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on Econometric Time Series |
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Issue of 2026–06–08
thirteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Chebbi, Ali |
| Abstract: | Standard trend–cycle extraction methods in macroeconomics rely on assumptions of global smoothness and time-invariant dynamics that become restrictive in the presence of structural breaks and regime-dependent behavior. These limitations affect both cyclical measurement and the stability of inferred long-run relationships under cointegration. This paper develops a fuzzy clustering–based filtering framework that provides a regime-sensitive decomposition of macroeconomic time series. While fuzzy methods have been used in applied filtering, their asymptotic properties—particularly under structural breaks and cointegration—have not been formally characterized. The proposed approach assigns observations probabilistically across latent regimes, allowing for smooth transitions and mixed states. We establish √T-consistency of the cyclical component, vanishing endpoint bias, and preservation of cointegrating relationships. The filter is embedded in a regime-dependent cointegrated system (MS-F-VECM), allowing both short-run dynamics and long-run equilibria to vary across regimes. Monte Carlo simulations confirm strong finite-sample performance in terms of break detection and cointegration preservation. An application to Eurozone data (1999–2023) shows that standard measures of comovement are not invariant but reflect regime-dependent aggregation. The contribution is methodological: a unified framework for regime-dependent filtering and cointegration analysis. |
| Keywords: | Fuzzy filtering; asymptotic theory; structural breaks; cointegration; regime dependence; nonlinear time series. |
| JEL: | C22 C32 C51 E32 |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128903 |
| By: | Jiti Gao; Fei Liu; Bin Peng |
| Abstract: | This paper rigorously analyzes the properties of the local projection methodology within a high-dimensional framework, with a central focus on achieving robust long-horizon inference. We integrate a general dependence structure into h-step ahead forecasting models via a flexible specification of the residual terms. Additionally, we study the corresponding high-dimensional covariance matrix estimation, explicitly addressing the complexity arising from the long-horizon setting. Extensive Monte Carlo simulations are conducted to substantiate the derived theoretical findings. In the empirical study, we utilize the proposed high-dimensional local projection framework to study the impact of business news attention on U.S. industry-level stock volatility. |
| Keywords: | high-dimensional local projection, long-horizon analysis, h-step ahead forecasting models, covariance matrix estimation, high-dimensional time series, volatility spillovers |
| JEL: | C32 C53 C55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2026-1 |
| By: | Li-Hsien Sun; Zong-Yuan Huang; Yi-Ling Huang; Chi-Yang Chiu; Ning Ning |
| Abstract: | We study offline change-point estimation for time series data exhibiting nonlinear serial dependence. To address this problem, we propose a copula-based Markov chain model with Weibull marginal distributions, which is suitable for modeling nonnegative data such as event times and volatility measures. Nonlinear dependence is incorporated through the Clayton and Joe copulas, allowing the model to capture asymmetric lower-tail and upper-tail dependence structures, respectively. We derive the corresponding likelihood function and estimate the change point and model parameters using maximum likelihood estimation implemented through the Newton--Raphson algorithm. Confidence intervals are constructed via a parametric bootstrap Monte Carlo procedure. Extensive numerical studies are conducted to evaluate the finite-sample performance and robustness of the proposed method under different dependence structures and copula misspecification scenarios. The results demonstrate that the proposed estimators perform well in terms of RMSE and relative error, particularly for the estimation of the change point. An empirical application to the VIX index during the COVID-19 pandemic further illustrates the practical usefulness of the proposed approach in detecting structural changes in both the marginal distributions and serial dependence structure. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.29541 |
| By: | Hilde C. Bjornland; Nicolas Hardy; Dimitris Korobilis |
| Abstract: | We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5%relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions. |
| Keywords: | oil price forecasting, quantile regression, Bayesian VAR, tail risk, distributional forecasting |
| JEL: | C32 C53 Q41 Q47 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-39 |
| By: | Jayesh Chaudhary |
| Abstract: | Financial volatility exhibits substantial non-stationarity, making single-regime models inadequate for characterising changing market conditions. This paper proposes a triple-timeframe Markov-Switching GARCH (MS-GARCH) framework for volatility regime detection in EUR/USD across daily, four-hour, and hourly horizons. Three independent AR(1)-MS-GARCH models are estimated to capture macro, meso, and micro regime dynamics, while Filardo-style time-varying transition probabilities (TVTP) are incorporated at the shorter horizons through composite stress indicators. The resulting regime probabilities are combined through an outer-product construction into a 27-state cross-scale probability tensor. Using EUR/USD data from 2015-2025, the framework produces statistically distinct Calm, Turbulent, and Crisis regimes and achieves superior out-of-sample volatility forecasting performance relative to a conventional GARCH benchmark. The results suggest that volatility dynamics contain meaningful structure across multiple timescales and that modelling these scales separately provides a more informative representation of market conditions than a single-timescale approach. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.06190 |
