nep-ets New Economics Papers
on Econometric Time Series
Issue of 2026–10–26
twelve papers chosen by
Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico
  1. This paper studies high-dimensional integrated curve time series with common stochastic trends. A dual functional factor model structure is adopted with a high-dimensional factor model for the observed curve time series and a low-dimensional factor model for the latent curves with common trends. A functional PCA technique is applied to estimate the common stochastic trends and functional factor loadings. Under some regularity conditions we derive the mean square convergence and limit distribution theory for the developed estimates, allowing the dimension and sample size to jointly diverge to infinity. We propose an easy-toimplement criterion to consistently select the number of common stochastic trends and further discuss model estimation when the nonstationary factors are cointegrated. Extensive Monte-Carlo simulations and two empirical applications to large-scale temperature curves in Australia and log-price curves of S&P 500 stocks are conducted, showing finite-sample performance and providing practical implementations of the new methodology. By Degui Li; Yu-Ning Li; Peter C.B. Phillips
  2. SHADE: Shrinkage via Halted Alpha-stable Denoising for Heavy-Tailed Probabilistic Time Series Forecasting By Jiahao Weng; Jiyu Liu
  3. Online Conditional Vine Copulas: Forecasting Electricity Demand By Christian Jobelius Schulz; Christoph Hanck; Simon Hirsch; Florian Ziel
  4. Almost Uniform Bias Correction for Persistent Panel Local Projections By Geert Dhaene; Arturas Juodis
  5. Financial Conditions and U.S. Macro Tail Risk By Larissa Nawo
  6. Cointegrated Regressors: Where Folded Concave Penalties Go Beyond LASSO By Hongqi Chen; Chengwang Liao; Ziwei Mei; Zhentao Shi
  7. Deep Learning Models in Central Bank Forecasting By Paulo Júlio
  8. Conventional Inference under Nonstationary Heavy Tailed Fractional Processes & Arrays By Ioannis Kasparis
  9. Persistence is not Independence: A structural Credibility Protocol for Distribution based Recovery in VARLiNGAM By Tu Anh Doan Nguyen; Alessandro Bucciol
  10. HP vs. Hamilton: which is better to estimate trends? A comparison based on Euro Area GDP By Silva Lopes, Artur
  11. Forecasting U.S. State Real GDP Growth with Panel Machine Learning and Mixed-Frequency Data By Oguzhan Cepni; Rangan Gupta
  12. Topological Compression of Prediction Market Belief States for Event Forecasting By Aadit Jerfy
  1. By: Degui Li (University of Macau); Yu-Ning Li (University of York); Peter C.B. Phillips (Yale University, University of Auckland)
    Keywords: Common trends, Curve time series, Factor models, Functional PCA, High dimensionality
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:boa:wpaper:202649
  2. By: Jiahao Weng; Jiyu Liu
    Abstract: Time-series observations are particularly susceptible to noise, and many real-world series have low signal-to-noise ratios (SNRs) together with heavy-tailed and time-varying disturbances. When Gaussian diffusion models are used for forecasting, reversing all the way to the zero-noise endpoint may yield an overly narrow and overconfident predictive distribution. We propose SHADE, an alpha-stable L\'evy diffusion forecaster that reads out at a nonzero depth so that the residual stable kernel acts as distribution-level shrinkage. By decomposing halting risk into smoothing bias and estimation error, we develop a scale-calibrated halt for estimating the optimal shrinkage $\tau^*$ and a context-amortized halt that requires no explicit noise labels. Gains concentrate in the extreme heavy-tailed regime $\alpha\leq1.3$: Across synthetic noise settings, median CRPS improves by 21.7\%, while the NLL falls significantly; Cryptocurrency and foreign-exchange data provide further evidence of improved tail calibration in real-data implement. On near-light-tailed or high-SNR data, the model reverts to Gaussian diffusion or full denoising. SHADE maintains overall forecast error comparable to strong baselines while providing more honest tail uncertainty for heavy-tailed series.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.26358
  3. By: Christian Jobelius Schulz; Christoph Hanck; Simon Hirsch; Florian Ziel
    Abstract: We propose an online multivariate distributional forecasting model in which we condition the parameters of a vine copula on covariates. An online algorithm updates the parameters sequentially using only the most recent observations. Existing approaches require full batch re-estimation using all available data at each update step, which makes them computationally slow in large-scale forecasting applications and less suitable for settings in which dependence structures change over time. Simulation results show that the online procedure achieves forecasting performance comparable to that of the batch estimator, while reducing computation time substantially. We illustrate the method in a forecasting application to electricity net-demand across five regions of Great Britain and show that (i) the conditional vine copula model outperforms unconditional Gaussian and vine copula benchmarks and (ii) that online estimation improves forecast accuracy when batch re-estimation is not available. The algorithm is implemented in the Python package ondil.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.23119
