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on Econometric Time Series |
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Issue of 2026–03–02
eleven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Cen, Zetai; Lam, Clifford |
| Abstract: | We propose a test for the Kronecker product structure of a factor loading matrix implied by a tensor factor model with Tucker decomposition in the common component. By defining a Kronecker product structure set, we determine whether a tensor time series has a Kronecker product structure, equivalent to its ability to decompose the series according to a tensor factor model. Our test is built on analysing and comparing the residuals from fitting a full tensor factor model, and the residuals from fitting a factor model on a reshaped version of the data. In the most extreme case, the reshaping is the vectorization of the tensor data, and the factor loading matrix in such a case can be general if there is no Kronecker product structure present. Our test is also generalized to the Khatri–Rao product structure in a tensor factor model with canonical polyadic decomposition. Theoretical results are developed through asymptotic normality results on estimated residuals. Numerical experiments suggest that the size of the tests approaches the pre-set nominal value as the sample size or the order of the tensor increases, while the power increases with mode dimensions and the number of combined modes. We demonstrate our tests through extensive real data examples. |
| Keywords: | factor-structured idiosyncratic error; tensor refold; tensor reshape; weak factor |
| JEL: | C1 |
| Date: | 2025–12–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129613 |
| By: | Harrison Katz |
| Abstract: | Understanding how the composition of guest origin markets evolves over time is critical for destination marketing organizations, hospitality businesses, and tourism planners. We develop and apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to forecast the compositional dynamics of guest origin market shares using proprietary Airbnb booking data spanning 2017--2024 across four major destination regions. Our analysis reveals substantial pandemic-induced structural breaks in origin composition, with heterogeneous recovery patterns across markets. The BDARMA framework achieves the lowest average forecast error across all destination regions, outperforming standard benchmarks including na\"ive forecasts, exponential smoothing, and SARIMA on log-ratio transformed data. For EMEA destinations, BDARMA achieves 23% lower forecast error than naive methods, with statistically significant improvements. By modeling compositions directly on the simplex with a Dirichlet likelihood and incorporating seasonal variation in both mean and precision parameters, our approach produces coherent forecasts that respect the unit-sum constraint while capturing complex temporal dependencies. The methodology provides destination stakeholders with probabilistic forecasts of source market shares, enabling more informed strategic planning for marketing resource allocation, infrastructure investment, and crisis response. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18358 |
| By: | Pieter Nel (Department of Economics, University of Pretoria); Renee van Eyden (Department of Economics, University of Pretoria) |
| Abstract: | Does media sentiment create artificial volatility, or do stock markets efficiently filter media sentiment as noise? This study tests these hypotheses using daily data (1994-2024) across the S&P 500, Dow Jones, and NASDAQ. Principal Component Analysis decomposes four uncertainty measures into fundamental uncertainty (PC1) and media-amplified supply sentiment (PC2). EGARCH modeling reveals that media sentiment mutes rather than amplifies volatility contradicting behavioral finance predictions. Time Varying Granger causality tests suggests no causality from uncertainty variables to volatility, but volatility has a causal relationship with fundamental uncertainty. The asymmetric relationship demonstrates that information flows from stock markets to uncertainty sentiment, not uncertainty sentiment to stock markets. These findings support rational updating hypothesis where investors observe volatility and correctly infer elevated uncertainty, rather than being misled by media sentiment. |
| Keywords: | Media sentiment, EGARCH modeling, Principal component analysis, Time-varying causality |
| JEL: | G41 C58 E44 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202605 |
| By: | Piersilvio De Bortoli; Davide Ferrari; Francesco Ravazzolo; Luca Rossini |
| Abstract: | This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to study model selection uncertainty in the Italian electricity load data. Rather than relying on a single model selected by an arbitrary criterion, the MSCS identifies a set of models that are statistically indistinguishable from the true data-generating process at a given confidence level. The size and composition of this set reveal crucial information about model selection uncertainty: noisy data scenarios produce larger sets with many candidate models, while more informative cases narrow the set considerably. To study the importance of each model term, we consider numerical statistics measuring the frequency with which each term is included in both the entire MSCS and in Lower Boundary Models (LBM), its most parsimonious specifications. Applied to Italian hourly electricity load data, the MSCS methodology reveals marked intraday variation in model selection uncertainty and isolates a collection of model specifications that deliver competitive short-term forecasts while highlighting key drivers of electricity load like intraday hourly lags, temperature, calendar effects and solar energy generation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.16527 |
| By: | boughabi, houssam |
| Abstract: | This paper analyzes the dynamic interactions between income growth, inflation, and unemployment in Morocco over the period 1990–2025. Income growth is modeled as a stochastic process exhibiting both short-term persistence and potential long-memory effects, captured via an ARFIMA(p, d, q) specification. The estimated income growth series is then used to investigate its influence on inflation and unemployment, linking income shocks to macro-labor outcomes within a Phillips–Okun framework. By com- bining long-memory income dynamics with empirical macro-labor modeling, the study provides new insights into how persistent fluctuations in income growth shape price adjustments and labor market responses. These findings offer both theoretical and policy-relevant implications for emerging economies experiencing cyclical or structural growth variations. |
