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on Econometrics |
| By: | Boyao Wu; Jiti Gao; Deshui Yu |
| Abstract: | We consider a general class of dynamic network autoregressions for high-dimensional time series with network dependence, extending existing dynamic models by allowing for timevarying model coefficients, cross-sectionally dependent errors and a general network structure smoothly evolving along the time. A nonparametric local linear kernel method is proposed to estimate these time-varying coefficients involved, and a recursive-design bootstrap procedure is developed to construct valid confidence intervals for time-varying coefficients in the presence of cross-sectional dependent errors. We establish asymptotic properties for the proposed local–linear based estimator and the bootstrap procedure under mild conditions. Both the proposed estimation and bootstrap procedures are illustrated using simulated and two real datasets. Our work contributes to high-dimensional time series associated with network effects and sheds light on bootstrap inference for locally stationary processes. |
| Keywords: | cross-sectional dependence, dynamic network autoregression, high-dimensional time-series, MA(∞) representation |
| JEL: | C14 C31 C33 D85 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2025-8 |
| By: | Hasan Fallahgoul; Jiti Gao |
| Abstract: | This paper introduces a theoretically robust framework for conditional mean estimation associated with maximum likelihood estimation (MLE) in state space models with nonstationary time series, integrating the classical Kalman filter with Bayesian inference. We propose a dierence-based approach using optimally designed summary statistics from observable data, overcoming the intractability of traditional Kalman filter statistics reliant on latent states. Our formulation creates a direct mapping between feasible statistics and structural parameters, enabling clean separation between state dynamics and measurement noise. Under mild regularity conditions, we prove the consistency and asymptotic normality of the estimators, achieving the Cram´er–Rao lower bound for eciency. The methodology extends to non-Gaussian innovations with finite moments, ensuring robustness. Monte Carlo approximations preserve asymptotic eciency under controlled sampling rates, while finite sample bounds and robustness to model misspecification and data contamination confirm reliability. These theoretical advances are complemented by a comprehensive simulation study demonstrating superior performance compared to conventional approaches. These results advance the theoretical foundations of state space modelling, providing a statistically ecient and computationally feasible alternative to conventional approaches. |
| Keywords: | estimation theory, Kalman filter, nonstationarity, resampling technique |
| JEL: | C11 C15 C21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2025-7 |
| By: | Rapha\"el Langevin |
| Abstract: | Finite mixture models are widely used in econometric analyses to capture unobserved heterogeneity. This paper shows that maximum likelihood estimation of finite mixtures of parametric densities can suffer from substantial finite-sample bias in all parameters under mild regularity conditions. The bias arises from the influence of outliers in component densities with unbounded or large support and increases with the degree of overlap among mixture components. I show that maximizing the classification-mixture likelihood function, equipped with a consistent classifier, yields parameter estimates that are less biased than those obtained by standard maximum likelihood estimation (MLE). I then derive the asymptotic distribution of the resulting estimator and provide conditions under which oracle efficiency is achieved. Monte Carlo simulations show that conventional mixture MLE exhibits pronounced finite-sample bias, which diminishes as the sample size or the statistical distance between component densities tends to infinity. The simulations further show that the proposed estimation strategy generally outperforms standard MLE in finite samples in terms of both bias and mean squared errors under relatively weak assumptions. An empirical application to latent group panel structures using health administrative data shows that the proposed approach reduces out-of-sample prediction error by approximately 17.6% relative to the best results obtained from standard MLE procedures. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20197 |
| By: | Bruno Ferman; Davi Siqueira; Vitor Possebom |
| Abstract: | This paper develops a unified framework for partial identification and inference in stratified experiments with attrition, accommodating both equal and heterogeneous treatment shares across strata. For equal-share designs, we apply recent theory for finely stratified experiments to Lee bounds, yielding closed-form, design-consistent variance estimators and properly sized confidence intervals. Simulations show that the conventional formula can overstate uncertainty, while our approach delivers tighter intervals. When treatment shares differ across strata, we propose a new strategy, which combines inverse probability weighting and global trimming to construct valid bounds even when strata are small or unbalanced. We establish identification, introduce a moment estimator, and extend existing inference results to stratified designs with heterogeneous shares, covering a broad class of moment-based estimators which includes the one we formulate. We also generalize our results to designs in which strata are defined solely by observed labels. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.12566 |
