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on Econometrics |
| By: | Brice Romuald Gueyap Kounga |
| Abstract: | This paper develops identification and estimation methods for a semiparametric dynamic logit model in which a binary outcome depends on observed covariates, the lagged outcome, and an unknown function of a latent social characteristic that also governs the formation of social ties. The unobserved characteristic is allowed to vary across agents and over time, and the network formation process is left completely unspecified. Identification combines three elements: conditional likelihood arguments that exploit the logistic structure, network-type matching that eliminates the unknown social influence function by comparing agents whose observed linking behavior reveals identical latent characteristics, and local temporal smoothing that handles the interaction between dynamics and time-varying unobserved heterogeneity. A kernel-weighted conditional maximum likelihood estimator is proposed, and its consistency and asymptotic normality are established at the $\sqrt{n}$ rate. Monte Carlo simulations show that the estimator substantially reduces the bias present in naive and control-function approaches across a range of network formation models and achieves close to nominal coverage at moderate sample sizes. The method is applied to longitudinal data on adolescent smoking and friendship networks from the Glasgow Teenage Friends and Lifestyle Study. An extension to ordered outcomes is developed using composite conditional maximum likelihood. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.16230 |
| By: | Mikihito Nishi; Ryo Okui |
| Abstract: | This paper introduces statistical inference procedures for unit-specific coefficients in panel data models, where the coefficients exhibit a latent group structure. The proposed methods achieve efficiency gains by clustering units into a small number of groups, while explicitly accounting for the statistical uncertainty of group assignments. The core idea is to integrate standard inference procedures, such as the $t$-test and Wald tests, with confidence sets for group membership. Two methods are proposed: the first takes the minimum of the test statistics over the confidence set for group membership, and the second corrects for bias caused by possible group misassignment. The former can produce shorter but possibly disconnected sets, while the latter guarantees connected, interpretable intervals at some cost in length. We also develop standard errors that are adjusted for possible group misassignment and valid even with short time periods, which may be of independent interest. Monte Carlo simulations demonstrate that our approach yields narrower confidence sets for units with relatively large error variances than unit-by-unit time-series methods. In contrast, ignoring statistical uncertainty in the group membership estimation leads to distortions in size and coverage. We illustrate the method with an empirical example that estimates the effect of the minimum wage in each U.S. state. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22035 |
| By: | Duong Trinh; Santiago Montoya-Bland\'on |
| Abstract: | This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome equations, addressing network endogeneity for credible estimation of heterogeneous spillover effects. We propose a fully Bayesian data augmentation approach for estimation and inference, overcoming challenges posed to standard likelihood-based methods. A simulation study validates our approach. Our empirical application to an innovation network among U.S. firms reveals significant positive, yet heterogeneous, peer effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight varying firm behaviors in response to exogenous R&D policy shocks and and quantify firm-level direct and spillover effects, offering valuable insights for evidence-based and targeted policy design. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.24850 |
| By: | Ayush Jha |
| Abstract: | Predictive dependence in time series need not be confined to the conditional mean. Outside the Gaussian setting, causal content may arise through conditional scale, tail behavior, asymmetry, or other distributional features, implying that no single Granger-type test provides a complete characterization of predictive dependence. This paper develops a framework for distributional Granger causality based on a finite collection of channel-specific restrictions. Under suitable determinacy conditions, the channel menu is shown to be complete, yielding an identification result that links distributional Granger non-causality to a finite set of testable hypotheses. Building on this representation, we develop an adaptive sequential testing procedure that allocates inferential resources across channels while maintaining familywise error control through an alpha-investing mechanism. A policy-invariant validity theorem establishes finite-sample size control under arbitrary admissible selection rules, while an asymptotic efficiency theorem shows that a confidence-bound allocation rule achieves power equivalent to that of an infeasible oracle benchmark. The theoretical guarantees are derived from primitive mixing and moment conditions together with a circular-block permutation scheme. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22230 |
