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on Econometric Time Series |
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Issue of 2026–08–31
thirteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Mariia Artemova (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam); Evgenii Vladimirov (Erasmus University Rotterdam) |
| Abstract: | We propose an extended score-driven (ESD) dynamic factor model (DFM) that can accommodate non-Gaussian innovations, nonlinear factor dynamics, and time-varying volatility. The main novelty of our model is a state equation that includes both lagged and contemporaneous scores, implying that factors are not predetermined. We show that this novel model nests both the classic (parameter-driven) state-space DFM as well as the more recent score-driven DFM, bridging the gap between these two model classes. Empirically, our ESD-DFM proves useful for working with (post-)COVID-19-era observations, which have posed substantial challenges for macroeconomic modeling. For instance, while the Federal Reserve Bank of Philadelphia suspended publication of its leading index due to pandemic-related anomalies, our model remains robust to such extreme observations and enables reliable computation of the index. We further apply the ESD-DFM to The Conference Board’s (TCB's) Coincident and Leading Economic Indices (CEI and LEI). When indices are constructed from the estimated factors, the unprecedented divergence between TCB's CEI and LEI observed during the post-pandemic period disappears: although the reconstructed LEI declines in 2022, it resumes an upward trajectory from the second half of 2023 through the end of 2025. |
| Keywords: | state-space model, observation-driven model, heavy-tailed distribution, coincident index, leading index |
| JEL: | C32 C38 C51 E32 |
| Date: | 2026–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260040 |
| By: | Inoue, Atsushi; Kilian, Lutz |
| Abstract: | Several recent studies have expressed concern that the Haar prior typically employed in estimating sign-identified VAR models is driving the prior about the structural impulse responses and hence their posterior. In this paper, we provide evidence that the quantitative importance of the Haar prior for posterior inference has been overstated. How sensitive posterior inference is to the Haar prior depends on the width of the identified set of a given impulse response. We demonstrate that this width depends not only on how much the identified set is narrowed by the identifying restrictions imposed on the model, but also depends on the data through the reduced-form model parameters. Hence, the role of the Haar prior can only be assessed on a case-by-case basis. We show by example that, when the identification is sufficiently tight, posterior inference based on a Gaussian-inverse Wishart-Haar prior provides a reasonably accurate approximation. |
| JEL: | C22 C32 C52 E31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19227 |
| By: | Ignacio Moreira Lara; Jan Pr\"user; Christoph Hanck |
| Abstract: | Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework that exploits the natural matrix structure of international macroeconomic data. By separating dependence across economic variables from dependence across countries, the framework provides a parsimonious representation that substantially reduces the dimensionality of large structural systems. We develop a Bayesian sampling algorithm for posterior inference that accommodates zero, sign, and ranking (magnitude) restrictions, allowing established SVAR identification schemes to be combined with a novel approach to identifying contemporaneous international spillovers. Applying the model to quarterly data for 15 economies, we find substantial heterogeneity in international shock transmission, with demand shocks playing a more prominent role than supply shocks in generating cross-country spillovers. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.00262 |
| By: | Ulrich Hounyo; Zhendong Li |
| Abstract: | Factor-MIDAS regressions forecast a low-frequency target by extracting common factors from a large panel of high-frequency predictors via principal component analysis (PCA). While PCA mitigates the curse of dimensionality, it relies on factor pervasiveness, an assumption often violated when factors are weak, as is common in macro-financial forecasting. We propose SsPCA-MIDAS, which integrates supervised scaled PCA (SsPCA) into the mixed-data sampling framework. We establish consistency and asymptotic normality under weak factors, permitting inference on the prediction target. Simulations show that SsPCA-MIDAS outperforms competing PCA-based and supervised methods, especially when weak factors are prevalent. Applying machine-learning techniques such as boosting to the cleaner factors it extracts yields further gains. An extensive application to U.S. macro-financial forecasting shows that SsPCA-MIDAS selects economically meaningful predictors and improves forecasts of GDP, inflation, unemployment, asset prices, and volatility. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12589 |
| By: | Koop, Gary; McIntyre, Stuart; Mitchell, James; Wu, Ping |