| By: | Myunghyun Song; Sokbae Lee; Serena Ng |
| Abstract: | We develop an empirical Bayes (EB) G-modeling framework for short-panel linear models with multidimensional heterogeneity and nonparametric prior. Specifically, we allow heterogeneous intercepts, slopes, dynamics, and a non-spherical error covariance structure. We establish identification and consistency of the nonparametric maximum likelihood estimator (NPMLE) under general conditions, and provide low-level sufficient conditions for several models of empirical interest. Conditions for regret consistency of the resulting EB estimators are also established. The NPMLE is computed using a Wasserstein-Fisher-Rao gradient flow algorithm adapted to panel regressions. Using data from the Panel Study of Income Dynamics, we find that the slope coefficient for potential experience is substantially heterogeneous and negatively correlated with the random intercept, and that error variances and autoregressive coefficients vary significantly across individuals. The EB estimates reduce mean squared prediction errors relative to individual maximum likelihood estimates. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.07059 |
| By: | Kim Christensen; Ulrich Hounyo; Mark Podolskij |
| Abstract: | In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump-diffusion process are available. The test is then based on asset returns, which are deflated by the seasonal component and therefore homoskedastic under the null. To construct our test statistic, we extend the concept of pre-averaged bipower variation to a general It\^o semimartingale setting via a truncation device. We prove a central limit theorem for this statistic and construct a positive semi-definite estimator of the asymptotic covariance matrix. The $t$-statistic (after pre-averaging and jump-truncation) diverges in the presence of stochastic volatility and has a standard normal distribution otherwise. We show that replacing the true diurnal factor with a model-free jump- and noise-robust estimator does not affect the asymptotic theory. A Monte Carlo simulation also shows this substitution has no discernable impact in finite samples. The test is, however, distorted by small infinite-activity price jumps. To improve inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation in volatility, but important sources of heteroskedasticity remain present in the data. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16613 |
| By: | Xin Liu; Luciano de Castro; Antonio F. Galvao |
| Abstract: | This paper suggests methods for estimation of the $\tau$-quantile, $\tau\in(0, 1)$, as a parameter along with the other finite-dimensional parameters identified by general conditional quantile restrictions. We employ a generalized method of moments framework allowing for non-linearities and dependent data, where moment functions are smoothed to aid both computation and tractability. Consistency and asymptotic normality of the estimators are established under weak assumptions. Simulations illustrate the finite-sample properties of the methods. An empirical application using a quantile intertemporal consumption model with multiple assets estimates the risk attitude, which is captured by $\tau$, together with the elasticity of intertemporal substitution. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20853 |
| By: | Shoya Ishimaru |
| Abstract: | This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08281 |
| By: | Jiaming Huang; Luca Neri |
| Abstract: | This paper develops a framework for robust identification in SVARs when researchers face a zoo of proxy variables. Instead of imposing exact exogeneity, we introduce generalized ranking restrictions (GRR) that bound the relative correlation of each proxy with the target and non-target shocks through a continuous proxy-quality parameter. Combining GRR with standard sign and narrative restrictions, we characterize identified sets for structural impulse responses and show how to partially identify the proxy-quality parameter using the joint information contained in the proxy zoo. We further develop sensitivity and diagnostic tools that allow researchers to assess transparently how empirical conclusions depend on proxy exogeneity assumptions and the composition of the proxy zoo. A simulation study shows that proxies constructed from sign restrictions can induce biased proxy-SVAR estimates, while our approach delivers informative and robust identified sets. An application to U.S.\ monetary policy illustrates the empirical relevance and computational tractability of the framework. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11195 |
| By: | Apoorva Lal; Guido Imbens; Peter Hull |
