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on Econometric Time Series |
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Issue of 2026–10–12
eleven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Fernando Roders (a.s.r. Asset Management); Siem Jan Koopman (Vrije Universiteit Amsterdam); Karim Moussa (Vrije Universiteit Amsterdam) |
| Abstract: | In this study, we propose to estimate the parameters of stochastic volatility models with stable disturbances using methods based on linearization. Specifically, we obtain a linear state space form of the model by taking the logarithm of the squared financial returns, which, together with analytical expressions for logarithmic and fractional lower order moments of the stable distribution, forms the basis of the proposed quasi-maximum likelihood and generalized method of moments estimators. The methods are straightforward to implement and do not require simulation. We provide details of the implementation and study the finite-sample performance of the two estimation methods in a Monte Carlo study. The empirical relevance is illustrated by studying model-implied volatility estimates from time series of daily returns from stock indices, exchange rates, and cryptocurrencies. |
| JEL: | C32 C58 G15 |
| Date: | 2026–10–05 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260082 |
| By: | Freddy García-Albán; Manuel González-Astudillo |
| Abstract: | This paper investigates which features of a Bayesian dynamic factor model improve U.S. GDP nowcasts and whether their contribution varies across historical episodes. Using pseudo-real-time information sets for 25 series, we estimate 16 combinations of dynamic heterogeneity, stochastic volatility, time-varying long-run growth, and a multiplicative outlier adjustment. We evaluate their marginal contributions to point, density, and quantile score forecasts using balanced paired factorial contrasts. Dynamic heterogeneity improves point, density, and tail forecasts, and it is the only feature that helps consistently across all three criteria. Stochastic volatility improves density and tail forecasts in some periods but not point forecasts consistently. Time-varying long-run growth lowers squared-error loss in some periods but worsens tail forecasts. The outlier adjustment does not improve point or density forecasts overall, has favorable but imprecisely estimated effects on upper-tail quantile scores during the pandemic, and worsens those scores afterward. |
| Keywords: | Bayesian dynamic factor models; forecast evaluation; stochastic volatility; longrun growth; outliers |
| JEL: | C11 C32 C53 E37 |
| Date: | 2026–10–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103878 |
| By: | Charisios Grivas (Aalborg University); Zacharias Psradakis (Birkbeck Business School); Marian Vavra (National Bank of Slovakia) |
| Abstract: | This paper considers tests for skewness, kurtosis and normality of the one-dimensional marginal distribution of a stationary fractional process, which may exhibit short memory, antipersistence or long memory. The autoregressive-sieve bootstrap is used to obtain Pvalues and critical values for tests based on studentized empirical coefficients of skewness and kurtosis. These studentized coefficients are constructed using variance estimators obtained by the same bootstrap procedure. Notably, such tests, as well as variants based on non-studentized statistics, can be implemented in a straightforward manner without requiring knowledge or estimation of the memory parameter of the process. Monte Carlo simulations show that the bootstrap-assisted tests perform well for sample sizes that are typical in applied settings. The methodology is further illustrated through an application to realized measures of stock-return volatility. |
| JEL: | C12 C22 C63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:svk:wpaper:1146 |
| By: | Feras A. Saad; Todd B. Walker |
| Abstract: | Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.36280 |
| By: | Jinyuan Chang; Jing He; Weiming Li; Chen Lin |
| Abstract: | We propose a new $L_2$-type test for white noise which allows the dimension $p$ of the time series to either (i) be a fixed constant, or (ii) diverge with the sample size $n$. The proposed test statistic exhibits an interesting phase transition, following two different regimes of behavior: $p$ is fixed, and $p\rightarrow\infty$. Because identification of the operable regime is difficult, if not impossible in practice, we devise a novel adaptive bootstrap method to construct unified testing procedure across different phases. Numerical experiments confirm the good finite sample performance of the proposed adaptive $L_2$-type test in comparison to the existing methods in the literature. The proposed testing procedure has been implemented in R package HDTSA. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.33418 |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Daanish Padha (Adam Smith Business School, University of Glasgow) |
| Abstract: | We extend the Three-Pass Regression Filter (3PRF) in two dimensions. First, we allow the factors driving the predictor panel to be weak. Second, we allow the idiosyncratic components of the predictors to contain additional predictive information for the forecasting target. We establish conditions under which 3PRF consistently recovers the target-relevant factor space under weak factor structures and characterize how its convergence rate depends on the relative strength of relevant and irrelevant factors. Stronger relevant factors improve the rate at which 3PRF approaches the infeasible factor forecast, whereas stronger irrelevant factors have the opposite effect. We use these results to compare 3PRF with Principal Component Regression (PCR) and identify settings in which either procedure has a relative advantage. To accommodate predictive information outside the common factor space, we augment 3PRF with a Least Absolute Shrinkage and Selection Operator (LASSO) regression on estimated idiosyncratic components, yielding the 3PRF LASSO estimator. We derive its prediction-error rate while accounting for the fact that the idiosyncratic regressors are generated from a first-stage factor estimator. Monte Carlo simulations support the asymptotic results and indicate strong finite-sample forecasting performance of 3PRF LASSO. In an empirical application involving forecasts of U.S. headline CPI inflation using FRED-QD data, 3PRF LASSO delivers competitive forecast performance. |