| By: | Runyu Dai; Yasumasa Matsuda |
| Abstract: | Estimating large volatility matrices is essential to finance research in the era of big data. We propose a unified estimate that embeds a dynamic conditional correlation GARCH (DCC-GARCH) structure into a mixed factor model comprising both observable and weak latent factors, with the residual covariance estimated through an adaptively thresholded sparse matrix based on the extended Principal Orthogonal Complement Thresholding (ePOET) framework. The resulting method, termed DCC-ePOET, jointly captures pervasive signals from both types of factors and dynamic idiosyncratic co-volatilities. It resolves the singularity issue that arises in high-dimensional settings where the cross-sectional dimension N exceeds the serial dimension T, while remaining computationally feasible. Monte Carlo simulations confirm the good finite sample performance of DCC-ePOET across various dimensions. An out-of-sample minimum variance portfolio analysis using S&P 500 data demonstrates the usefulness of DCC-ePOET in practice. |
| Date: | 2026–05–17 |
| URL: | https://d.repec.org/n?u=RePEc:toh:dssraa:152 |
| By: | Giorgi Nikolaishvili (Wake Forest University); Noah D. Gade (Wake Forest University) |
| Abstract: | Impulse response analysis builds economic narratives by scanning a large set of coefficients for significant effects. Pointwise inference ignores this multiplicity, so the false rejection rate grows unbounded with the response family. Simultaneous inference bounds the probability of even a single false rejection, which yields increasingly uninformative results as the family expands. Researchers are left to choose between overstating their evidence and understating it. We propose false discovery and false coverage control as a more appropriate target: bounding the expected share of false rejections among responses declared significant, with calibrated post-selection confidence intervals. Neither guarantee deteriorates as the response family grows, so researchers are not penalized for investigating thoroughly. The procedure integrates into standard VAR and local projection bootstrap workflows. Applications show that this inference strategy recovers effects lost under simultaneous bands while discarding fragile pointwise findings, in some cases materially altering the economic narrative. |
| Keywords: | impulse response; multiple testing; false discovery; VAR; local projection |
| JEL: | C12 C22 C32 E00 |
| Date: | 2026–04–17 |
| URL: | https://d.repec.org/n?u=RePEc:ris:wfuewp:022594 |
| By: | Jin Seo Cho (Yonsei University); Peter C. B. Phillips (Yale University, University of Auckland & Singapore Management University) |
| Abstract: | In GMM estimation it is well known that if the number of moment conditions grows with the sample size, GMM asymptotics differ from the standard case with moment size fixed as the sample size tends to infinity. The present work explores infinite dimensional GMM estimation under various conditions on the moment conditions and the weight matrix. Our approach employs a partial sum process formed by the moment conditions to represent high dimensional moments and an invariance principle to capture the infinite dimensional asymptotics as the moment size grows. Next, the GMM weight matrix is assumed to converge to one of two kernels at the limit: a continuous kernel or the Dirac delta function. Combining these different conditions enables development of a large sample theory for most efficient GMM estimation. The effects of permuting the moment conditions on GMM efficiency are also explored. The resulting theory is applied to weak instrumental variable estimation and the Angrist and Krueger(1991) data are re-analyzed in an empirical application of the new methods. |
| Keywords: | Infinite dimensional GMM; Invariance principle; Neumann’s series expansion; Stochastic integral; Weak IV, 2SLS. |
| JEL: | C13 C18 C36 C55 E24 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2026rwp-289 |
| By: | Beniamino Hadj-Amar; Jack Jewson |
| Abstract: | The Bayesian approach provides powerful methods for variable selection. The ability to incorporate sparsity through prior beliefs and account for parameter uncertainty allows Bayesian variable selection to consistently identify which variables are active and exhibit strong finite-sample performance. However, Bayesian methods require the correct specification of full likelihoods for the data, and there is increasing awareness of the problems that model misspecification causes for variable selection. Current approaches to mitigate misspecification either require complex models, detracting from the interpretability of the variable selection task, or move outside rigorous Bayesian uncertainty quantification and provide no recognised method for variable selection. This paper establishes the model quasi-posterior as a principled tool for variable selection. |
| Keywords: | Bayesian variable selection, quasi-posterior, model misspecification, sparse regression, posterior consistency, Bayesian inference |
| JEL: | C11 C15 C52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2026-2 |
| By: | Vidal Llauradó, Joan |