| Abstract: | We propose a method for estimating long-term treatment effects with many short-term proxy outcomes: a central challenge when experimenting on digital platforms. We formalize this challenge as a latent variable problem where observed proxies are noisy measures of a low-dimensional set of unobserved surrogates that mediate treatment effects. Through theoretical analysis and simulations, we demonstrate that regularized regression methods substantially outperform naive proxy selection. We show in particular that the bias of Ridge regression decreases as more proxies are added, with closed-form expressions for the bias-variance tradeoff. We illustrate our method with an empirical application to the California GAIN experiment. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.06359 |
| By: | Chencheng Fang; Dominik Liebl |
| Abstract: | Event study plots are the centerpiece of Difference-in-Differences (DiD) analysis, but current plotting methods cannot provide honest causal inference when the parallel trends and/or no-anticipation assumptions fail. We introduce a novel functional data approach to DiD that directly enables honest causal inference via event study plots. Our DiD estimator converges to a Gaussian process in the Banach space of continuous functions, enabling fast and powerful simultaneous confidence bands. This theoretical contribution allows us to turn an event study plot into a rigorous honest causal inference tool through equivalence and relevance testing: Honest reference bands can be validated using equivalence testing in the pre-anticipation period, and honest causal effects can be tested using relevance testing in the post-treatment period. We demonstrate the performance of the method in simulations and two case studies. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.06804 |
| By: | Tomohiro Ando; Tadao Hoshino; Ruey Tsay |
| Abstract: | This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, violating the monotonicity of quantiles. To address this issue, we propose a simplex quantile vector autoregression (SQVAR) framework, which transforms the autoregressive (AR) structure of the original QVAR model into a simplex, ensuring that the estimated quantile curves remain monotonic across all quantile levels. In addition, we impose the smoothly clipped absolute deviation (SCAD) penalty on the SQVAR model to mitigate the explosive nature of the parameter space. We further develop a Bayesian information criterion (BIC)-based procedure for selecting the optimal penalty parameter and introduce new frameworks for impulse response analysis of QVAR models. Finally, we establish asymptotic properties of the proposed method, including the convergence rate and asymptotic normality of the estimator, the consistency of AR order selection, and the validity of the BIC-based penalty selection. For illustration, we apply the proposed method to U.S. financial market data, highlighting the usefulness of our SQVAR method. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04663 |
| By: | Harrison Katz |
| Abstract: | Compositional time series, vectors of proportions summing to unity observed over time, frequently exhibit structural breaks due to external shocks, policy changes, or market disruptions. Standard methods either ignore such breaks or handle them through ad-hoc dummy variables that cannot extrapolate beyond the estimation sample. We develop a Bayesian Dirichlet ARMA model augmented with a directional-shift intervention mechanism that captures structural breaks through three interpretable parameters: a unit direction vector specifying which components gain or lose share, an amplitude controlling the magnitude of redistribution, and a logistic gate governing the timing and speed of transition. The model preserves compositional constraints by construction, maintains innovation-form DARMA dynamics for short-run dependence, and produces coherent probabilistic forecasts during and after structural breaks. We establish that the directional shift corresponds to geodesic motion on the simplex and is invariant to the choice of ILR basis. A comprehensive simulation study with 400 fits across 8 scenarios demonstrates that when the shift direction is correctly identified (77.5% of cases), amplitude and timing parameters are recovered with near-zero bias, and credible intervals for the mean composition achieve nominal 80% coverage; we address the sign identification challenge through a hemisphere constraint. An empirical application to fee recognition lead-time distributions during COVID-19 compares baseline, fixed-effects, and intervention specifications in rolling forecast evaluation, demonstrating the intervention model's superior point accuracy (Aitchison distance 0.83 vs. 0.90) and calibration (87% vs. 71% coverage) during structural transitions. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16821 |
| By: | Jonas E. Arias; Juan F. Rubio-Ramirez; Minchul Shin |
| Abstract: | We develop a new algorithm for inference in structural vector autoregressions (SVARs) identified with sign restrictions that can accommodate big data and modern identification schemes. The key innovation of our approach is to move beyond the traditional accept-reject framework commonly used in sign-identified SVARs. We show that embedding the elliptical slice sampling within a Gibbs sampler can deliver dramatic gains in computational speed and render previously infeasible applications tractable. To illustrate the approach in the context of sign-identified SVARs, we use a tractable example. We further assess the performance of our algorithm through two applications: a well-known small-SVAR model of the oil market featuring a tight identified set, and a large SVAR model with more than ten shocks and 100 sign restrictions. |
