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on Econometric Time Series |
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Issue of 2025–12–22
eleven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Amaze Lusompa |
| Abstract: | It is well known that model selection via cross validation can be biased for time series models. However, many researchers have argued that this bias does not apply when using cross-validation with vector autoregressions (VAR) or with time series models whose errors follow a martingale-like structure. I show that even under these circumstances, performing cross-validation on time series data will still generate bias in general. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.05900 |
| By: | Tetsuya Takaishi |
| Abstract: | We employ single-qubit quantum circuit learning (QCL) to model the dynamics of volatility time series. To assess its effectiveness, we generate synthetic data using the Rational GARCH model, which is specifically designed to capture volatility asymmetry. Our results show that QCL-based volatility predictions preserve the negative return-volatility correlation, a hallmark of asymmetric volatility dynamics. Moreover, analysis of the Hurst exponent and multifractal characteristics indicates that the predicted series, like the original synthetic data, exhibits anti-persistent behavior and retains its multifractal structure. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10584 |
| By: | Nicolas Hardy; Dimitris Korobilis |
| Abstract: | We revisit macroeconomic time-varying parameter vector autoregressions (TVP-VARs), whose persistent coefficients may adapt too slowly to large, abrupt shifts such as those during major crises. We explore the performance of an adaptively-varying parameter (AVP) VAR that incorporates deterministic adjustments driven by observable exogenous variables, replacing latent state innovations with linear combinations of macroeconomic and financial indicators. This reformulation collapses the state equation into the measurement equation, enabling simple linear estimation of the model. Simulations show that adaptive parameters are substantially more parsimonious than conventional TVPs, effectively disciplining parameter dynamics without sacrificing flexibility. Using macroeconomic datasets for both the U.S. and the euro area, we demonstrate that AVP-VAR consistently improves out-of-sample forecasts, especially during periods of heightened volatility. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.03763 |
| By: | Yingyao Hu |
| Abstract: | This paper develops new identification results for multidimensional continuous measurement-error models where all observed measurements are contaminated by potentially correlated errors and none provides an injective mapping of the latent distribution. Using third-order cross-moments, the paper constructs a three-way tensor whose unique decomposition, guaranteed by Kruskal’s theorem, identifies the factor loading matrices. Starting with a linear structure, the paper recovers the full distribution of latent factors by constructing suitable measurements and applying scalar or multivariate versions of Kotlarski’s identity. As a result, the joint distribution of the latent vector and measurement errors is fully identified without requiring injective measurements, showing that multivariate latent structure can be recovered in broader settings than previously believed. Under injectivity, the paper also provides user-friendly testable conditions for identification. Finally, this paper provides general identification results for nonlinear models using a newly-defined generalized Kruskal rank - signal rank - of intergral operators. These results have wide applicability in empirical work involving noisy or indirect measurements, including factor models, survey data with reporting errors, mismeasured regressors in econometrics, and multidimensional latent-trait models in psychology and marketing, potentially enabling more robust estimation and interpretation when clean measurements are unavailable. |
| Date: | 2025–12–10 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:19/25 |
| By: | Pablo Guerron-Quintana (Boston College; Boston College); Amazon Web Services |
| Abstract: | Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2’s universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used “as is” in real-world forecasting pipelines. |
| Keywords: | forecasting, time series |
| Date: | 2025–12–10 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1105 |
| By: | Helmut Lütkepohl; Till Strohsal |
| Abstract: | We replicate a study by Känzig (American Economic Review, 111 (2021), 1092-1125), who employs structural vector autoregressive techniques to examine the impact of changes in oil supply expectations on the price of oil and other macroeconomic aggregates. Känzig identifies an oil supply news shock by constructing a proxy from OPEC announcements about their production plans. As this proxy is a controversial instrument for oil supply news, we use the non-Gaussianity of the data to identify independent structural shocks and find that one of them corresponds closely to Känzig’s oil supply news shock, implying that the proxy is not necessarily needed to obtain a shock with the same characteristics. |
| Keywords: | Structural vector autoregression, non-Gaussian shocks, proxy SVARs, instruments, shock labeling |
