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on Econometric Time Series |
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Issue of 2026–03–23
nine papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Alain Guay; Dalibor Stevanovic |
| Abstract: | This paper develops a spectral framework for identification, estimation, and inference in non-Gaussian Structural Vector Autoregressive (SVAR) models using higher-order cumulants. Under independence or the absence of cross-cumulants, cumulant tensors of whitened innovations admit an orthogonal decomposition whose singular vectors recover the structural shocks. Identification is therefore governed by the spectral geometry of the population cumulant ten- sor. In particular, separation of tensor singular values provides a quantitative measure of identification strength through explicit perturbation bounds linking estimation error to the inverse singular-value gap. This characterization yields asymptotic normality under strong identification and nonstandard limits under local-to-weak identification sequences. We derive asymptotic distributions for tensor SVD estimators and show how statistically identified subsystems can be completed using conventional structural restrictions. Monte Carlo experiments and empirical applications illustrate the finite-sample properties and empirical relevance of the approach. Cet article développe un cadre spectral pour l’identification, l’estimation et l’inférence dans les modèles SVAR (Structural Vector Autoregressive) non gaussiens à l’aide de cumulants d’ordre supérieur. Sous l’hypothèse d’indépendance ou d’absence de cumulants croisés, les tenseurs de cumulants des innovations blanchies admettent une décomposition orthogonale dont les vecteurs singuliers permettent de retrouver les chocs structurels. L’identification est ainsi gouvernée par la géométrie spectrale du tenseur de cumulants de la population. En particulier, la séparation des valeurs singulières du tenseur fournit une mesure quantitative de la force de l’identification grâce à des bornes explicites de perturbation reliant l’erreur d’estimation à l’inverse de l’écart entre les valeurs singulières. Cette caractérisation conduit à une normalité asymptotique sous identification forte et à des lois limites non standard dans des séquences d’identification localement faibles. Nous dérivons les distributions asymptotiques des estimateurs fondés sur la SVD tensorielle et montrons comment des sous-systèmes statistiquement identifiés peuvent être complétés à l’aide de restrictions structurelles conventionnelles. Des expériences de Monte Carlo et des applications empiriques illustrent les propriétés en échantillon fini et la pertinence empirique de l’approche. |
| Keywords: | Non-Gaussian SVAR, tensor decomposition, cumulants, SVAR non gaussien, décomposition tensorielle, cumulants |
| JEL: | C12 C32 C51 |
| Date: | 2026–03–09 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2026s-02 |
| By: | Timothy J. Vogelsang |
| Abstract: | This paper develops inference methods for ratios of deterministic trend slopes in systems of pairs of time series. Hypotheses based on linear cross-equation restrictions are considered with particular interest in tests that trend ratios are equal across pairs of trending series. Tests of equal ratios can be used for the empirical assessment of climate models through comparisons of trend ratios (amplification ratios) of model generated temperature series and observed temperature series. The analysis in this paper builds on the estimation and inference methods developed by Vogelsang and Nawaz (2017, Journal of Time Series Analysis) for a single pair of trending time series. Because estimators of ratios can have poor finite sample properties when the trend slope are small relative to variation around the trends, tests of equal trend ratios are restated in terms of products of trend slopes leading to inference that is less affected by small trend slopes. Asymptotic theory is developed that can be used to generate critical values. For tests of equal trend ratios, finite sample performance is assessed using simulations. Practical advice is provided for empirical practitioners. An empirical application compares amplification ratios (trend ratios) across a set of five groups of observed global temperature series. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.23482 |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Yifeng Chen (Department of Economics, Nanyang Technological University, Singapore 639798); Seok Young Hong (Department of Economics, Nanyang Technological University, Singapore 639798); Daniel Tsvetanov (Norwich Business School, University of East Anglia, Norwich NR4 7TJ, UK) |
| Abstract: | We develop an empirical likelihood framework for testing return predictability in the conditional mean and conditional quantiles. A unified chi-square limit theory is established across a broad spectrum of predictor persistence, including stationary, mildly integrated, nearly integrated, unit-root, and mildly explosive cases. We provide two complementary approaches to handle the unknown intercept: (i) a sample-splitting approach under relaxed regularity conditions and (ii) a new two-stage method that improves efficiency and accommodates quantile inference, where sample-splitting is infeasible. We examine the finite-sample bias of the two-stage method, and propose a bias-correction scheme and gradually saturated weights that improve performance under high persistence. Simulation evidence demonstrates that our tests exhibit competitive size and power across persistence classes, with notable gains in quantile predictability. An empirical application to the U.S. stock market shows modest evidence of mean predictability, whereas quantile-based inference reveals stronger and economically relevant predictability in the tails of the return distribution. |
| Keywords: | Predictive Mean Regression; Predictive Quantile Regression; Empirical Likelihood; Bartlett Bias Correction. |
| JEL: | C12 C32 C51 C52 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202609 |
| By: | Batuhan Koyuncu; Byeungchun Kwon; Marco Jacopo Lombardi; Fernando Perez-Cruz; Hyun Song Shin |
| Abstract: | This article introduces the BIS Time-series Regression Oracle (BISTRO), a general purpose time series model for macroeconomic forecasting. Its edge over traditional econometric approaches lies in its ability to deal with generic unconditional and conditional forecasting tasks, without requiring to adjust the model to the macroe conomic tasks being tackled. Building on the transformer architecture underlying LLMs, BISTRO is fine-tuned on the large repository of macroeconomic data main tained at the BIS. We show that BISTRO provides reliable unconditional forecasts for key macroeconomic aggregates and illustrate how using it for conditional fore casting can help unveiling patterns of nonlinearity in the data. |
