while only aggregate outcomes are available for potential comparison jurisdictions. This article develops an identification framework for heterogeneous policy effects when individual-level data are unavailable for the comparison units. The framework combines treatment-effect contrasts from difference-in-differences comparisons within the treated jurisdiction with a compatible population-average effect identified from aggregate panel data. Identification requires relative parallel trends within the treated jurisdiction together with the assumptions needed to identify the population-average effect from the aggregate panel. The within-jurisdiction component can be estimated from repeated cross-sections with a single pretreatment period. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.28678 |
| By: | Andres Aradillas Fernandez; Victor Chernozhukov; Carlos Cinelli; Sven Klaassen; Whitney Newey; Martin Spindler; Jan Teichert-Kluge; Suhas Vijaykumar |
| Abstract: | Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is valid and develop a practical framework for causal inference with learned representations. For a broad class of estimands, an imperfect representation distorts the target causal parameter by the product of two representation errors: one in the outcome regression and one in the balancing weight (or Riesz representer). This yields three constructive results. First, cross-fitted double machine learning (DML) provides valid Wald inference for the representation-dependent target. When representation errors are small, the same interval covers the causal parameter, and it can even attain the semiparametric efficiency bound. Second, fold-wise representation learning (or fine-tuning) is compatible with DML inference for the causal parameter. To this end, we develop convex- and star-aggregation pipelines for learning and combining representations. Third, when representation errors are substantial, we can provide interpretable sensitivity regions and root-$n$ inference for their endpoints. In a multi-modal demand application, seven representation-specific estimates and their star aggregate all imply a negative near-unit elasticity for rank-based price response, and the result remains robust over the reported sensitivity grid. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01935 |
| By: | Shoki Okubo |
| Abstract: | Refreshment samples are the standard remedy for panel attrition, and the identification results behind them maintain that participation does not change measurement. We characterize what a refreshment sample identifies about panel conditioning, modelled as a deterministic monotone map at reinterview, when attrition is unrestricted. A candidate map is consistent with the data if and only if the retention-scaled distribution of the stayers' implied latent outcomes is setwise dominated by the refreshment distribution; every such map is rationalized by an explicit attrition process. Without attrition the map is identified on the latent-outcome support; with attrition, a density-ratio condition governs the identified set, and tail behaviour alone does not determine it. For an unrestricted map the survivors' mean effect has the familiar trimming bounds; for an item with all categories reported, the model reduces to a test of no conditioning. Within a cohort, other waves, dropout patterns and entry-wave items leave the set unchanged unless restrictions link selection across waves. Under explicit selection restrictions, survival matching, symmetric matching and entry-wave correction identify survivor effects. We derive their biases and give rank conditions under which refreshment schedules identify curvature in the conditioning path. A Japanese panel illustrates the results. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01654 |
| By: | Peter Reinhard Hansen; Werner Ploberger; Chen Tong |
| Abstract: | We study identity testing for high-dimensional covariance matrices against dense alternatives of unknown direction, with $p/n \to \gamma$. Along a globally positive quadratic precision path, mixing Gaussian alternatives over a Gaussian Orthogonal Ensemble direction yields a contiguous experiment whose log likelihood reduces to the corrected Frobenius statistic; its upper-tail test attains the limiting weighted-power envelope at every fixed strength. Fixing the prior's Frobenius radius perturbs the mixture by only $O(p^{-1/2})$ in total variation, and exact whitening carries the experiment, the statistic, and its null law to any known null covariance. Separately, under a product-coordinate null, feasibility needs only $4+\eta$ moments, plus identical distributions over time when means are estimated; studentization and an exact degrees-of-freedom correction preserve the local power. A stability inequality turns near-envelope attainment into null agreement with the Frobenius rule, so uniform noninferiority on the typical dense bulk precludes gains at any contiguous alternative. For trace-matched rank-one alternatives, the corrected statistic is the first likelihood direction when $\vartheta_n \to 0$ and $n\vartheta_n \to \infty$; at fixed strength, the log likelihood ratio in the Onatski-Moreira-Hallin fixed-spike benchmark is governed by a richer linear spectral statistic below the Baik-Ben Arous-Peche threshold, while eigenvalue separation permits cost-free largest-eigenvalue enhancement above it. Simulations illustrate the theory. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27200 |