| JEL: | C32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2146 |
| By: | Jan Ditzen (Free University of Bozen-Bolzano) |
| Abstract: | Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007–2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks and dating them is therefore necessary not only for estimation purposes but also for understanding drivers of change and their effect on relationships. This talk introduces a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing multiple structural breaks in time-series and panel data. xtbreak can detect the existence of breaks, determine their number and location, and provide break date confidence intervals. A special emphasis of the talk will be put on Python integration to gain speed advantages. |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:boc:isug25:03 |
| By: | Andrzej Tokajuk; Jaros{\l}aw A. Chudziak |
| Abstract: | Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply the Partial-Multivariate Transformer (PMformer) to forecast daily returns for BTCUSDT and ETHUSDT, benchmarking it against eleven classical and deep learning models. Our empirical results yield two primary contributions. First, we demonstrate that the partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise. Second, we experiment and discuss an observable disconnect between this statistical performance and practical trading utility; lower prediction error did not consistently translate to higher financial returns in simulations. This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.04099 |
| By: | Bufan Li; Lujia Bai; Weichi Wu |
| Abstract: | This paper presents a systematic framework for controlling false discovery rate in learning time-varying correlation networks from high-dimensional, non-linear, non-Gaussian and non-stationary time series with an increasing number of potential abrupt change points in means. We propose a bootstrap-assisted approach to derive dependent and time-varying P-values from a robust estimate of time-varying correlation functions, which are not sensitive to change points. Our procedure is based on a new high-dimensional Gaussian approximation result for the uniform approximation of P-values across time and different coordinates. Moreover, we establish theoretically guaranteed Benjamini--Hochberg and Benjamini--Yekutieli procedures for the dependent and time-varying P-values, which can achieve uniform false discovery rate control. The proposed methods are supported by rigorous mathematical proofs and simulation studies. We also illustrate the real-world application of our framework using both brain electroencephalogram and financial time series data. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10467 |
| By: | Qihui Chen; Zheng Fang; Ruixuan Liu |
| Abstract: | There has been significant progress in Bayesian inference based on sparsity-inducing (e.g., spike-and-slab and horseshoe-type) priors for high-dimensional regression models. The resulting posteriors, however, in general do not possess desirable frequentist properties, and the credible sets thus cannot serve as valid confidence sets even asymptotically. We introduce a novel debiasing approach that corrects the bias for the entire Bayesian posterior distribution. We establish a new Bernstein-von Mises theorem that guarantees the frequentist validity of the debiased posterior. We demonstrate the practical performance of our proposal through Monte Carlo simulations and two empirical applications in economics. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.09257 |
| By: | Bullock, David W.; Okoto, Edna M. |
| Abstract: | The predictive ability of two alternative forward price distribution forecasting methods based upon the full range of option premiums was developed and tested using 10 years of price and premium history for five traded commodities. The two models were a best-fit parametric distribution and a non-parametric linear interpolation fit. These were compared to two traditional approaches: historical time series and Black-76 option implied volatility. The forecast horizons ranged from 6 months to 1 week in duration. A modification of the theoretical results of King and Fackler (1985) nonparametric option pricing model was presented to justify the fitting of a price probability density function to the option premiums with the intrinsic value removed. Time series fits to the historical futures price indicted that the integrated ARCH (1) and GARCH (1, 1) models were the most prevalent best fit to the data. For parametric fits to the option premiums, the Burr Type XII and Dagum distributions were the most prevalent best fits. Predictive ability was measured using 10-percent value-at-risk portfolio models for simple short and long futures positions where the number of actual exceptions was compared to the theoretical values. The predictive results indicated that the parametric and non-parametric distribution fits performed best on the short futures portfolios over the longer-term forecast horizons (6- and 3-months) while the Black-76 performed best over the same time horizon. For the shorter time horizons (1-month or less), the Black-76 and time series methods performed best. These results point to the possibility that a hybrid Black-76 and premium distribution fit approach (via a splice) might perform best for longer-term projections. |
| Keywords: | Agricultural and Food Policy, Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:nccc24:379004 |