  4. By: Geert Dhaene; Arturas Juodis
    Abstract: Panel local projections are widely used to estimate impulse responses in policy analysis. This paper examines the properties of the fixed-effect estimator and various bias-correction methods in panel local projections, focusing on settings with autoregressive control variables. We analyze the bias and variance of these estimators while allowing for persistence in both the target and control variables. Our theoretical findings indicate that the half-panel jackknife has desirable bias-reducing properties beyond the stationary setup. For empirical researchers, we recommend using bias-correction-adjusted standard errors to account for the sampling uncertainty associated with the half-panel jackknife estimator, regardless of the time-series dimension of the data.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.26427
  5. By: Larissa Nawo (Associated student HEC Montréal)
    Abstract: How do financial conditions shape U.S. macroeconomic tail risk, and what is their incremental forecasting value beyond standard macroeconomic predictors? Using quarterly U.S. data from 1971Q1 to 2026Q1, this paper compares macro-only and macro-financial Bayesian VARs. First-lag financial-conditions coefficients are negative for GDP growth and positive for unemployment, with 95% posterior credible intervals excluding zero; second-lag coefficients have opposite signs. Adding financial conditions to the Gaussian VAR raises predicted probabilities of negative GDP growth by 1.5 and 2.5 percentage points at four- and eight-quarter horizons and lowers GDP tail quantiles. However, out-of-sample tests do not establish a general forecast-accuracy improvement. Student-t forecast innovations, simulated with the same Gaussian posterior parameter draws, produce heavier predictive tails. Their positive GDP log-score differences reverse when 2020Q1–2021Q4 forecast targets are excluded, while CRPS favors Gaussian innovations in several comparisons. Financial conditions change measured tail risk without consistently improving forecast accuracy.
    Keywords: Financial conditions; macroeconomic tail risk; recession risk; macroeconomic vulnerability
    Date: 2026–10–07
    URL: https://d.repec.org/n?u=RePEc:ris:crcrmw:023696
  6. By: Hongqi Chen; Chengwang Liao; Ziwei Mei; Zhentao Shi
    Abstract: LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions with many regressors, the convex penalty of LASSO overly suppresses the coefficients of cointegrated regressors and therefore fails to deliver consistent parameter estimation. We employ folded concave penalties (FCP), including the well-known SCAD and MCP, to address this issue. Using FCP, we establish two asymptotic results: (i) a local oracle property for the penalized regression, and (ii) a global oracle property for regressions with a penalty-level constraint. In particular, the latter guarantees a global solution that consistently estimates the true coefficients and enjoys oracle prediction performance. Simulation studies and an application to predicting the euro area unemployment rate show that FCP improves variable selection and out-of-sample prediction relative to LASSO.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.14617
  7. By: Paulo Júlio
    Abstract: This paper assesses whether pretrained time-series foundation models and locally trained deep neural networks can make a useful contribution to central bank forecasting in a small, data-constrained economy. We compare TimesFM, Chronos-2, Moirai 2.0, and several locally estimated architectures with conventional statistical and econometric benchmarks and Banco de Portugal official projections. The applications cover monthly inflation, direct quarterly macroeconomic forecasting, and mixed-frequency nowcasting. All headline exercises use historical-vintage information and final data as evaluation truth. Foundation models are generally more competitive than locally trained neural networks and, for selected targets and horizons, match or outperform simple benchmarks and official projections. These gains are not uniform. Official projections remain particularly strong when judgment, granular or advance information, and exceptional economic events matter, while persistent or irregular series can leave little scope for deep-learning improvements. Locally trained and native mixed-frequency models nevertheless add useful signals for selected real and labor-market aggregates. The results therefore favor using deep-learning forecasts as systematic, reproducible complements to official projections rather than as universal replacements.