| By: | Zecharias Anteneh |
| Abstract: | In difference-in-differences designs, the parallel trends assumption requires that the outcome gap between treated and control units would have remained flat absent treatment. Pre-treatment event studies frequently reject this flat-gap requirement. Existing responses include parametric trend controls and bounds on the treatment effect under assumptions about the magnitude of the violation. This paper shows that point identification of cohort-specific and aggregate treatment effects in staggered designs remains achievable under strictly weaker assumptions. I replace the flat-gap requirement with a hierarchy of higher-order conditions, Parallel[p], embed this framework in the group-time average treatment effect structure of Callaway and Sant'Anna (2021), and prove an aggregation theorem for the case where different cohorts are identified under different feasible polynomial orders, a challenge unique to staggered designs that has not been previously addressed. A sequential order-selection procedure guides applied practice. Monte Carlo evidence confirms that post-selection bootstrap coverage remains near-nominal and that inference is robust to realistic serial correlation. Applied to Medicaid expansion data, the method yields point estimates resting on an assumption the pre-treatment data do not reject, in contrast to the flat-gap requirement which those same data decisively reject. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17977 |
| By: | Oliver Kojo Ayensu (Paderborn University); Yuanhua Feng (Paderborn University); Dominik Schulz (Paderborn University) |
| Abstract: | This paper considers two tractable special cases of the fractionally integrated asymmetric power ARCH (FIAPARCH) model, called FIGJR-GARCH and FITGARCH, which exhibit improved numerical stability relative to the general FIAPARCH specification. Under a restriction on the leverage parameter, almost sure positivity of the conditional variance process is ensured by the Conrad and Haag (2006) conditions. Building on these parametric specifications, we develop semiparametric extensions. In this framework, we first estimate the time-varying long-run component for unconditional variance by a local linear estimator, and then estimate the time-invariant parameters in GARCH-type short-run component by a quasi maximum likelihood estimator based on descaled returns. Next, we construct pointwise confidence bands for inference on the long-run component. An application to equity returns suggests that part of the persistence attributed to fractional integration in parametric long-memory GARCH models may instead reflect long-run variation in the unconditional variance. The empirical evidence also suggests that individual stocks exhibit more pronounced long-run variation in volatility than aggregate indices. |
| Keywords: | EGARCH family, FIGJR-GARCH, FITGARCH, QMLE based on descaled returns, scale function estimation, semiparametric GARCH model |
| JEL: | C14 C22 C51 C58 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:175 |
| By: | Gokul Gopalan Ramachandran |
| Abstract: | I develop the asymptotic theory of instrument strength for Granular Instrumental Variables (GIV) in large panels with both $N$ and $T$ growing. The strength of the GIV depends on the presence of dominant units. I formalise what dominance means and characterise three regimes of instrument strength. When a few units dominate the aggregate, the instrument is strong. The GIV estimator is consistent and asymptotically normal at the standard $\sqrt{T}$ rate. When large units stand out but do not dominate, the instrument weakens. But I show that the parameter of interest remains recoverable. The GIV estimator remains consistent and asymptotically normal, now at a rate slower than $\sqrt{T}$. When units are comparable in size and none stands out, the instrument is weak in the standard sense. The GIV estimator is inconsistent and has a non-standard distribution. Wald inference is reliable only outside the weak regime. When the instrument is weak, I recommend Anderson-Rubin confidence sets. In practice, the instrument must be constructed in a first stage. I show that the feasible estimator attains the same rate, but its asymptotic variance picks up an additional term from the first-stage estimation. Valid inference must use standard errors that account for this term. I apply the GIV estimator with the correct standard errors to recover the short-run demand elasticities of three commodities: refined copper, crude oil, and natural gas. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.02095 |
| By: | Abhimanyu Gupta; Xi Qu; Jiajun Zhang |