| Keywords: | Weak Factors; Forecasting; High-dimensional data; Supervision; Three pass regression filter; LASSO |
| JEL: | C18 C22 C53 C55 E27 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202607 |
| By: | Virginie Terraza (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier, uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg); Aslı Boru İpek (Kütahya Dumlupinar University = Kütahya Dumlupınar Üniversitesi [Kütahya, Turquie]) |
| Abstract: | This study proposes a multi-stage approach to modelling dependencies using Vector Autoregression (VAR), Nonlinear Autoregressive Neural Network (NAR-NN) models, and copulas. This paper aims to assess the dynamic dependency structure between cryptocurrencies and traditional financial assets, considering regime changes and safe-haven properties. The proposed methodology integrates change point detection with copula-based conditional correlation Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to identify structural breaks and nonlinear dependencies between regimes. A sequential change point procedure based on fast signal segmentation is employed to detect structural changes. According to this study, cryptocurrencies and traditional assets exhibit regimedependent dependence. It was also determined that during periods of market stress, this dependency significantly increases. This paper has significant implications for portfolio diversification, risk management, and the role of digital assets in financial markets. Furthermore, this proposed methodology contributes to the literature by capturing nonlinear, time-dependent, and regime-dependent dependency structures. |
| Keywords: | 62M10, 68T07, neural network MSC: 91G15, DCC-GARCH family models, VAR, copula, change point detection, financial markets, interconnections, interconnections financial markets change point detection copula VAR DCC-GARCH family models neural network MSC: 91G15 62M10 68T07 |
| Date: | 2026–09–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05752791 |
| By: | Guglielmo Maria Caporale; Sergio Di Nallo Serrano; Sergio E. García Bermejo; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines the cyclical structure of the monthly US unemployment rate using an approach that differs from the stationary I(0) and unit root cycle ones. In particular, the model specification allows for the spectrum to explode at a frequency away from zero. This long memory component enables us to approximate the length of the cycle such that shocks can have transitory but persistent effects. The empirical results show the adequacy of the proposed framework to capture the observed behaviour of the monthly US unemployment rate. |
| Keywords: | US unemployment rate, stochastic cycles, long memory |
| JEL: | C22 E24 J64 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13026 |
| By: | Mikihito Nishi; Takashi Yamagata |
| Abstract: | This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid two-point wild bootstrap as the serially independent case. The induced multiplier autocovariances determine the lag weights in a corresponding heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator, which coincides exactly with the conditional covariance of the bootstrap estimating-equation sum. We establish first-order bootstrap validity for asymptotically linear estimators by showing that the matched-HAC estimator consistently estimates the long-run covariance and that the bootstrap estimating-equation sum converges conditionally to the same Gaussian limit as its original-sample counterpart, yielding valid HAC-studentized $z$-tests and the corresponding Wald and Lagrange multiplier tests. Monte Carlo experiments in nonlinear generalized method of moments (GMM) and linear regression show that Rademacher DWB generally provides more accurate finite-sample size control for $z$-tests than the Mammen and Gaussian DWB. A GMM application to a nonlinear short-rate mean-reversion model illustrates the practical relevance of the proposed two-point DWB. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.32369 |
| By: | Anna B. Schmidt |
| Abstract: | This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from information across categories and improves its predictive performance as the dimensionality of the choice environment increases, whereas the mixed logit model does not exhibit the same pattern. MCMC delivers the highest predictive accuracy but is computationally intensive. VI achieves slightly lower predictive performance while substantially reducing runtime. In the empirical application, VI also outperforms the mixed logit benchmark. These findings highlight a trade-off between accuracy and computational feasibility in multi-category demand estimation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08409 |
| By: | Giulia Chiara; Stefano Lucarelli |
| Abstract: | This paper formalizes and empirically tests the causal transmission mechanism linking international capital centralization, macro-financial imbalances, protectionism, and systemic military escalation. We challenge the orthodox view of globalization as a self-stabilizing framework for peace, conceptualizing contemporary geoeconomic fragmentation through a theoretical framework stemming from the debate on global imbalances and the analytical revival of the Marxian tendential law of capital centralization. Integrating Structural Vector Autoregression (SVAR) and Bayesian Vector Autoregression (BVAR) models on historical time-series data (1998–2024), we estimate the dynamic interactions between net control, net international investment positions, a protectionism index, and military expenditure metrics across both a bilateral U.S.-China framework and an aggregated creditor-debtor group model. The empirical results confirm a statistically significant multi-stage causal sequence: an increase in capital centralization widens gaps in net international investment positions, prompting debtors to deploy protectionist measures to mitigate further capital centralization. Conversely, these protectionist shocks induce persistent, long-term positive impacts on the military expenditure of creditor states. Econometric evidence demonstrates that trade conflicts and rearmament are endogenous structural symptoms of the systemic motion of capital rather than exogenous diplomatic failures. |
| Keywords: | Capital centralization; Protectionism; Military expenditure; SVAR and BVAR models |
| JEL: | B51 F51 C32 P16 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:hpo:wpaper:1_2026 |