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on Econometric Time Series |
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Issue of 2025–10–20
seven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Alain Hecq; Ivan Ricardo; Ines Wilms |
| Abstract: | We propose a pseudo-structural framework for analyzing contemporaneous co-movements in reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector-autoregressive (VAR) models that would discard the matrix structure, our formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row-column) interactions across the matrix-valued time series. Our estimator admits standard asymptotic inference and we propose a BIC-type criterion for the joint selection of the reduced ranks and the autoregressive lag order. We validate the method's finite-sample performance in terms of estimation accuracy, coverage and rank selection in simulation experiments, including cases of rank misspecification. We illustrate the method's practical usefelness in identifying co-movement structures in two empirical applications: U.S. state-level coincident and leading indicators, and cross-country macroeconomic indicators. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.19911 |
| By: | Luisa Bisaglia (University of Padua); Margherita Gerolimetto (Ca’ Foscari University of Venice); Margherita Palomba (University of Padua) |
| Abstract: | This paper introduces a novel combined bootstrap methodology for the analysis of stationary long-memory time series, addressing the challenges posed by their persistent dependence structures. Unlike existing hybrid approaches that merge algorithms at the procedural level, our method combines independently generated bootstrap samples from a variety of established techniques, including parametric, semi-parametric, and block-based methods, into a unified composite sample. This integration is performed using both simple (mean, median, trimmed mean) and performance-based (correlation, MSE, MAE, regression-based) combination schemes. Through extensive Monte Carlo simulations and empirical applications to the Nile River minima and Microsoft stock returns, we show that the combined bootstrap approach yields improved estimation accuracy for the long-memory parameter d, particularly in terms of root mean squared deviation and confidence interval coverage. The proposed method is shown to mitigate model misspecification risk and improve inference robustness. While our focus is on estimating the long-memory parameter, the approach is general and can be extended to other statistics and dependence structures. This work offers a new perspective on bootstrap methodology and opens avenues for future theoretical and practical advancements. |
| Keywords: | Bootstrap, Long-memory time series, Pre-filtering, Combinations |
| JEL: | C22 C15 C13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:19 |
| By: | Dimitris Korobilis |
| Abstract: | I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structural shocks as latent factors, enabling computationally efficient estimation in high-dimensional settings through a straightforward Gibbs sampler. By incorporating time variation in the effects of monetary policy while maintaining tractability, the methodology offers a flexible and scalable approach to empirical macroeconomic analysis using BVARs, well-suited to handle data irregularities observed in recent times. Applied to the U.S. economy, I identify monetary shocks using a combination of high-frequency surprises and sign restrictions, yielding results that are robust across a wide range of specification choices. The findings indicate that the Federal Reserve’s influence on disaggregated consumer prices fluctuated significantly during the 2022–24 high-inflation period, shedding new light on the evolving dynamics of monetary policy transmission. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bny:wpaper:0140 |
| By: | Bellocca, Gian Pietro Enzo; Garrón Vedia, Ignacio; Rodríguez Caballero, Carlos Vladimir; Ruiz Ortega, Esther |
| Abstract: | In the context of macroeconomic/financial time series, the FARS package provides a comprehensive framework in R for the construction of conditional densities of the variable of interest based on the factor-augmented quantile regressions (FA-QRs) methodology, with the factors extracted from multi-level dynamic factor models (ML-DFMs) with potential overlapping group-specific factors. Furthermore, the package also allows the construction of measures of risk as well as modeling and designing economic scenarios based on the conditional densities. In particular, the package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the FA-QRs together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; and (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed. |
| Keywords: | Multi-level dynamic factor model; Quantile regression; Scenario analysis; R |
| Date: | 2025–10–13 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:48180 |
| By: | Patrick Fève; Alban Moura |
| Abstract: | We propose to measure business cycles using vector autoregressions (VARs). Our method builds on two insights: VARs automatically decompose the data into stable and unstable components, and variance-based shock identification can extract meaningful cycles from the stable part. This method has appealing properties: (1) it isolates a well-defined component associated with typical fluctuations; (2) it ensures stationarity by construction; (3) it targets movements at business-cycle frequencies; and (4) it is backward-looking, ensuring that cycles at each date only depend on current and past shocks. Since most existing filters lack one or more of these features, our method offers a valuable alternative. In an empirical application, we show that the two shocks with the largest cyclical impact effectively capture postwar U.S. business cycles and we find a tighter link between real activity and inflation than previously recognized. We compare our method with standard alternatives and document the plausibility and robustness of our results. |
| Keywords: | business cycles, detrending, filtering, shocks, vector autoregressions. |
| JEL: | C32 E32 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bcl:bclwop:bclwp201 |
| By: | Aryan Manafi Neyazi |
| Abstract: | This paper investigates the properties of the Generalized Covariance (GCov) estimator under misspecification and constraints with application to processes with local explosive patterns, such as causal-noncausal and double autoregressive (DAR) processes. We show that GCov is consistent and has an asymptotically Normal distribution under misspecification. Then, we construct GCov-based Wald-type and score-type tests to test one specification against the other, all of which follow a $\chi^2$ distribution. Furthermore, we propose the constrained GCov (CGCov) estimator, which extends the use of the GCov estimator to a broader range of models with constraints on their parameters. We investigate the asymptotic distribution of the CGCov estimator when the true parameters are far from the boundary and on the boundary of the parameter space. We validate the finite sample performance of the proposed estimators and tests in the context of causal-noncausal and DAR models. Finally, we provide two empirical applications by applying the noncausal model to the final energy demand commodity index and also the DAR model to the US 3-month treasury bill. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.13492 |
| By: | Yifan He; Svetlozar Rachev |
| Abstract: | This study presents a comprehensive empirical investigation of the presence of long-range dependence (LRD) in the dynamics of major U.S. stock market indexes--S\&P 500, Dow Jones, and Nasdaq--at daily, weekly, and monthly frequencies. We employ three distinct methods: the classical rescaled range (R/S) analysis, the more robust detrended fluctuation analysis (DFA), and a sophisticated ARFIMA--FIGARCH model with Student's $t$-distributed innovations. Our results confirm the presence of LRD, primarily driven by long memory in volatility rather than in the mean returns. Building on these findings, we explore the capability of a modern deep learning approach, Quant generative adversarial networks (GANs), to learn and replicate the LRD observed in the empirical data. While Quant GANs effectively capture heavy-tailed distributions and some aspects of volatility clustering, they suffer from significant limitations in reproducing the LRD, particularly at higher frequencies. This work highlights the challenges and opportunities in using data-driven models for generating realistic financial time series that preserve complex temporal dependencies. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.19663 |