    JEL: C45 C53 E27 E31 E37
    Date: 2026
    URL: https://d.repec.org/n?u=RePEc:ptu:wpaper:w202612
  8. By: Ioannis Kasparis (University of Cyprus)
    Abstract: Existing regression inference methods for nonstationary heavy tailed data are largely limited to unit roots tests. Functional central limit theorems (FCLTs), which are commonly used for the asymptotic analysis of nonstationary models, are inapplicable to nonstationary data characterized by a memory parameter $d
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.25237
  9. By: Tu Anh Doan Nguyen; Alessandro Bucciol
    Abstract: VAR-LiNGAM recovers a contemporaneous structure from reduced-form innovations under mutual independence of the underlying structural shocks. Recovery itself and bootstrap stability of the recovered edges are natural post-estimation outputs, but neither directly assesses the identifying independence assumption. We organize post-recovery validation as an ordered, non-compensatory protocol with four layers (reduced-form admissibility, distributional informativeness, independence support and edge persistence) and establish, by a constructive population result, a Monte Carlo experiment under known ground truth and a firm-level Compustat application, that the last two layers are non-nested: an edge can persist under resampling while the recovered shocks remain dependent, and independence can be supported while the leading recovered edge fails to persist. Independence support is classified operationally by a block-permutation dHSIC diagnostic on the recovered shocks, with mutual information as a continuous measure of residual dependence and tail co-exceedance as a complementary extreme-dependence diagnostic. Empirically, the conjunction of no detected independence violation and a reportable persistent leading edge occurs only in a small subset.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.25238
  10. By: Silva Lopes, Artur
    Abstract: This paper reconsiders the case of the rivalry between the HP and the Hamilton detrending methods, assessing it from a new angle, that of estimation of the trend itself. An extensive Monte Carlo study based on Euro area GDP is performed which, besides confirming and even reinforcing the superiority of the HP filter in terms of goodness of fit, demonstrates that, when the assessment criteria are designed to measure the success in terms of replicating long-run properties, the positions are switched, Hamilton's method dominating the HP filter. Since, however, both methods exhibit implausible features, a combination of them is employed to derive some empirical evidence concerning the Euro Area economy.
    Keywords: trends, trend-cycle decomposition, detrending, Euro Area
    JEL: C18 C22 E01 O47
    Date: 2026
    URL: https://d.repec.org/n?u=RePEc:zbw:esprep:344078
  11. By: Oguzhan Cepni (Center for Economics and Policy Studies, Istinye University, Istanbul, Turkiye; Ostim Technical University, Ankara, Turkiye; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: We examine whether panel machine learning and mixed-frequency indicators improve fore- casts of quarterly real GDP growth across the 50 U.S. states. Using weekly, monthly and quarterly predictors, we compare six models under state-specific, global and grouped estimation at horizons of one to four quarters. The evaluation covers 2015--2026 and uses final- vintage data aligned with an assumed publication calendar. Pooled forecasts have roughly half the mean squared forecast error of a random walk benchmark over the full sample. Outside the pandemic window, the globally pooled forecast combination reduces MSFE by approximately 45 percent--56 percent relative to the random walk, with statistically significant improvements at each forecast horizon. Pooled implementations generally outperform separate state estimation at longer horizons, while differences among pooled structures are small. Model rankings vary across horizons and evaluation samples, and forecast accuracy differs across states. Extreme pandemic observations affect subsequent forecasts as they move through the mixed-frequency lag vector; excluding the collapse and rebound targets from estimation and limiting extreme predictors reduces this deterioration. The findings support combining timely regional indicators within pooled forecasting systems and evaluating their performance across horizons, states and economic conditions.
    Keywords: regional forecasting, panel data, mixed-frequency data, machine learning, forecast evaluation, GDP growth
    JEL: C53 C55 E37 R11
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:pre:wpaper:202632
  12. By: Aadit Jerfy
    Abstract: Topological Data Analysis (TDA) and Persistent Homology (PH) have been utilized in numerous fields$-$from protein modeling to financial analysis$-$to understand the underlying structure of data. In this paper, we incorporate TDA and PH into forecasting models over a prediction market universe to determine whether the belief state of such markets forms an underlying structure whose shape can provide predictive insight. We conclude that single-parameter PH is able to improve predictive ability and calibrated accuracy of linear forecasting models, while two-parameter multipersistence produces similar benefits for nonlinear models.
    Date: 2026–10
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2610.25756
This nep-ets issue is ©2026 by Simon Sosvilla-Rivero. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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This page is being maintained by Thomas Krichel. It was last updated on 2026‒10‒26 at 5:56.