| By: | Giovanni Bonaccolto (Department of Economics and Law, ``Kore" University of Enna, Piazza dell'Universita, 94100 Enna, Italy); Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241/243, Padova, Italy); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We revisit Quantile-on-Quantile connectedness and argue that estimating the equations of a Quantile Vector AutoRegressive model at different quantile levels does not fully capture the conventional Quantile-on-Quantile mechanism. We propose a local, kernel-weighted QVAR approach that explicitly accounts for the quantile location of the conditioning variable. An empirical comparison using U.S. Treasury yields shows that this modification substantially affects estimated connectedness, producing a more homogeneous pattern across quantile pairs and markedly attenuating the differences between directly and reversely related quantiles. |
| Keywords: | Quantile-on-Quantile, Connectedness, Quantile VAR, Spillovers, Quantile Regression |
| JEL: | C22 C32 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202629 |
| By: | Joshua Brault (Bank of Canada); Qazi Haque (The University of Adelaide); Louis Phaneuf (Université du Québec à Montréal) |
| Abstract: | Taylor rule estimation can be biased if the central bank’s inflation target is counterfactually held fixed. First, we identify in the standard New Keynesian model a downward bias affecting the OLS estimate of the policy response to inflation. Based on simulations from a similar model with positive trend inflation, we show that the policy response to inflation is nearly half the true value with large inflation target fluctuations, as purportedly exhibited by the Fed during the 1960s and 1970s. Second, we implement a procedure for estimating DSGE models using a novel population-based MCMC routine known as parallel tempering. Applying this procedure to a medium-scale DSGE model with positive trend inflation, we do not find evidence of a bias when jointly estimating model parameters and the unobserved time-varying inflation target. |
| Keywords: | Unbiased Taylor rule estimation; time-varying inflation target; parallel tempering algorithm; DSGE models; Positive Trend Inflation. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:adl:wpaper:2025-03 |
| By: | Schröder, Maximilian |
| Abstract: | Macroeconomic policy increasingly requires assessments of risk rather than point forecasts alone. This paper develops a framework to compute impulse responses for scenario probabilities and the full predictive distribution of macroeconomic outcomes. The approach combines a regime-dependent mixture VAR with endogenous state probabilities and heteroskedastic shock identification, complemented by sparse sign and narrative restrictions. Applied to the euro area and U.S., the framework addresses a central policy question following the outbreak of the 2026 Iran war: do geopolitical shocks imply a return to the risk profile observed after Russia’s invasion of Ukraine, or is this time different? Results show that shocks differ not only in their effects on expected outcomes, but also in how they reshape scenario probabilities, tail risks, and transitions into high-volatility states. JEL Classification: C11, C32, C34, C53, E37 |
| Keywords: | Bayesian econometrics, density forecasting, macroeconomic risks, scenario analysis, state-dependent transmission |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263290 |
| By: | Haroon Mumtaz; Sofia Velasco |
| Abstract: | The tails of macroeconomic outcomes can respond differently from the centre of their distribution: shocks with modest effects on median growth or inflation can shift downside growth or upside inflation risk. We develop a threshold stochastic-volatility-in-mean VAR with regime-dependent leverage to study their structural drivers. The model allows endogenous interactions between outcomes and volatility, contemporaneous level-volatility dependence, and regime-specific propagation. In nearly 150 years of U.S. data, predictive model selection supports three inflation-defined regimes. We identify business-cycle, financial, macroeconomic-uncertainty, and financial-uncertainty shocks and decompose their contributions to growth- and inflation-at-risk. The structural composition of tail risk differs from that of the predictive median. Business-cycle shocks dominate the median response of GNP growth but account for a substantially smaller share of growth-at-risk. Macroeconomic uncertainty makes a material contribution to both growth- and inflation-at-risk, with its share of growth-at-risk increasing with the magnitude of a positive macroeconomic-uncertainty impulse, despite its limited role at the median. In high-inflation states, the contribution of financial uncertainty to inflation-at-risk rises with the magnitude of positive financial-uncertainty impulses. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.26994 |