| Abstract: | We develop a semi-nonparametric framework for spatial dynamic panel data (SDPD) models with two-way fixed effects when the spatial interaction structure is unknown beyond a distance measure. This is accomplished by modelling spatial weights in the outcome, lagged-outcome, and disturbance channels as unknown functions of underlying economic distances. These enter the SDPD system through matrix-function operators, providing a unified approach that accommodates both spatial autoregressive and matrix exponential spatial specifications. Allowing for unknown heteroskedasticity, we propose sieve GMM estimators based on a stacked set of linear and quadratic moment conditions, and derive a feasible optimal GMM estimator and a more efficient feasible best GMM estimator. As $(n, T) \rightarrow \infty$, the parametric component is $\sqrt{n(T - 1)}$-consistent and asymptotically normal, echoing classical semi-nonparametric results. Monte Carlo experiments indicate excellent finite-sample performance. We apply the method to 'witch' killings as studied by Miguel (2005), and find that economic-geography proximity rather than cultural-geography proximity between communities significantly amplifies spatial dependence in these economic murders. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.24266 |
| By: | Satarupa Bhattacharjee; Bing Li; Lingzhou Xue |
| Abstract: | We develop a novel distributional Difference-in-Differences (DiD) framework to capture treatment heterogeneity across outcome distributions. By leveraging optimal transport, we use the control group to estimate the untreated distributional drift from the pre- to post-treatment period and apply it to the treated group's pre-treatment baseline, constructing a counterfactual distribution under the assumption of no treatment effect. We frame the null hypothesis as a distributional equality between the transported counterfactual distribution and the observed treated post-treatment distribution, and test it using a maximum mean discrepancy statistic in a reproducing kernel Hilbert space (RKHS). The resulting nonparametric omnibus test is sensitive to changes in location, scale, shape, and tail behavior. Under the null, we derive the asymptotic Gaussian quadratic-form limit of the test statistic, while under local alternatives, we provide a unified characterization of power that establishes its Pitman local power and moderate-deviation consistency. Our theory reveals how detectability is shaped by the interaction between transport-induced drift and RKHS geometry. Simulations and an application to the Card--Krueger minimum-wage data demonstrate that the proposed method identifies key distributional treatment effects missed by classical mean-based DiD. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.21840 |
| By: | Sebastian Jensen; Siem Jan Koopman |
| Abstract: | We propose a new treatment of nonlinear regression with serially correlated disturbances that incorporates autoregressive moving average structures into feedforward neural networks. The resulting model provides an alternative to modeling temporal dependence using lagged variables. In simulations, the proposed method accurately recovers regression functions of varying complexity and the underlying error dynamics across a range of time-series lengths and signal-to-noise ratios. Finite-sample properties and out-of-sample predictive performances are shown to be robust to model misspecification induced by omitted lagged variables and incorrect specification of the error dynamics. Cloud cover is an important factor in climate projections. In an empirical study of cloud cover prediction for a grid of locations within and around the Mediterranean Sea, our proposed model yields more accurate predictions than existing methods, including long short-term memory networks. Improvements are observed broadly and are particularly pronounced in mountain areas relative to linear models with serially correlated errors, consistent with the presence of stronger nonlinear effects in cloud composure in such regions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22483 |
| By: | Jiawei Fu; Cyrus Samii; Ye Wang |
| Abstract: | A common experimental research design is one in which individuals are randomly allocated into groups that then interact under different group-level treatment conditions. We develop design-based inference for such "group interaction" experiments, covering scenarios in which groups are either fixed or randomly formed and in which potential outcomes are either fixed relative to others' group assignments or subject to interference. For each scenario, we characterize the causal estimand that the design targets and the inferential strategy appropriate to it. Working in a sparse-sampling asymptotic regime, we show that cluster-robust inference remains consistent and accounts for dependencies from various sources when interference is present, delivering valid inference on marginalized exposure effects. When interference is absent and groups are formed randomly, the design reduces to an individually randomized experiment, and individual-level heteroskedasticity-robust inference suffices for the average treatment effect. Our results on the asymptotic distribution of commonly used estimators rely on a novel coupling strategy that may be useful for design-based inference in other complex experiments. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.02385 |