| Keywords: | large structural vector autoregressions; sign restrictions; elliptical slice sampling |
| JEL: | C32 |
| Date: | 2025–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:102347 |
| By: | Craig S Wright |
| Abstract: | This paper develops a design-first econometric framework for event-study and difference-in-differences estimands under staggered adoption with heterogeneous effects, emphasising (i) exact probability limits for conventional two-way fixed effects event-study regressions, (ii) computable design diagnostics that quantify contamination and negative-weight risk, and (iii) sensitivity-robust inference that remains uniformly valid under restricted violations of parallel trends. The approach is accompanied by orthogonal score constructions that reduce bias from high-dimensional nuisance estimation when conditioning on covariates. Theoretical results and Monte Carlo experiments jointly deliver a self-contained methodology paper suitable for finance and econometrics applications where timing variation is intrinsic to policy, regulation, and market-structure changes. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.18801 |
| By: | Fu Ouyang; Thomas T. Yang; Wenying Yao |
| Abstract: | Empirical measures of financial connectedness based on Forecast Error Variance Decompositions (FEVDs) often yield dense network structures that obscure true transmission channels and complicate the identification of systemic risk. This paper proposes a novel information-criterion-based approach to uncover sparse, economically meaningful financial networks. By reformulating FEVD-based connectedness as a regression problem, we develop a model selection framework that consistently recovers the active set of spillover channels. We extend this method to generalized FEVDs to accommodate correlated shocks and introduce a data-driven procedure for tuning the penalty parameter using pseudo-out-of-sample forecast performance. Monte Carlo simulations demonstrate the approach's effectiveness with finite samples and its robustness to approximately sparse networks and heavy-tailed errors. Applications to global stock markets, S&P 500 sectoral indices, and commodity futures highlight the prevalence of sparse networks in empirical settings. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.03598 |
| By: | Anthony Britto |
| Abstract: | Time series often exhibit non-ergodic behaviour that complicates forecasting and inference. This article proposes a likelihood-based approach for estimating ergodicity transformations that addresses such challenges. The method is broadly compatible with standard models, including Gaussian processes, ARMA, and GARCH. A detailed simulation study using geometric and arithmetic Brownian motion demonstrates the ability of the approach to recover known ergodicity transformations. A further case study on the large macroeconomic database FRED-QD shows that incorporating ergodicity transformations can provide meaningful improvements over conventional transformations or naive specifications in applied work. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11237 |
| By: | Alessio Brini; Ekaterina Seregina |
| Abstract: | We propose Mixed-Panels-Transformer Encoder (MPTE), a novel framework for estimating factor models in panel datasets with mixed frequencies and nonlinear signals. Traditional factor models rely on linear signal extraction and require homogeneous sampling frequencies, limiting their applicability to modern high-dimensional datasets where variables are observed at different temporal resolutions. Our approach leverages Transformer-style attention mechanisms to enable context-aware signal construction through flexible, data-dependent weighting schemes that replace fixed linear combinations with adaptive reweighting based on similarity and relevance. We extend classical principal component analysis (PCA) to accommodate general temporal and cross-sectional attention matrices, allowing the model to learn how to aggregate information across frequencies without manual alignment or pre-specified weights. For linear activation functions, we establish consistency and asymptotic normality of factor and loading estimators, showing that our framework nests Target PCA as a special case while providing efficiency gains through transfer learning across auxiliary datasets. The nonlinear extension uses a Transformer architecture to capture complex hierarchical interactions while preserving the theoretical foundations. In simulations, MPTE demonstrates superior performance in nonlinear environments, and in an empirical application to 13 macroeconomic forecasting targets using a selected set of 48 monthly and quarterly series from the FRED-MD and FRED-QD databases, our method achieves competitive performance against established benchmarks. We further analyze attention patterns and systematically ablate model components to assess variable importance and temporal dependence. The resulting patterns highlight which indicators and horizons are most influential for forecasting. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16274 |