| Keywords: | Inflation; Unemployment; Income dynamics; Volatility; ARFIMA; GARCH |
| JEL: | C22 E24 E31 E32 |
| Date: | 2026–02–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128041 |
| By: | Aur\'elien Alfonsi; Ahmed Kebaier |
| Abstract: | For a class of stochastic models with Gaussian and rough mean-reverting volatility that embeds the genuine rough Stein-Stein model, we study the weak approximation rate when using a Euler type scheme with integrated kernels. Our first result is a weak convergence rate for the discretised rough Ornstein-Uhlenbeck process, that is essentially in $\min(3\alpha-1, 1)$, where $\frac{t^{\alpha-1}}{\Gamma(\alpha)} $ is the fractional convolution kernel with $\alpha \in (1/2, 1)$. Then, our main result is to obtain the same convergence rate for the corresponding stochastic rough volatility model with polynomial test functions. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18234 |
| By: | Robben, Jens (University of Amsterdam); Barigou, Karim (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | Accurate forecasts of weekly mortality are essential for public health and the insurance industry. We develop a forecasting framework that extends the Lee–Carter model with age- and region-specific seasonal effects and penalized distributed lag non-linear components that capture the delayed and non-linear effects of heat, cold, and influenza on mortality. The model accommodates overdispersed mortality rates via a negative binomial distribution. We model the temporal dynamics of the latent factors in the model using SARIMAX processes and capture cross-regional dependencies through a copula-based approach. Using regional French mortality data (1990–2019), we demonstrate that the proposed framework yields well-calibrated forecast distributions and improves predictive accuracy relative to benchmark models. The results further show substantial heterogeneity in temperature- and influenza-related relative risks between ages and regions. These findings underscore the importance of incorporating exogenous drivers and dependence structures into a weekly mortality forecasting framework. |
| Keywords: | Stochastic mortality modeling ; seasonal mortality ; distributed lag non-linear models ; excess mortality |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025016 |
| By: | Marc Wildi |
| Abstract: | We re-examine the traditional Mean-Squared Error (MSE) forecasting paradigm by formally integrating an accuracy-timeliness trade-off: accuracy is defined by MSE (or target correlation) and timeliness by advancement (or phase excess). While MSE-optimized predictors are accurate in tracking levels, they sacrifice dynamic lead, causing them to lag behind changing targets. To address this, we introduce two `look-ahead' frameworks--Decoupling-from-Present (DFP) and Peak-Correlation-Shifting (PCS)--and provide closed-form solutions for their optimization. Notably, the classical MSE predictor is shown to be a special case within these frameworks. Dually, our methods achieve maximum advancement for any given accuracy level, so our approach reveals the complete efficient frontier of the accuracy-timeliness trade-off, whereas MSE represents only a single point. We also derive a universal upper bound on lead over MSE for any linear predictor under a consistency constraint and prove that our methods hit this ceiling. We validate this approach through applications in forecasting and real-time signal extraction, introducing a leading-indicator criterion and tailored linear benchmarks. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.23087 |
| By: | Léonard, Lise (Université catholique de Louvain, LIDAM/ISBA, Belgium); Pircalabelu, Eugen (Université catholique de Louvain, LIDAM/ISBA, Belgium); von Sachs, Rainer (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | Selection methods for high-dimensional models are well developed, but they do not take into account the choice of the model, which leads to an underestimated variance. We propose a procedure for high-dimensional model averaging that allows inference even when the number of predictors is greater than the sample size. The proposed estimator is constructed from the debiased Lasso and the weights are chosen to reduce the prediction risk associated with them. We derive the asymptotic distribution of the estimator within a high-dimensional framework and offer guarantees for the minimal loss prediction obtained from the weights. With this, in contrast to existing approaches, our proposed method combines the advantages of model averaging with the possibility of inference based on asymptotic normality. In a simulation study and on a real, high-dimensional dataset, the estimator shows a smaller prediction risk than its competitors. |
| Keywords: | Debiased Lasso ; High-Dimensional Inference ; Model Averaging ; Prediction Risk |
| Date: | 2025–06–11 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025014 |
| By: | Sumin Kim; Minjae Kim; Jihoon Kwon; Yoon Kim; Nicole Kagan; Joo Won Lee; Oscar Levy; Alejandro Lopez-Lira; Yongjae Lee; Chanyeol Choi |
| Abstract: | Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07048 |
| By: | Mariano Kulish (Univeristy of Sydney); Inna Tsener (Universitat de les Illes Balears) |
| Abstract: | We assess the accuracy and efficiency of time-varying linear solution methods for non-stationary rational expectations models. These methods construct a sequence of local linear approximations, each with coefficients that vary over time, based on a set of expansion points. Benchmarking against globally accurate non-linear solutions, we show, both theoretically and numerically, that their accuracy depends critically on the choice of expansion points and on agents’ expectations about the future. Our results contribute to the literature on solving non-stationary stochastic models with rational expectations, spanning a wide range of sources of non-stationarity, including evolving structural parameters, changing policy regimes, and cases without a balanced growth path. |
| Keywords: | piecewise linear solutions, approximation points, time-inhomogeneous models, non-stationary models, semi-Markov models, unbalanced growth, time-varying parameters, extended function path |
| JEL: | C61 C63 C68 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:aoz:wpaper:387 |