| Keywords: | forecasting, scenarios, large language models |
| JEL: | C32 C45 C55 C87 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1337 |
| By: | Dobrislav Dobrev; Pawel J. Szerszén |
| Abstract: | Replacing erroneous observations with missing values is known to mitigate outlier-induced distortions in state-space model inference. Yet, in economic data, outliers can be small and difficult to detect, while still occurring in temporal clusters and generating persistent distortions. We therefore put forward an unsupervised approach for exogenously randomized substitution of missing data (RMDX), designed as an ensemble-averaging enhancement that can be used to improve the robustness of any filter also to more elusive outliers. Our bias-variance decomposition theory for RMDX ensemble averaging establishes that, under mild regularity conditions on the influence of outliers, the missing data randomization rate acts as a regularization parameter, which can be set optimally to minimize mean squared error loss using standard cross-validation. We corroborate these theoretical results using Monte Carlo simulations, which show that RMDX ensemble averaging can substantially enhance the performance of commonly used robust filters, including ones that rely on supervised missing data substitution upon exceeding outlier detection thresholds. As anticipated, the gains are most pronounced in the presence of patches of moderately sized outliers that are difficult to mitigate. To further assess empirical relevance in economics, we also document that RMDX-enhanced filters perform favorably in widely used state-space models for extracting inflation trends, where clusters of measurement outliers in inflation data are known to pose an extra challenge. |
| Keywords: | State-space models; outlier-robust filtering and forecasting; missing data randomization; bagging and ensemble averaging; bias-variance tradeoff. |
| JEL: | C15 C22 C53 E37 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-004 |
| By: | Amir Ahmadi, Pooyan; Matthes, Christian; Wang, Mu-Chun |
| Abstract: | The effects of monetary policy shocks are regularly estimated using high-frequency sur- prises in asset prices around central bank meetings as an instrument. These studies, insofar as they explicitly model the relationship between instrument and structural shock, assume a constant relationship between the instrument and the monetary policy shock. By allowing for time variation in this relationship, we show that only a few distinct periods are infor- mative about monetary policy shocks. Therefore, we build a narrative for instrument-based identification. For the instrument in Gertler & Karadi (2015), the effect on the (log) price level is almost 50 percent larger than the standard specification would suggest. |
| Keywords: | High-Frequency Identification, Instruments, Monetary Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:338091 |
| By: | Katarzyna Chec; Bartosz Uniejewski; Rafal Weron |
| Abstract: | Electricity demand forecasts are crucial for power system operations. Market participants frequently rely on day-ahead predictions provided by Transmission System Operators (TSOs), but these can be systematically biased and - as recent studies report - may be improved using parsimonious autoregressive models. Despite the fact that many operational and economic decisions require well-calibrated uncertainty estimates, previous work has focused on point forecasts. The key question is how to derive accurate quantile and density predictions. Here we show that processing TSO forecasts with the Least Absolute Shrinkage and Selection Operator (LASSO) brings further accuracy gains and provides strong inputs for probabilistic forecasts. Drawing on ten years of data (2016-2025) from three European and North American power markets, we find that Generalized Additive Models for Location, Scale, and Shape (GAMLSS) deliver consistently better probabilistic performance than commonly used econometric and machine learning approaches. Together, these findings highlight how regularization and flexible distributional modeling can improve uncertainty quantification of electricity demand. |
| Keywords: | Electricity demand; Day-ahead market; LASSO; Probabilistic forecasting; GAMLSS |
| JEL: | C22 C45 C51 C52 C53 Q41 Q47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ahh:wpaper:worms2601 |
| By: | Wissal Zribi (ICL - Institut Catholique de Lille - UCL - Université catholique de Lille); Talel Boufateh; Duc K. Nguyen; Thomas Walther |
| Keywords: | Structure threshold VAR, SV, varying SVAR, time, carbon efficiency, Carbon prices, Uncertainty |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05493722 |
| By: | Maxime Menuet (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | This paper studies how the temporal structure of adjustment shapes evolutionary dynamics in symmetric games. We introduce a fractional replicator dynamic that modifies the classical replicator only through the time operator, replacing the ordinary derivative with a fractional derivative of order α ∈ (0, 1]. This formulation preserves payoff monotonicity, feasibility, and the equilibrium set, while allowing past payoff differences to affect current behavior through long memory with power-law decay. We show that fractional time fundamentally alters local stability and equilibrium selection. While evolutionarily stable strategies remain locally asymptotically stable, equilibria that are unstable under the classical replicator can become locally stable when memory is sufficiently persistent, generating stability switching and purely temporal bifurcations without any change in payoffs or strategic interaction. Moreover, convergence toward stable equilibria becomes polynomial rather than exponential, implying slow adjustment even in simple games. As a consequence, equilibrium selection may fail to be completed over economically relevant horizons despite being guaranteed asymptotically. Finally, we provide a microfoundation based on standard payoff-monotone revision processes with heterogeneous and asynchronous revision opportunities. Fractional dynamics thus offer a parsimonious way to incorporate persistent memory into evolutionary models while preserving their core economic structure. |
| Keywords: | Fractional replicator dynamics; Evolutionary game theory; Fractional calculus; Long memory; Stability switching; Equilibrium selection |
| JEL: | C72 C73 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2026-05 |