| By: | Adel Daoud; Richard Johansson; Connor T. Jerzak |
| Abstract: | Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatment leakage. Treatment leakage occurs when text intended to capture confounding information also contains signals predictive of treatment status, thereby inducing post-treatment bias in causal estimates. Critically, this problem can arise even when documents precede treatment assignment, as authors may employ future-referencing language that anticipates subsequent interventions. Despite growing recognition of this issue, no systematic methods exist for identifying and mitigating treatment leakage in text-as-confounder applications. This paper addresses this gap through three contributions. First, we provide formal statistical and set-theoretic definitions of treatment leakage that clarify when and why bias occurs. Second, we propose four text distillation methods -- similarity-based passage removal, distant supervision classification, salient feature removal, and iterative nullspace projection -- designed to eliminate treatment-predictive content while preserving confounder information. Third, we validate these methods through simulations using synthetic text and an empirical application examining International Monetary Fund structural adjustment programs and child mortality. Our findings indicate that moderate distillation optimally balances bias reduction against confounder retention, whereas overly stringent approaches degrade estimate precision. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.02400 |
| By: | Deborah Sulem; Jack Jewson; David Rossell |
| Abstract: | Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plausibility of conditional independence statements given the data, and parameter estimates. However, computational demands have limited their application when the number of variables is large, which prompted the use of pseudo-Bayesian approaches. We propose fully Bayesian algorithms that provably scale to high dimensions when the data-generating precision matrix is sparse, at a similar cost to the best pseudo-Bayesian methods. First, a Metropolis-Hastings-within-Block-Gibbs algorithm that allows row-wise updates of the precision matrix, using local moves. Second, a global proposal that enables adding or removing multiple edges within a row, which can help explore multi-modal posteriors. We obtain spectral gap bounds for both samplers that are dimension-free under suitable settings. |
| Keywords: | scalable Bayesian algorithms, Gibbs, Metropolis-within-Gibbs, spectral gap, Gaussian graphical models, spike-and-slab priors |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2025-10 |
| By: | Jochmans, Koen |
| Abstract: | Inference procedures for dyadic data based on two-way clustering rely on the data being exchangeable and dissociated. In particular, observations must be independent if they have no index in common. In an effort to relax this we consider, instead, data where Yij and Ypq can be dependent for all index pairs, with the dependence vanishing as the distance between the indices grows large. We establish limit theory for the sample mean and propose analytical and bootstrap procedures to perform inference. |
| Keywords: | bootstrap; clustering; dependence; dyadic data; inference; serial correlation |
| Date: | 2026–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:131300 |
| By: | Valentin Winkler |
| Abstract: | This paper studies state-dependent local projections (LPs). First, I establish a general characterization of their estimand: under minimal assumptions, state-dependent LPs recover weighted averages of causal effects. This holds for essentially all specifications used in practice. Second, I show that state-dependent LPs and VARs target different estimands and propose a simple VAR-based estimator whose probability limit equals the LP estimand. Third, in instrumental variable (LP-IV) settings, state-dependent weighting can generate nonzero interaction terms, even when the effects are not state-dependent. Overall, this paper shows how to correctly interpret state-dependent LPs, clarifying their connection to VARs and highlighting a key source of LP-IV misinterpretation. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01622 |
| By: | Ricardo E. Miranda |
| Abstract: | In this paper I derive a set of testable implications for econometric models defined by three assumptions: (i) the existence of strictly exogenous discrete instruments, (ii) restrictions on how the instruments affect adoption of a finite number of treatment types (such as monotonicity), and (iii) the assumption that the instruments only affect outcomes through their effect on treatment adoption (i.e. an exclusion restriction). The testable implications aggregate (via integration) an otherwise potentially infinite set of inequalities that must hold for every measurable subset of the outcome's support. For binary instruments the testable implications are sharp. Furthermore, I propose an implementation that links restrictions on latent response types to a generalization of first-order stochastic dominance and random utility models, allowing to distinguish violations of the exclusion restriction from violations of monotonicity-type assumptions. The testable implications extend naturally to the many instruments case. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.14464 |
