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on Econometric Time Series |
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Issue of 2026–04–27
twelve papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Aknouche, Abdelhakim; Francq, Christian; Goto, Yuichi |
| Abstract: | In this paper, we introduce flexible observation-driven Z-valued time series models constructed from mixtures of negative and non-negative components. Compared to models based on the standard Skellam distribution or on a difference of two integer-valued variables, our specification offers greater versatility. For example, it easily allows for skewness and bimodality. Furthermore, the observation of one component of the mixture makes interpretation and statistical analysis easier. We establish conditions for stationarity and mixing, and develop a mixed Poisson quasi-maximum likelihood estimator with proven asymptotic properties. A portmanteau test is proposed to diagnose residual serial dependence. The finite-sample performance of the methodology is assessed via simulation, and an empirical application on tick prices demonstrates its practical usefulness. |
| Keywords: | Discrete difference distribution; GARCH for tick-by-tick data, Mixed difference; Mixed Poisson QMLE; Random-weighting bootstrap; Z-valued time series. |
| JEL: | C12 C13 C22 C25 C58 |
| Date: | 2026–03–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128358 |
| By: | Hilde C. Bjørnland; Nicolás 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. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bny:wpaper:0148 |
| By: | Gonzalo, Jesús; Pitarakis, Jean-Yves |
| Abstract: | We propose a two-step procedure for detecting sparse cointegration in high-dimensional singleequation models. First, we employ the adaptive lasso to identify the subset of integrated covariates driving the long-run equilibrium relationship. Second, we adopt an information-theoretic criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding reliance on asymptotic distributions. A key theoretical contribution is demonstrating that this residualbased decision rule remains consistent regardless of the internal cointegration structure among the right-hand side predictors themselves. Monte Carlo experiments confirm the procedure'srobust finite-sample performance under endogeneity, serial correlation, and rank deficiency in the regressor matrix. |
| Keywords: | Cointegration; High dimensional data; Adaptive lasso; Unit roots |
| JEL: | C32 C52 |
| Date: | 2026–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:49894 |
| By: | Hardy, Nicolas; Korobilis, Dimitris |
| Abstract: | Composite quantile regression (CQR) is a robust and efficient estimator under heavy-tailed and contaminated errors. Existing Bayesian extensions rely on working likelihoods that require latent-variable augmentation and can deliver poorly calibrated credible intervals. We develop generalized Bayesian CQR, which exponentiates the composite quantile loss directly, targeting the same objective as frequentist CQR. Because generalized Bayes replaces point optimization with posterior averaging over the loss surface, it is especially relevant under heavy-tailed errors where the composite quantile loss flattens near its minimum. In generalized Bayes posterior dispersion depends on a learning rate that we calibrate by matching marginal variances to their frequentist sandwich counterparts. The resulting credible intervals achieve near-nominal coverage in cross-sectional settings and substantially reduce the undercoverage of i.i.d.\ intervals under serial dependence, with a residual shortfall under high persistence that mirrors the finite-sample bias of frequentist HAC inference. The calibration has a closed-form solution under flat priors and extends to normal and spike-and-slab LASSO priors for shrinkage and variable selection. Sampling uses standard Metropolis-Hastings with no latent variables, achieving roughly 100-fold computational gains over likelihood-based Bayesian CQR at a common quantile grid. Monte Carlo experiments show competitive or improved point estimation relative to frequentist CQR, reliable coverage, and robust variable selection across Gaussian, heavy-tailed, and contaminated error distributions. An equity premium forecasting application demonstrates that the efficiency and robustness gains translate into economically meaningful improvements in out-of-sample portfolio performance. |
| Keywords: | Composite quantile regression, Gibbs posterior, Generalized Bayes, Learning rate calibration, Equity premium forecasting, Spike-and-slab priors |
| JEL: | C11 C14 C21 C52 C53 E37 G17 |
| Date: | 2026–04–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128752 |
| By: | Yukai Yang; Rickard Sandberg |
| Abstract: | Subsample-based estimation is a standard tool for achieving robustness to outliers in econometric models. This paper shows that, in dynamic time series settings, such procedures are fundamentally invalid under contamination, even under oracle knowledge of contamination locations. The key issue is that contamination propagates through the model's residual filter and distorts the estimation criterion itself. As a result, removing contaminated observations does not, in general, restore the uncontaminated objective or ensure consistency. We characterise this failure as a structural incompatibility between pointwise subsampling and residual propagation. To address it, we propose a propagation-compatible transformation of index sets, formalised through a patch removal operator that removes the residual footprint of contamination. Under suitable conditions, the proposed operator leaves the estimator asymptotically unchanged under the uncontaminated model, while restoring consistency for the clean-data parameter under contamination. The results apply to a broad class of residual-based estimators and show that valid subsample-based estimation in dynamic models requires explicit control of residual propagation. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.17676 |
| By: | Ana Maria Herrera; Elena Pesavento; Alessia Scudiero |
| Abstract: | We propose a clustered local projection (clustered LP) method to estimate impulse response functions in a class of time-varying models where parameter variation is linked to a low-dimensional matrix of observables. We show that the clustered LP recovers the conditional average response when the driving variables are exogenous and a weighted average of the conditional marginal effects when they are endogenous. We propose an iterative estimation method that first classifies the data using k-means, estimates impulse response functions via GMM, and evaluates differences across clustered LP estimates. Our Monte Carlo simulations illustrate the ability of clustered LP to approximate the conditional average response function. We employ our technique to examine how uncertainty influences the transmission of a contractionary monetary policy shock to the 5- and 10-year U.S. nominal Treasury yields. Our estimation results suggest macroeconomic and monetary policy uncertainty operate through complementary but distinct channels: the former primarily amplifies the risk compensation embedded in the term premium, while the latter governs the speed and persistence with which markets revise their expectations about the future rate path following a monetary policy shock. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.18778 |