| By: | Andrew Chesher; Adam Rosen; Yuanqi Zhang |
| Abstract: | This paper introduces a new approach to econometric analysis of nonlinear panel data models when the number of observations per observational unit is small. In such models the presence of variables that are constant within, while varying across, units results in an incidental parameter problem. The approach taken in this paper removes these incidental parameters via projection, which produces a correspondence specifying all combinations of observed variables and within-unit-varying unobserved heterogeneity that are achievable by choice of some value of the unit-specific incidental parameters. With unit specific variables removed, there is no need for assumptions concerning their joint distribution with other variables. The result is an incomplete model which is typically partially identifying. Identified sets are characterized via moment inequalities using tools of random set theory. Examples of application to static and dynamic models with discrete or continuous outcomes using distribution free restrictions on within-unit-varying unobserved heterogeneity are presented. |
| Date: | 2026–07–14 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:11/26 |
| By: | Sangmyung Ha |
| Abstract: | We propose two procedures for determining the number of dynamic factors, extending Bai and Ng (2002) and Ahn and Horenstein (2013) to dynamic factor models where lagged factors may directly influence the observed variables. As an intermediate step, we develop a simple and computationally efficient alternating least squares algorithm that directly estimates the dynamic factors, rather than their static representations. By working with these direct estimates, our approach enables joint determination of the number of factors and the filter length. Our test is shown to be consistent under weaker conditions than those in Bai and Ng (2007) and Amengual and Watson (2007). We apply our procedures to estimate the number of primitive shocks in a large panel of US macroeconomic time series. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.26142 |
| By: | Mattia Stival (Ca’ Foscari University of Venice); Stefano F. Tonellato (Ca’ Foscari University of Venice) |
| Abstract: | We study graph-indexed time series in which the vertices of a connected graph are partitioned into spatially contiguous clusters and each cluster carries a Gaussian or generalized linear dynamic state-space regression. The observation layer covers continuous Gaussian responses and binomial, Poisson, and negative-binomial GLM responses, while the clusterlevel state is a time-varying regression vector shared by all vertices in the same connected region. The partition prior is represented through cuts of spanning trees, which enforces connected clusters while allowing irregular shapes. We establish support properties of the prior, exact-target invariance for reversible-jump and tempered kernels, posterior concentration for dynamic predictors under non-i.i.d. Gaussian/GLM likelihoods, and partition-selection consistency under a separation condition. We then develop a posterior sampler based on split, merge, cut-swap, tree-refresh, boundary-reassignment, state-hyperparameter, and observation-parameter moves, together with proxy-guided proposals, locally balanced options, and standard parallel tempering. We also describe pointwise and simultaneous functional credible bands for dynamic coefficients. A controlled negative-binomial simulation illustrates the data-generating mechanism, recovery of the connected partition, the role of parallel tempering in improving exploration, and posterior estimation of main-effect and interaction trajectories. |
| Keywords: | Bayesian asymptotics; Bayesian computation; connected graph partitions; generalized linear dynamic models; graph-indexed time series; posterior concentration; posterior credible bands; parallel tempering; reversible-jump MCMC; simulation study; spanning trees; state-space models |
| JEL: | C11 C13 C15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2026:21 |
| By: | Ramon F. A. de Punder (University of Amsterdam); Mathijs R. G. Dijkstra (University of Amsterdam); Cees G. H. Diks (University of Amsterdam) |
| Abstract: | The score-driven framework relies on pre-specified scoring rules tied to assumed conditional densities, making it vulnerable to misspecification under outliers or structural breaks. We embed the flexible Barron loss within the quasi score-driven (QSD) framework, allowing the degree of robustness to be learned from the data. The resulting Barron-Loss Adaptive Estimation (BLADE) filter generates a strictly stationary, ergodic, and invertible sequence of time-varying parameters under mild regularity conditions. Within an extended quasi score-driven estimation framework, obtained by generalizing the required moment condition, the associated estimator is shown to be consistent and asymptotically normal. The Barron loss is strictly consistent for a family of functionals