| By: | Aknouche, Abdelhakim; Dimitrakopoulos, Stefanos; Rabehi, Nadia |
| Abstract: | A general class of seasonal autoregressive integrated moving average models (SARIMA), whose period is an independent and identically distributed random process valued in a finite set, is proposed. This class of models is named random period seasonal ARIMA (SARIMAR). Attention is focused on three subsets of them: the random period seasonal autoregressive (SARR) models, the random period seasonal moving average (SMAR) models and the random period seasonal autoregressive moving average (SARMAR) models. First, the causality, invertibility, and autocovariance shape of these models are revealed. Then, the estimation of the model components (coefficients, innovation variance, probability distribution of the period, (unobserved) sample-path of the random period) is carried out using the Expectation-Maximization algorithm. In addition, a procedure for random elimination of seasonality is developed. A simulation study is conducted to assess the estimation accuracy of the proposed algorithmic scheme. Finally, the usefulness of the proposed methodology is illustrated with two applications about the annual Wolf sunspot numbers and the Canadian lynx data. |
| Keywords: | EM algorithm, irregular seasonality, non-integer period, random period, random period seasonal ARMA models. |
| JEL: | C10 C18 C22 |
| Date: | 2025–12–06 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127200 |
| By: | Masahiro Kato |
| Abstract: | Estimating the Riesz representer is a central problem in debiased machine learning for causal and structural parameter estimation. Various methods for Riesz representer estimation have been proposed, including Riesz regression and covariate balancing. This study unifies these methods within a single framework. Our framework fits a Riesz representer model to the true Riesz representer under a Bregman divergence, which includes the squared loss and the Kullback--Leibler (KL) divergence as special cases. We show that the squared loss corresponds to Riesz regression, and the KL divergence corresponds to tailored loss minimization, where the dual solutions correspond to stable balancing weights and entropy balancing weights, respectively, under specific model specifications. We refer to our method as generalized Riesz regression, and we refer to the associated duality as automatic covariate balancing. Our framework also generalizes density ratio fitting under a Bregman divergence to Riesz representer estimation, and it includes various applications beyond density ratio estimation. We also provide a convergence analysis for both cases where the model class is a reproducing kernel Hilbert space (RKHS) and where it is a neural network. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.07752 |
| By: | Jeremy Zuchuat |
| Abstract: | Many recent studies use individual longitudinal data to analyze job search behaviors. Such data allow the use of fixed-effects models, which supposedly address the issue of dynamic selection and make it possible to identify the structural effect of time. However, using fixed effects can induce a sizable within-estimation bias if job search outcomes take specific values at the time job seekers exit unemployment. This pattern creates an undesirable mechanical correlation between the error term and the time regressor. This paper derives the conditions under which the fixed-effects estimator provides valid estimates of structural duration-dependence relationships. Using Monte Carlo simulations, we show that the magnitude of the bias can be extremely large. Our results are not limited to the job search context but naturally extend to any framework in which longitudinal data are used to measure the structural effect of time. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.06928 |
| By: | Jan Rosenzweig |
| Abstract: | Financial time series exhibit multiscale behavior, with interaction between multiple processes operating on different timescales. This paper introduces a method for separating these processes using variance and tail stationarity criteria, framed as generalized eigenvalue problems. The approach allows for the identification of slow and fast components in asset returns and prices, with applications to parameter drift, mean reversion, and tail risk management. Empirical examples using currencies, equity ETFs and treasury yields illustrate the practical utility of the method. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11201 |
| By: | Daisuke Kurisu; Yuta Okamoto; Taisuke Otsu |