| By: | Jos\'e Francisco Perles-Ribes |
| Abstract: | We propose a descriptive, realization-centred framework for detecting and characterising explosive and co-explosive behaviour in economic time series, which we term path-explosive behaviour. Departing from the data-generating-process (DGP) perspective that underlies recursive unit root testing, the approach operates directly on observable path properties of the realised series. Four diagnostic layers -- level geometry, growth rate dynamics, normalised curvature, and log-space behaviour -- yield statistics that discriminate between genuine self-reinforcing multiplicative growth and I(2) dynamics without distributional assumptions or asymptotic critical values. Two theoretically motivated absolute gate thresholds screen detected episodes before a composite intensity score is assigned. Co-explosive behaviour between pairs of series is assessed at the episode level through a Jaccard co-occurrence index and non-parametric intensity concordance measures. The theoretical motivation draws on the path dependence and planning irreversibility literatures to argue that, in settings where discrete institutional decisions shape growth trajectories, a realization-centred characterisation is epistemically more appropriate than a DGP-based test. A simulation study across four DGP regimes validates the framework's discriminating power and conservatism. An empirical application to real house prices, commodity prices, public debt, and Spanish tourism destinations illustrates the empirical content of the path-explosive concept and distinguishes it from speculative bubble detection. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.16186 |
| By: | Korobilis, Dimitris; Schroeder, Maximilian |
| Abstract: | This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of low, medium, and high economic policy uncertainty, as well as loose, median, and tight financial conditions. We show that the high uncertainty and tight financial conditions indexes have superior predictive ability for various measures of economic activity. In a high-dimensional exercise involving about 1000 daily financial series, we find that quantile factors also provide superior out-of-sample information compared to mean or median factors. |
| Keywords: | variational Bayes; penalized factors; quantile regression |
| JEL: | C11 C31 C32 C52 C53 |
| Date: | 2024–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128773 |
| By: | Mariko I. Ito; Hiroyuki Hasada; Yudai Honma; Takaaki Ohnishi; Tsutomu Watanabe; Kazuyuki Aihara |
| Abstract: | Market instability has been extensively studied using mathematical approaches to characterize complex trading dynamics and detect structural change points. This study explores the potential for early warning of market instability by applying the Dynamical Network Marker (DNM) theory to order placement and execution data from the Tokyo Stock Exchange. DNM theory identifies indicators associated with critical slowing down -- a precursor to critical transitions -- in high-dimensional systems of many interacting elements. In this study, market participants are identified using virtual server IDs from the trading system, and multivariate time series representing their trading activities are constructed. This framework treats each participant as an interacting element, thereby enabling the application of DNM theory to the resulting time series. The results suggest that early warning signals of large price movements can be detected on a daily time scale. These findings highlight the potential to develop practical DNM-based early-warning systems for large price movements by further refining forecasting horizons and integrating multiple time series capturing different aspects of trading behavior. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.21297 |
| By: | David Gunawan |
| Abstract: | Survey data are widely used to study how income inequality, poverty, and welfare evolve over time. A common practice is to estimate the income distribution separately for each year, treating annual observations as independent cross-sections. For population subgroups with relatively small sample sizes, however, this approach can produce unstable parameter estimates, imprecise inference for inequality and poverty measures, and potentially misleading posterior probabilities of Lorenz and stochastic dominance. This paper develops flexible Bayesian models for time-varying income distributions that borrow strength across adjacent years by allowing the parameters of income distributions to evolve dynamically. We consider a random walk specification and an extended model with shrinkage priors. The proposed framework yields coherent inference for the full income distributions over time, as well as for associated inequality measures, poverty indices, and dominance probabilities. Simulation studies show that, relative to independent year-by-year models, the proposed approach produces substantially more precise and stable inference, while avoiding spurious variation in welfare comparisons. An application to the Aboriginal and residents of the Australian Capital Territory (ACT) population subgroups in the Household, Income and Labour Dynamics in Australia survey shows that the dynamic models deliver improved inference for income distributions and related welfare measures, and can change conclusions about distributional dominance over time. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.21258 |
| By: | Jon Frost; Carlos Madeira; Yash Rastogi; Harald Uhlig |
| Abstract: | This paper introduces a framework for performing Bayesian inference using quantum computation. It presents a proof-of-concept quantum algorithm that performs posterior sampling. We provide an accessible introduction to quantum computation for economists and a practical demonstration of quantum-based posterior sampling for Bayesian estimation. Our key contribution is the preparation of a quantum state whose measurement yields samples from a discretised posterior distribution. While the proposed approach does not yet offer computational speedups over classical techniques such as Markov Chain Monte Carlo, it demonstrates the feasibility of simulating Bayesian inference with quantum computation. This work serves as a first step in integrating quantum computation into the econometrician's toolbox. It highlights both the conceptual promise and practical challenges – especially those related to quantum state preparation – in leveraging quantum computation for Bayesian inference. |
| Keywords: | quantum computing; Bayesian estimator; Bayesian inference; Markov chain Monte Carlo (MCMC) algorithms; Gibbs sampling |
| JEL: | C11 C20 C30 C50 C60 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1342 |
| By: | Sotirios D. Nikolopoulos |
| Abstract: | Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.15531 |