indexed by the shape parameter γ, enabling smooth adaptation between classical and robust targets. We establish that the BLADE update belongs to the clas of Proper and Robust Autoregressive Derivative Adaptive (PRADA) models and is therefore expected divergence reducing. We further establish that more robust updates achieve an at least as large expected local divergence reduction as less robust ones over explicit intervals, of step sizes in a non-contaminated setting and of contamination proportions under a contaminated updating draw, providing a formal robustness guarantee against corrupted observations. Monte Carlo experiments under non-contaminated and contaminated estimation environments confirm these theoretical findings and demonstrate superior performance relative to GARCH and βt–GARCH models when outliers are present. |
| Keywords: | Robust statistics, Score-driven models, Local Divergences, Consistent scoring functions, Online Z-Estimation |
| Date: | 2026–05–19 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260023 |
| By: | Florian Gunsilius |
| Abstract: | This article introduces an empirical condition for the nonparametric point-identification of multivariate instrumental variable models with continuous endogenous variables using binary instruments. Verifying this condition can confirm point-identification in settings in which traditional approaches are not applicable. In particular, it shows that nonlinear instrumental variable models with general heterogeneity can be point-identified with only a binary instrument. This generalizes existing identification results which either restrict the unobserved heterogeneity substantially or require the instrument to have a large support. The main assumption on the instrumental variable model is cyclic monotonicity of its first stage, a multivariate generalization of the classical rank-invariance assumption for univariate models. Asymptotic convergence results for the empirical observable distributions are derived that allow to check the condition in practice. The identification rests on a fixed-set convergence result of cyclically monotone maps between quasi-concave functions. The corrigendum corrects the proof of Lemma 1. The proof given there incorrectly identifies preservation of distributional level sets with preservation of the underlying probability measure via Brenier maps. We replace that argument by one based on inverse Brenier maps, which play the role of multivariate ranks. The corrected argument applies to a different but significantly more flexible class of distributions than the quasi-concave class considered in the original paper. In particular, it allows for smooth non-quasi-concave and multimodal densities on compact supports, provided the associated rank fixed set satisfies a nondegeneracy condition. Moreover, it is generically satisfied for smooth parmetric classes of distributions. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.01429 |
| By: | Jordi Llorens-Terrazas; Mika Meitz |
| Abstract: | We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that is based on a folklore result from measure theoretic probability. This representation provides a direct simulation-based approximation to the predictive distribution, enabling straightforward computation of forecasts for the conditional mean and variance, fan charts, value at risk, expected shortfall, joint tail risks, and other quantities of interest. We estimate this generative representation using a version of conditional generative adversarial networks and provide a formal statistical analysis of estimation under weak temporal dependence. Specifically, estimation is expressed as a particular minimax problem and we establish consistency of its approximate solutions in Hausdorff distance. The empirical relevance of the approach is illustrated using applications to equity returns, realized variance, and realized covariances. The proposed method is also computationally manageable, with estimation in our applications taking approximately one minute on a standard laptop. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.16773 |
| By: | Luis Orea; Alan Wall; Roberto Balado-Naves |
| Abstract: | This chapter discusses the empirical literature that uses a spatial stochastic frontier (SSF) analysis framework in production economics. We first outline standard spatial extensions of the classical stochastic frontier models aiming to capture the spillover effects of neighbouring production units. We then turn our attention to studies mainly focused on spatially correlated noise and inefficiency terms. Following this, semiparametric and nonparametric models, and modern approaches incorporating space-varying coefficients, unobserved common factors, stochastic spatial weight matrices, endogeneity and latent class structures, are discussed. We then provide a section with guidelines for researchers regarding model selection followed with a section devoted to issues relating to returns to scale and productivity growth are then discussed. A brief guide for practitioners to available software is provided in an appendix. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:oeg:wpaper:2026/02 |