| Abstract: | Difference-in-differences (DID) is one of the most popular tools used to evaluate causal effects of policy interventions. This paper extends the DID methodology to accommodate interval outcomes, which are often encountered in empirical studies using survey or administrative data. We point out that a naive application or extension of the conventional parallel trends assumption may yield uninformative or counterintuitive results, and present a suitable identification strategy, called parallel shifts, which exhibits desirable properties. Practical attractiveness of the proposed method is illustrated by revisiting an influential minimum wage study by Card and Krueger (1994). |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.08759 |
| By: | Sunil K Sapra (California State University, Los Angeles, CA, USA) |
| Abstract: | The paper demonstrates applications of machine learning techniques to economic data. The techniques include nonlinear regression, generalized additive models (GAM), regression trees, bagging, random forest, boosting, and multivariate adaptive regression splines (MARS). Their relative model fitting and forecasting performance are studied. Common algorithms for implementing these techniques and their relative merits and shortcomings are discussed. Performance comparisons among these techniques are carried out via their application to the current population survey (CPS) data on wages and Boston housing data. Overfitting and post-selection inference issues associated with these techniques are also investigated. Our results suggest that the recently developed adaptive machine learning techniques of random forests, boosting, GAM and MARS outperform nonlinear regression model with Gaussian errors and can be scaled to bigger data sets by fitting a rich class of functions almost automatically. |
| Keywords: | Generalized Additive Models, Multivariate Adaptive Regression Splines, Random Forests, Regression Trees, Semi-parametric Regression |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0594 |
| By: | Sergey Ivashchenko (Bank of Russia, Russian Federation) |
| Abstract: | The conventional practice in estimating DSGE models is to rely on seasonally adjusted data. While convenient, this approach distorts the microeconomic foundations of the model. An alternative is to model seasonality explicitly, but this often introduces severe misspecification. This paper proposes a middle ground: using year-over-year growth rates instead of quarter-over-quarter growth rates, which allows the model to endogenously determine the seasonal adjustment. This approach greatly improves forecast accuracy by more than 20% while keeping the internal consistency of the model. Moreover, we show that model misspecification and seasonal adjustment can offset each other, implying that seasonality should be treated as model-specific rather than imposed exogenously. Empirical results for U.S. and Russian data confirm that structural seasonality improves forecasting performance, and model fit relative to conventional seasonal adjustment methods. |
| Keywords: | DSGE; seasonality; structural modeling |
| JEL: | C13 C32 E32 E52 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bkr:wpaper:wps160 |
| By: | James Rice |
| Abstract: | I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds an It\^o SDE in the latent space of a variational autoencoder, allowing for flexible, continuous-time modeling of uncertainty while preserving a principled mathematical foundation. The drift and diffusion terms of the SDE are parameterized by neural networks, enabling data-driven inference and generalizing classical time series models to handle irregular sampling and complex dynamic structure. A central theoretical contribution is the co-parameterization of the adjoint state with a dedicated neural network, forming a coupled forward-backward system that captures not only latent evolution but also gradient dynamics. I introduce a pathwise-regularized adjoint loss and analyze variance-reduced gradient flows through the lens of stochastic calculus, offering new tools for improving training stability in deep latent SDEs. My paper unifies and extends variational inference, continuous-time generative modeling, and control-theoretic optimization, providing a rigorous foundation for future developments in stochastic probabilistic machine learning. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.05227 |
| By: | Timo Dimitriadis; Yannick Hoga |
| Abstract: | Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an "online" fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08598 |
| By: | Sergey Ivashchenko (Bank of Russia, Russian Federation) |
| Abstract: | This article proposes a technique for computing sign restrictions in large-scale models. The technique is applied to a Bayesian vector autoregression (BVAR) model with 16 industries (16 growth rates, 16 inflations), and the interest rate. The results demonstrate that the suggested tech- nique can yield different implications for the density of relevant measures compared to the con- ventional random draw approach. Shocks identification is more accurate for suggested approach in experiments with simulated from DSGE model data. The usage of industry specific data and identification of demand and supply shock have large influence on identification of MP-shocks. It reveals important elements of transmission mechanics of monetary policy including differences in magnitude and shape of responses on MP-shocks, differences in historical decomposition, differ- ences in importance of demand and supply shocks for interest rates dynamic. Variance decompo- sition shows decrease of relative importance of its own shocks to industries with switching from short-run to long-run decomposition. There are some similarities with input-output tables and some differences those open questions for future researches |
