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on Econometric Time Series |
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Issue of 2026–03–30
ten papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Fei Shang (Guangdong University of Foreign Studies); Tomasz Wo\'zniak (University of Melbourne) |
| Abstract: | We propose a structural vector autoregressive model with a new and flexible specification of the volatility process which we call Sparse Heterogeneous Markov-Switching Heteroskedasticity. In this model, the conditional variance of each structural shock changes in time according to its own Markov process. Additionally, it features a sparse representation of Markov processes, in which the number of regimes is set to exceed that of the data-generating process, with some regimes allowed to have zero occurrences throughout the sample. We complement these developments with a definition of a new distribution for normalised conditional variances that facilitates Gibbs sampling and identification verification. In effect, our model: (i) normalises the system and estimates the structural parameters more precisely than popular alternatives; (ii) can be used to verify homoskedasticity reliably and, thus, inform identification through heteroskedasticity; and (iii) features excellent forecasting performance comparable with Stochastic Volatility. Finally, revisiting a prominent macro-financial structural system, we provide evidence for the identification of the US monetary policy shock via heteroskedasticity, with estimates consistent with those reported in the literature. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.16035 |
| By: | Guglielmo Maria Caporale; Antonio Fons Palomares; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines long-run linkages and possible instabilities in the gold–silver price relationship using daily futures prices over the period from 4 January 2010 to 28 November 2025. The empirical analysis includes unit-root and cointegration tests as well as endogenous structural break tests, namely the Pruned Exact Linear Time (PELT) algorithm applied to the Engle–Granger residuals and the Bai–Perron endogenous break test, both detecting a break in late 2017. Standard cointegration tests fail to detect a stable long-run equilibrium over the full sample and the pre-break subsample, while one is found in the post-break subsample. Further, the Local Whittle fractional integration method provides evidence of a high degree of persistence consistent with long-memory dynamics. The estimation of a Fractionally Cointegrated VAR (FCVAR) model corroborates the previous findings: although full-sample evidence for cointegration is limited, a stable and economically meaningful long-run relationship between gold and silver emerges clearly in the post-break period. The results are shown to be robust across frequencies. |
| Keywords: | gold, silver, cointegration, fractional integration, structural breaks, PELT algorithm, Bai–Perron test, fractional cointegration, FCVAR |
| JEL: | C22 G15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12559 |
| By: | Federico Vittorio Cortesi; Giuseppe Iannone; Giulia Crippa; Tomaso Poggio; Pierfrancesco Beneventano |
| Abstract: | Neural networks applied to financial time series operate in a regime of underspecification, where model predictors achieve indistinguishable out-of-sample error. Using large-scale volatility forecasting for S$\&$P 500 stocks, we show that different model-training-pipeline pairs with identical test loss learn qualitatively different functions. Across architectures, predictive accuracy remains unchanged, yet optimizer choice reshapes non-linear response profiles and temporal dependence differently. These divergences have material consequences for decisions: volatility-ranked portfolios trace a near-vertical Sharpe-turnover frontier, with nearly $3\times$ turnover dispersion at comparable Sharpe ratios. We conclude that in underspecified settings, optimization acts as a consequential source of inductive bias, thus model evaluation should extend beyond scalar loss to encompass functional and decision-level implications. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.02620 |
| By: | Fernando Delbianco |
| Abstract: | Instrumental variable (IV) methods rely critically on the exclusion restriction, which is untestable in exactly-identified models under standard assumptions. We propose a framework combining IV analysis with the LiNGAM method to test this restriction by exploiting non-Gaussianity in the data. Under non-Gaussian structural errors, the exclusion violation parameter is point-identified without additional instruments. Five complementary tests (bootstrap percentile, asymptotic normal, permutation, likelihood ratio, and independence-based) are introduced to assess the restriction under varying data conditions. Monte Carlo simulations and an empirical application to the Card (1995) dataset demonstrate controlled Type I error rates and reasonable power against economically relevant violations. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.13505 |
| By: | Siddhartha Chib; Minchul Shin; Anna Simoni |
| Abstract: | A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this paper, using the exponentially tilted empirical likelihood framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity against an extended model that is correctly specified in either case. We provide a comprehensive study of the log-marginal exponentially tilted empirical likelihood. We demonstrate that our testing procedure is consistent from a frequentist point of view: as the sample grows, it almost surely selects the base model if and only if the regressors are exogenous, and the extended model if and only if the regressors are endogenous. The methods are illustrated with simulated data, and problems concerning the causal effect of automobile prices on automobile demand and the causal effect of potentially endogenous airplane ticket prices on passenger volume. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.07780 |
| By: | Lucas A. Souza |