| By: | Denise R. Osborn; Jing Tian; Jan P.A.M. Jacobs |
| Abstract: | Four sources of seasonality are distinguished for quarterly time series: (i) seasonal unit roots, (ii) deterministic seasonal shifts, (iii) trending deterministic seasonals, and (iv) stationary stochastic seasonality. The identification of relevant UC models is discussed, including the role of a stationary seasonal lag term when the innovations are correlated. Methodologically, the importance of unit root testing for seasonal UC model specification is emphasized, with the proposed approach applied to quarterly U.S. government expenditure series. |
| Keywords: | trend-cycle-seasonal decomposition, univariate unobserved com-ponents models, correlated component models |
| JEL: | C22 E32 E37 H50 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-57 |
| By: | Borusyak, Kirill; Hull, Peter |
| Abstract: | When estimating the effects of treatments defined by complex formulas, researchers often use simple functions of exogenous shocks as instruments. A leading example is “simulated instruments†for public policy eligibility, which capture variation in state-level policy generosity. We show how more powerful instruments can be constructed by incorporating heterogeneous shock exposure while using a recentering procedure to avoid bias. We characterize the asymptotically efficient instruments in this class and propose an algorithm for constructing feasible approximations to them. Compared to a simulated instrument approach, our approach yields a 44% smaller standard error on the private insurance crowd-out effect of Medicaid enrollment from the 2014 Affordable Care Act expansions. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21281 |
| By: | Ramon de Punder (University of Amsterdam) |
| Abstract: | This paper introduces the class of Proper and Robust Autoregressive Derivative Adaptive (PRADA) models, extending score-driven updates beyond the logarithmic scoring rule to all strictly proper and locally proper scoring rules and strictly consistent scoring functions. PRADA updates reduce an expected local divergence measure under misspecification and thereby generalize the information-theoretic foundation of score-driven models beyond the Kullback-Leibler divergence. They are interpreted as the online analogues of M-estimators, and are linked to online Z-estimation through strict identification functions. When derived from scoring functions or identification functions, PRADA updates operate directly on elicitable functionals of the postulated conditional distribution, such as conditional means, quantiles or risk measures, and therefore do not require a parametric model. The results provide general conditions under which updates are guaranteed to reduce their corresponding divergence, stablish robustness through bounded and censored updates, and encompass many existing score-driven inspired models as special cases. |
| Keywords: | Generalized autoregressive score (GAS), Dynamic conditional score (DCS), Scoring rules, Scoring functions, Divergence measures, Censoring |
| Date: | 2026–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260022 |
| By: | Sizhong Sun |
| Abstract: | This paper develops an approach for estimating demand for a new product. Taking willingness to pay (WTP) as primitive, it establishes a general and yet analytically simple demand function, and proposes an estimation procedure that consistently recovers the underlying demand function from the WTP data. Monte Carlo simulations find the estimation procedure works well in identifying the demand function. This approach complements existing methods of demand estimation, and can be applied both within and outside academia, for example in teaching economics, for a business to launch new products, and for policymakers to conduct non-market valuation. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.15748 |
| By: | H. Peter Boswijk (University of Amsterdam); Roger J. A. Laeven (University of Amsterdam); Niels Marijnen (University of Amsterdam); Evgenii Vladimirov (Erasmus University Rotterdam) |
| Abstract: | We develop a framework to analyze option markets using factor modeling techniques, offering a novel method to study how many and which risk factors drive the price process of a single asset. We exploit information contained in option prices to construct observations on the characteristic function of the returns on the underlying asset, without having to specify a parametric model. Our asymptotic setting is one in which the number of observed options, with varying strikes, tends to infinity. We establish consistency and asymptotic normality of the option-based log-characteristic function estimator, and provide a feasible central limit theorem that can be used for testing. Based on this, we prove that principal component analysis is able to extract the factors of affine jump-diffusions. We show in Monte Carlo simulations that our has good finite-sample properties. An empirical application indicates that the main factor driving S&P 500 returns is a stochastic variance process, along with a factor related to left-tail jump risk, and that at least two factors are needed to explain higher-order moments with reasonable accuracy. |