| Keywords: | sign-restriction, BVAR, VAR, SVAR |
| JEL: | C32 C51 E32 E52 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:bkr:wpaper:wps129 |
| By: | Hui Chen; Yuhan Cheng; Yanchu Liu; Ke Tang |
| Abstract: | Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility but are prone to overfitting and weak out-of-distribution generalization. We propose a theory-guided transfer learning framework that integrates structural restrictions from economic theory into machine learning models. The approach pre-trains a neural network on synthetic data generated by a structural model and then fine-tunes it using empirical data, allowing potentially misspecified economic restrictions to inform and regularize learning on empirical data. Applied to option pricing, our model substantially outperforms both structural and purely data-driven benchmarks, with especially large gains in small samples, under unstable market conditions, and when model misspecification is limited. Beyond performance, the framework provides diagnostics for improving structural models and introduces a new model-comparison metric based on data-model complementarity. |
| JEL: | C45 C52 G13 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34713 |
| By: | Matteo Barigozzi; Diego Fresoli; Esther Ruiz |
| Abstract: | Factor extraction from systems of variables with a large cross-sectional dimension, $N$, is often based on either Principal Components (PC)-based procedures, or Kalman filter (KF)-based procedures. Measuring the uncertainty of the extracted factors is important when, for example, they have a direct interpretation and/or they are used to summarized the information in a large number of potential predictors. In this paper, we compare the finite $N$ mean square errors (MSEs) of PC and KF factors extracted under different structures of the idiosyncratic cross-correlations. We show that the MSEs of PC-based factors, implicitly based on treating the true underlying factors as deterministic, are larger than the corresponding MSEs of KF factors, obtained by treating the true factors as either serially independent or autocorrelated random variables. We also study and compare the MSEs of PC and KF factors estimated when the idiosyncratic components are wrongly considered as if they were cross-sectionally homoscedastic and/or uncorrelated. The relevance of the results for the construction of confidence intervals for the factors are illustrated with simulated data. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04087 |
| By: | Koos B. Gubbels; Andre Lucas |
| Abstract: | We introduce a novel model for time-varying, asymmetric, tail-dependent copulas in high dimensions that incorporates both spectral dynamics and regularization. The dynamics of the dependence matrix' eigenvalues are modeled in a score-driven way, while biases in the unconditional eigenvalue spectrum are resolved by non-linear shrinkage. The dynamic parameterization of the copula dependence matrix ensures that it satisfies the appropriate restrictions at all times and for any dimension. The model is parsimonious, computationally efficient, easily scalable to high dimensions, and performs well for both simulated and empirical data. In an empirical application to financial market dynamics using 100 stocks from 10 different countries and 10 different industry sectors, we find that our copula model captures both geographic and industry related co-movements and outperforms recent computationally more intensive clustering-based factor copula alternatives. Both the spectral dynamics and the regularization contribute to the new model's performance. During periods of market stress, we find that the spectral dynamics reveal strong increases in international stock market dependence, which causes reductions in diversification potential and increases in systemic risk. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13281 |
| By: | Chiara Casoli (InsIDE Lab, DiEco, Universita' degli Studi dell'Insubria, Fondazione Eni Enrico Mattei); Riccardo Lucchetti (Dipartimento di Scienze Economiche e Sociali - Universita' Politecnica delle Marche) |
| Abstract: | The yield curve is widely regarded as a powerful descriptor of the economy and market expectations. A common approach to its statistical representation relies on a small number of factors summarizing the curve, which can then be used to forecast real economic activity. We argue that optimal factor extraction is crucial for retrieving information when considering an approximate factor model. By introducing a rotation of the model including cointegration, we reduce cross-sectional dependence in the idiosyncratic components. This leads to improved forecasts of key macroeconomic variables during periods of economic and financial instability, both in the US and the euro area. |
| Keywords: | Yield curve, Nelson-Siegel model, Dynamic Factor Model, cointegration, forecasting |
| JEL: | C32 C53 E43 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:anc:wpaper:503 |