| Abstract: | We introduce a performance-driven framework for constructing strictly causal forward-oriented observables in strongly non-stationary time series. The method combines a robustly normalized composite of heterogeneous indicators with a causally computed derivative component, yielding a local phase-leading effect that is amplified near regime transitions while remaining fully causal. A hysteresis-based decision functional maps the observable into discrete system states, with execution delayed by one step to preserve strict temporal ordering. Adaptation is achieved through a walk-forward scheme, in which model parameters are selected using rolling train--validation windows and subsequently applied out-of-sample. In this setting, the validation segment acts as an internal performance screen rather than as a statistical validation set, and no claims of generalization are inferred from it alone. The framework is evaluated on high-frequency financial time series as an experimentally accessible realization of a non-stationary complex system. Under a controlled zero-cost setting, the resulting dynamics exhibit a pronounced risk-reshaping effect, characterized by smoother trajectories and reduced drawdowns relative to direct exposure, and should be interpreted as an upper bound on achievable performance. These results illustrate how causal signal engineering can generate anticipatory structure in non-stationary systems without relying on non-causal information, explicit horizon labeling, or high-capacity predictive models. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.13638 |
| By: | Denis Chetverikov (University of California, Los Angeles); Jesper R.-V. S¿rensen (University of Copenhagen); Aleh Tsyvinski (Yale University) |
| Abstract: | In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case. |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2507 |
| By: | Sterzinger, Philipp; Kosmidis, Ioannis; Moustaki, Irini |
| Abstract: | Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, called Heywood cases, are characterised by non-positive variance estimates and can cause numerical instability, convergence failures, and misleading inferences. We derive sufficient conditions on the model and a penalty to the log-likelihood function that guarantee the existence of maximum penalised likelihood estimates in the interior of the parameter space, and that the corresponding estimators possess desirable asymptotic properties expected by the maximum likelihood estimator, namely consistency and asymptotic normality. Consistency and asymptotic normality follow when penalisation is soft enough, in a way that adapts to the information accumulation about the model parameters. We formally show, for the first time, that the penalties of Akaike (1987) and Hirose et al. (2011) to the log-likelihood of the normal linear factor model satisfy the conditions for existence, and, hence, deal with Heywood cases. Their vanilla versions, though, can result in questionable finite-sample properties in estimation, inference, and model selection. Our maximum softly-penalised likelihood framework ensures that the resulting estimation and inference procedures are asymptotically optimal. Through comprehensive simulation studies and real data analyses, we illustrate the desirable finite-sample properties of the maximum softly penalised likelihood estimators. |
| Keywords: | Heywood cases; infinite estimates; singular variance components |
| JEL: | C1 |
| Date: | 2026–02–18 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137057 |
| By: | Minchul Shin; Nathan Schor |
| Abstract: | We introduce ForeComp, an R package for comparing predictive accuracy using Diebold-Mariano type tests of equal predictive ability with standard and fixed smoothing inference. The package provides a common interface for loss differential based testing and includes Plot Tradeoff, a visual diagnostic for bandwidth sensitivity and the size-power tradeoff. We illustrate the toolkit with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.07458 |
| By: | Julián Andrada-Félix (Department of Quantitative Methods in Economics and Management, Universidad de Las Palmas de Gran Canaria, Spain.); Marta Gómez-Puig (Department of Economics and Riskcenter, Universitat de Barcelona, Spain.); Simón Sosvilla-Rivero (Complutense Institute for Economic Analysis, Universidad Complutense de Madrid, Spain.) |
| Abstract: | Comparing the UK’s 2022 sovereign debt crisis with earlier European examples is crucial for a holistic understanding of how such crises emerge and evolve to better comprehend the warning signs of sovereign distress and the importance of coherent and credible economic governance. Both crises were marked by sudden and severe shifts in investor confidence. The UK government’s “mini budget” announcement on September 23, 2022, sent yields on UK gilts soaring at a daily rate not seen since the 1990s. Similarly, official disclosure by Papandreou’s government regarding the actual state of Greece’s public finances on October 20, 2009, caused daily sovereign debt yields in some euro area countries to rise to levels not seen since joining the euro. The primary objective of this paper is to conduct a comparative econometric analysis of the euro area sovereign bond market, with the goal of identifying past episodes similar to the turmoil experienced in the UK government bond market during September–October 2022. This comparative perspective aims to provide valuable insights for future crisis prevention in an increasingly interconnected global financial system. Specifically, we use daily data on 10-year government bond yields from January 3, 2000, to June 30, 2023, and apply both univariate and multivariate nearest neighbours’ techniques. We also introduce a novel methodology, k-Related Simultaneous Nearest Neighbours (k-RSNN), which offers significant advantages over traditional forecasting models such as ARIMA and GARCH (it enables simultaneous analysis of multiple sovereign bond markets, effectively capturing cross-country dynamics, detecting nonlinear patterns and structural breaks, and identifying past events similar to recent crises). Our results show that financial markets initially interpreted the UK bond market disruptions between October 17 and 31, 2022, as comparable to the fiscal credibility crises faced by Spain and Italy during the European sovereign debt crisis. However, after the Bank of England’s targeted intervention, perceptions of the UK’s fiscal credibility shifted toward alignment with core euro area countries. Finally, from January 16 to June 30, 2023, we find strong parallels with the sovereign-bank risk nexus that previously affected Spain and Italy during the euro area crisis. Our findings indicate that although the origins of the crisis in the UK and the euro area are different (lack of fiscal credibility and poor communication vs. solvency concerns, weak banking systems, and limitations of incomplete economic unions), examining them together offers valuable lessons: policymakers should better recognise early warning signs of sovereign distress and reinforce the importance of coherent and credible economic governance. |
| Keywords: | Financial crisis; Bond markets; Fiscal credibility; United Kingdom; Analogies; Nearest Neighbours’ Analysis. JEL classification: C22; G14; H30; H61. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202525 |