| Abstract: | This paper develops a method to incorporate micro data, available as repeated cross-sections, into macro VAR models to understand the distributional effects of macroeconomic shocks at business cycle frequencies. The method extends existing functional VAR models by “looking within” the micro distribution to identify the degree to which specific types of micro unit are affected by macro shocks. It does so by creating a pseudo-panel from the repeated cross-section and adding these pseudo individuals into the macro VAR. Jointly modeling the micro and macro data leads to a large (pseudo) VAR and we use Bayesian methods to ensure shrinkage and parsimony. Our application revisits Chang et al. (2024) and compares their functional VAR-based distributional impulse response functions with our proposed pseudo VAR-based ones to identify what types of individuals’ earnings are most affected by business cycle-type shocks. We find that the individuals exhibiting the strongest positive cyclical sensitivity are those in the lower tail of the earnings distribution, particularly men and those without a college education, as well as young workers. |
| Keywords: | Bayesian VAR; functional VAR; pseudo panel; earnings distribution; business cycle shocks |
| JEL: | C32 C53 E37 |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:eoe:escoed:escoe-dp-2026-04 |
| By: | Kyungsub Lee; Kennedy Titus Kayaki |
| Abstract: | This paper proposes a GARCH-type volatility model in which level-and-slope updates of a latent power-law kernel generate state-dependent decay of past shocks within a two-dimensional Markov state. We derive a joint Foster--Lyapunov condition and establish positive Harris recurrence and uniqueness of the invariant distribution. Simulations show substantial low-frequency persistence in log-squared innovations, especially near the diagnostic stability boundary. Empirically, the model captures a substantial portion of observed volatility persistence and delivers competitive out-of-sample forecast accuracy using only a two-dimensional Markov state. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.25189 |
| By: | Lison Christiaens; Julien Hambuckers; Alain Hecq |
| Abstract: | This paper studies the presence of noncausal dynamics in standard macro-finance VAR models and asks whether they reflect genuine nonfundamentalness or omitted information available to economic agents but unobserved by the econometrician. To that end, we introduce a factor-filtering mixed causal-noncausal VARX approach designed to account for common macroeconomic information. We assess its performance in simulated settings, while showing also that the generalized covariance (GCov) estimator correctly recovers causal and noncausal dynamics when using several lags. Empirically, we revisit the well-known Stock-Watson monetary policy (S)VAR and show that the noncausal components detected in the baseline specification largely disappear once common factors are filtered out. Finally, we compare impulse responses from the filtered and original data to assess the transmission of monetary policy shocks and show that filtering further removes the price puzzle. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.28131 |
| By: | Avishek Bhandari |
| Abstract: | Many questions across the sciences take the same form: several coupled series are observed together, and the analyst wants to know not merely that they move together but which one moves first, and how strongly. This paper sets out a complete method built on one organising idea: the direction of a coupled system is exactly the part of its behaviour that changes when the record is played backwards. Tools built on contemporaneous covariance alone (correlation matrices, distance measures, spanning trees, undirected centralities, principal components) carry no information about direction: a reversible system and a circulating one can share identical covariance at every sampling of the same point-in-time record. Formally, direction is a circulation matrix carried by the lagged covariance. Its vanishing is exactly statistical time reversibility for linear systems, feature maps carry the characterisation to nonlinear ones, and under the Gaussian benchmark its magnitude is an entropy-production functional of the identified circulation, the quadratic component of the divergence per unit time between the forward and reversed records. Around this estimand we build a cross-fitted estimator removing first-order bias, delete-block jackknife standard errors, and a randomisation test exact under its stated block null, with a familywise correction and a nonlinear extension. A sampling theory says when the arrow is measurable at all, and a design layer separates transmission from the ordering of clocks. A laboratory of four systems with known answers compares the method with correlation networks, Granger causality, transfer entropy, and connectedness indices, reporting the failures of each when read as a measure of direction, including our own. Complete algorithms and worked examples in two languages make the paper the base reference for a series of applications. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.13431 |
| By: | Jeron Tan Kang |