| Abstract: | This paper develops a feasible estimator for the source-screened latent contagion object isolated in the first two papers and applies it to a balanced Oxford-Man realized-volatility panel of eight global equity indices. Starting from the reduced local Gaussian block experiment, it represents local alternatives by covariance derivatives, removes the target-only tangent space, and estimates the remaining source-screened component with a low-dimensional projected covariance-score GMM statistic. The paper derives the projected-score geometry, proves the associated local Gaussian efficiency, rough-regime projected-rank, pilot-adaptive transfer, and uniform minimax results, and validates the implementation in synthetic experiments using closed-form information and noncentrality constants. In the Oxford-Man application, estimated physical-measure roughness lies between about 0.04 and 0.09 across the panel, with H_P approximately 0.071 for SPX, while the full-sample directed contagion map is dense and economically informative through intensity ranking and rolling stability rather than sparse edge selection. The paper closes the trilogy with a feasible estimator, a validation protocol, and a real-data physical-measure application, while leaving matched option-panel P/Q classification for later work. |
| Keywords: | latent volatility contagion; projected score estimator; covariance score GMM; source-screened inference; rough volatility; realized volatility; Oxford-Man realized library; local Gaussian experiments; nuisance-orthogonal estimation; financial econometrics |
| JEL: | C13 C14 C58 G12 G17 |
| Date: | 2026–04–12 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128738 |
| By: | Gerdie Everaert (-) |
| Abstract: | Local projections (LPs) are a widely used method for estimating impulse responses. While LP estimators are consistent as the number of time periods T tends to infinity under standard conditions, they exhibit non-negligible bias in finite samples. This bias is particularly relevant in panel data settings, where the number of individuals N may be large but T relatively small. In this paper, we derive an analytical expression for the incidental-parameters bias in the LP estimator with individual fixed effects. We show that this bias exhibits a non-monotone pattern across the projection horizon: it increases at intermediate horizons, where it can substantially exceed the standard dynamic panel bias even for moderate T, before declining at longer horizons. We propose an iterative bias-correction procedure that, when suitably initialized, effectively eliminates the incidental-parameters bias across the entire projection horizon. |
| Keywords: | Local projections, panel data, fixed effects, incidental parameters bias, bias correction |
| JEL: | C32 C33 C13 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:rug:rugwps:26/1145 |
| By: | Eduard Gracia (Universitat de Barcelona) |
| Abstract: | Under very general conditions, the best predictor of any random variable’s observed time series is not its mean but its median. Hence, if we aim to model a variable with a skewed (a.k.a. asymmetric) probability distribution, so mean and median diverge, it is the model’s predicted median path that must be compared to that variable’s observed time series. Thus e.g. rational economic agents base their decisions on their target variables’ expected (a.k.a. mean) paths, which must as a result follow certain rules (mainly no arbitrage); but, if those variables are skewedly distributed, irrational-looking observations may not reflect irrationality, for the median is not subject to the rules rationality imposes on the mean. Yet economic models rarely pose this hypothesis and, when they do, their skewness assumptions often present major theoretical and/or empirical drawbacks. This paper proposes instead to assume normally distributed (hence symmetric) random perturbations and then rely on economics’ standard nonlinear assumptions (e.g. diminishing returns, decreasing marginal utility, etc.) to skew relevant variables’ distributions endogenously. To put this analytical framework to work, we build three new, rational expectations, frictionless markets’ macroeconomic models (two for market bubbles and one for Tobin’s q) and prove their predictions fit the stylized facts better, and more comprehensively, than the standard models’ while relying on more general, parsimonious standard assumptions. |
| Keywords: | Bubbles, cycles, rationality, macroeconomics |
| JEL: | C53 E32 E44 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ewp:wpaper:497web |
| By: | Frank, Luis |
| Abstract: | The report proposes four alternatives to the standard X-13ARIMA-SEATS procedure (which uses the Henderson filter) for extracting the trend-cycle from the INDEC’s Monthly Estimator of Economic Activity (EMAE). The smoothing parameters of these alternatives were calibrated with the aim of emulating the official EMAE trend-cycle in two periods: 2004--2019 (excluding the impact of the pandemic) and 2004--2025 (including the pandemic). The results suggest using the Nadaraya-Watson (NW) kernel with bandwidths between 8 and 10, and Local Linear Regression (LLR) with bandwidths between 10 and 12. Both alternatives clearly outperform the LOESS method and deliver highly competitive results -- albeit slightly inferior -- compared to the Hodrick-Prescott filter (with $\lambda$ between 100 and 500). Additionally, it was observed that the nonparametric filters (NW and LLR) provide more stable and realistic adjustments in the presence of extreme shocks, such as the COVID-19 pandemic. This is because they are not affected by the outlier exclusion procedure performed by X-13ARIMA-SEATS on the seasonally adjusted series. At the ends of the series, Local Linear Regression (LLR) shows better behavior than the NW kernel, which suffers from truncation bias. In conclusion, it is recommended to extract the trend-cycle using the NW method when working with volatile series or those with frequent shocks, and to use LLR for more stable series or when greater accuracy at the final end of the series is prioritized. |
| Keywords: | trend-cycle, non-parametric filters, Hodrick-Prescott filter, EMAE |
| JEL: | C82 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129296 |