| Keywords: | Options, Factor Model, Characteristic Function, Affine Jump-Diffusion |
| JEL: | C14 C38 G13 |
| Date: | 2026–05–29 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260026 |
| By: | Xu, Yongdeng (Cardiff University, Cardiff, UK); Lyu, Juyi (Loughborough University, UK); Lu, Wenna (Cardiff Metropolitan University, Cardiff, UK) |
| Abstract: | This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with an application to green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH variance equations, the model delivers a sparse and data-driven volatility spillover structure while preserving positive definiteness of the conditional covariance matrix. Using daily data on green and conventional bonds, equities, energy commodities, and carbon allowances, we show that adaptive regularisation substantially reduces model complexity and improves economic interpretability relative to an unpenalised MGARCH benchmark. Out-of-sample forecasting experiments at multiple horizons demonstrate that the Adaptive LASSO-MGARCH model consistently achieves lower covariance forecast losses, and statistical tests based on the White reality check confirm that these improvements are significant across alternative loss functions. |
| Keywords: | Adaptive LASSO; Multivariate GARCH; Volatility Forecasting; High-Dimensional; Green Finance |
| JEL: | C32 C58 G17 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2026/4 |
| By: | Eric Auerbach; Jonathan Auerbach; Sidonia McKenzie |
| Abstract: | Researchers often use the density of connections between groups of agents, such as communities, blocs, or markets, to characterize the structure of a social or economic network. In many cases, these groups are selected using the network data, making conventional fixed-group inference procedures potentially invalid. To address this issue, we develop two new confidence intervals that are universally valid post-selection in the sense that they guarantee simultaneous coverage asymptotically over all pairs of groups whose relative sizes do not vanish. Our first interval builds on a strategy of \cite{berk2013valid}. Our second interval is based on a Talagrand-type concentration inequality for empirical processes. Both intervals are simple to compute and scalable to large networks, but a key technical contribution of our paper is show that only the second interval achieves the best-possible width asymptotically up to a constant factor. Three empirical illustrations show that accounting for selection can matter in practice. Some evidence for homophily in a social network and a hub-and-spoke structure in a trade network survives our correction, while evidence for disjoint market segments in a worker transition network does not. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.00312 |
| By: | Joan Alegre Canton |
| Abstract: | Structural models often fix (calibrate) some parameters and estimate the rest, but this calibration-estimation partition is usually chosen by convention. This paper treats that choice as an econometric partition-selection problem. For each admissible partition, we construct a scalar sensitivity statistic measuring the local response of a target object -- such as a policy effect, welfare measure, impulse response, or treatment effect -- to perturbations of the calibrated parameters. The selected partition minimizes this statistic and therefore minimizes worst-case local bias from calibration errors. We first illustrate the decision problem in two canonical examples. We then apply it to the New Keynesian model of Nakamura and Steinsson (2018), where the partition choice has large implications for credibility: some partitions remain reliable under sizeable miscalibrations, whereas others generate large bias from small calibration errors. The procedure requires only local derivatives, avoids repeated re-estimation, and applies to a broad class of structural models. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.25688 |
| By: | Jie Jian; Aaron Schein |
| Abstract: | We study sparse semi-continuous tensor data with excess zeros, heavy right tails, and slice-specific dispersion. Such features arise naturally in monetary-valued multi-way data, such as international trade, where most exporter--importer--product--year cells are zero while positive values are continuous and highly variable. To model these data, we propose a Bayesian hierarchical tensor factorization model that places a low-rank CP structure on a latent Poisson rate tensor and couples it with a conditional Gamma model for positive outcomes, with rate parameters that can vary across slices within a mode. The model therefore separates the occurrence and magnitude of positive observations while borrowing strength across all tensor dimensions through a shared low-rank latent structure. To scale posterior inference to large arrays, we develop a hybrid variational--Monte Carlo algorithm that combines efficient coordinate ascent updates with a partially collapsed augmented-data sampler. Applied to approximately 60 million trade flows, the method surfaces multiway dependence across exporters, importers, products, and years that is difficult to recover from gravity-type or pairwise network analyses, which do not jointly model the product and temporal dimensions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17267 |