| Abstract: | This paper compares direct-yield and factor-based approaches to U.S. Treasury yield curve forecasting using a common high-dimensional macroeconomic information set. Forecasts are evaluated on monthly zero-coupon yields over the 2015-2025 out-of-sample period. Gains over the random walk are concentrated at short maturities and in slope forecasts, and decline with the forecast horizon. Direct-yield models perform best for slope forecasts and are relatively stronger at short horizons, while factor-based models become more competitive at longer horizons. Macroeconomic predictors provide clear incremental predictive power, strongest for slope-related movements. A trading simulation reinforces that macro-augmented models perform best in slope trades. The simulation also highlights a gap between statistical and economic performance, as the random walk is a strong benchmark under statistical loss but performs poorly as a trading signal. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.07536 |
| By: | Zhuohan Wang; Carmine Ventre |
| Abstract: | Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data. Their appeal is both structural and practical: they offer stable likelihood-based training, strong mode coverage, flexible conditioning, and a stochastic-differential-equation formulation that aligns naturally with the It\^o calculus and stochastic control frameworks widely used in finance. This survey reviews the growing literature on diffusion-family generative models for financial applications. We organize prior work primarily by financial data type, covering time series, limit order books, tabular data, and other structured financial objects, while discussing the modeling goals and application contexts that arise within each category. To the best of our knowledge, this is the first survey dedicated specifically to diffusion-family models for financial data. For more detailed information, we have open-sourced a repository https://github.com/ZhuoHan1998/Diffusion -Models-In-Finance. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12583 |
| By: | Thomas R. Cook; Mariia Dzholos; Johannes Matschke |
| Abstract: | Trade flows are volatile and notoriously difficult to predict. This paper introduces trimmed import and export growth rates for the United States to help improve trade forecasts. We calculate the growth rates as a trimmed mean that strips out subcomponents with less predictive power. By separating the signal from the noise, our trimmed growth rates provide a better perspective on where aggregate imports or exports are heading, particularly at horizons four to 12 months ahead. Consequently, out-of-sample forecasts based on our trimmed growth rates reduce forecast errors by up to 15 percent relative to forecasts from aggregate import or export growth. These trimmed forecasts are simple to compute, outperform both naive forecasts and models that rely on a richer information set, and are particularly useful during episodes with heightened volatility. We update the trimmed growth rates monthly and make them publicly available. |
| Keywords: | imports and exports; forecasting; signal extraction |
| JEL: | F17 C22 |
| Date: | 2026–08–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:103656 |
| By: | Damiano Brigo; Vladimir Lucic |
| Abstract: | This article studies the conditional-density equation and its pathwise transformation in local stochastic rough volatility models, with rough Heston (rHeston) as the main explicit example. Under the stated common-filtration, measurability, predictability and spatial-regularity assumptions, we show that the Ito-Wentzell random-PDE reduction of the conditional density SPDE remains valid under local stochastic rough volatility. After fixing a common-environment realization and the associated stochastic flow, the transformed equation becomes a deterministic PDE with path-dependent coefficients. This yields a pathwise Fokker--Planck formulation that connects naturally with Rao--Blackwellized calibration. In the pure rough Heston case, the transformed coefficients simplify and the conditional density admits an explicit lognormal form. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.27588 |
| By: | Saad Mouti |
| Abstract: | We measure volatility roughness across asset classes using a common data infrastructure and pipeline. Our data covers 3, 926 United States equities, 34 CME futures roots, rates, FX, and commodities, and options on 44 underlyings over 2010-2025. Realized volatility is rough everywhere. The class-median Hurst estimate ranges from $0.05$ (livestock) through $0.07-0.10$ (rates, FX, agriculture, energy, metals) to $0.13$ (single stocks) and $0.20$ (equity indices). The option-implied measure identifies $H$ only where the leverage effect produces a clean skew term structure. For the equity indices, implied estimates of $0.21-0.28$ are just above realized volatility $H$, while for rates and FX the ATM skew regression fails with an R-squared near zero even though realized volatility remains rough. We also show a mean-reversion contamination formula for the second-moment estimator of the roughness for the stationary fractional Ornstein-Uhlenbeck process. The local slope of the increment second moment deviates from $2H$ by $(1-H)\Gamma(2H+1)(\kappa\Delta)^{2-2H}$ for all $H\in(0, 1)$. A correction framework for the second moment, when the log realized volatility measure has additive noise, raised the $H$ estimate slightly but nowhere near the Brownian diffusion framework. Finally, a failure taxonomy discusses where rough-volatility methods apply and where they fail, suggesting alternative paths to further explore the rough-volatility paradigm. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.16749 |