| By: | Shujie Li (Paderborn University); Yuanhua Feng (Paderborn University) |
| Abstract: | Many economic and financial series exhibit non-stationarity as well as long-memory behavior in both the first and second moments. To capture both non-stationarity and long-memory characteristics simultaneously, a general dual-trend and dual longmemory framework is proposed. In this framework, the error term of the semiparametric FARIMA model is assumed to exhibit a slowly changing scale and longmemory heteroskedasticity. A four-step estimation procedure is proposed, including a trend and a FARIMA model estimation for the first moment, followed by a scaling function and a long-memory volatility model estimation for the second moment. Three long-memory EGARCH-type models and the FIGARCH model are employed in the final stage. Our results indicate that the proposed approach can effectively model the selected economic series exhibiting dual-trend and dual long-memory features. |
| Keywords: | dual-trend, dual long-memory, semi-strong FARIMA, modulus FILog- GARCH, modified FIEGARCH, FIEGARCH and FIGARCH |
| JEL: | C22 C14 C58 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:174 |
| By: | Nizam, Ahmed Mehedi |
| Abstract: | Here we have devised a new structural VAR (SVAR) based approach to measuring marginal propensity to consume (MPC) of households from different income groups. To be precise, we build structural VAR model with group-wise income and expenditure data of US households and perform impulse response analysis on the model to estimate the MPC of different income groups. Our analysis suggests that MPC values of US households belonging to bottom [0-20]%, [20-40]%, [40-60]%, [60-80]% and [80-100]% income group are 0.95, 0.81, 0.74, 0.71 and 0.35 respectively, which nicely converges with the existing theory of MPC that says, low income households have higher MPCs. Alongside conventional approaches of measuring MPCs, which involve OLS regression, instrumental variable, randomized control trial, natural experiment, variance decomposition etc., here we have developed a new methodology of measuring MPCs, which relies on standardized secondary data and provides reliable estimates of MPCs that nicely resembles to the existing theory. |
| Keywords: | Marginal propensity to consume; structural VAR (SVAR); income group |
| JEL: | C32 E21 |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128019 |
| By: | Valentin Haddad; Zhiguo He; Paul Huebner; Péter Kondor; Erik Loualiche |
| Abstract: | Portfolio choice involves substituting across many assets at once, complicating inference about asset demand. An elementary condition often captures this behavior in theory and practice: homogeneous substitution conditional on observables (e.g., factor loadings, maturity, credit ratings). We characterize natural experiments identifying demand elasticity and price impact under this condition. Cross-sectional IV and difference-in-differences identify relative elasticity, own- minus cross-price elasticity for assets sharing observables. But a missing-coefficient problem leaves substitution unidentified: the coefficients on observables mechanically absorb it. Identifying substitution requires time-series regressions on portfolios sorted on observables. We apply the framework to corporate bonds, comparing alternative Fed asset-purchase programs. |
| JEL: | G10 G20 L00 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35413 |
| By: | Yuan Christopher Qiang; Fabio Sigrist |
| Abstract: | We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a censored Gaussian variable with a two-parameter affine-logit transformation on the interior (0, 1). We characterize the transformation parameters, establish large-sample properties, and relate the affine-logit specification to broader classes of interior distributions. Theoretical and experimental results demonstrate that the proposed model can capture a wider range of qualitative density shapes than several benchmark models while remaining parsimonious, computationally efficient, and numerically stable. Furthermore, the ZOC-TN model can be extended (i) to account for nonlinearities and interactions in a tree-boosting machine learning framework and (ii) to explicitly model residual spatio-temporal variability. We apply the ZOC-TN model to loss given default (LGD) modeling for a large dataset of U.S. residential mortgages and compare it to multiple benchmark models. We find that a tree-boosted ZOC-TN model with a spatio-temporal frailty Gaussian process delivers the strongest out-of-sample performance, indicating that mortgage losses are shaped by nonlinear covariate effects and by unaccounted-for space-time variation. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.21515 |