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on Econometric Time Series |
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Issue of 2026–04–06
thirteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Minkey Chang; Jae-Young Kim |
| Abstract: | We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors are identifiable up to permutation and component-wise affine (or monotone invertible) transformations. Linear diagonal dynamics preserve this identifiability and admit scalable computation via companion-matrix and Krylov methods. We demonstrate improved factor recovery on synthetic data, stable intervention accuracy on synthetic SCMs, and competitive probabilistic forecasting on real-world benchmarks. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.22886 |
| By: | Jacob Carlson; Neil Shephard |
| Abstract: | The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions and SVARs. It closes a gap between time series causality and nonparametric cross-sectional causal methods, and provides a foundation for many new methods which have causal content. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20394 |
| By: | Dezhbakhsh, Hashem; Levy, Daniel |
| Abstract: | It is well established that the U.S. prewar output was more volatile and less shock persistent than the postwar output. This is often attributed to the data interpolation employed to construct the prewar series. Our analytical results, however, indicate that commonly used linear interpolation has the opposite effect on shock persistence and volatility of a series—it increases shock persistence and reduces volatility. The surprising implication of this finding is that the actual differences between the volatility and shock persistence of the prewar and postwar output series are likely greater than the existing literature recognizes, and interpolation has dampened rather than magnified this difference. Consequently, the view that postwar output was more stable than prewar output because of the effectiveness of the postwar stabilization policies and institutional changes has considerable merit. Our results hold for parsimonious stationary and nonstationary time series commonly used to model macroeconomic time series. |
| Keywords: | Business Cycles; Output Volatility; Shock Persistence; Prewar and Postwar US Time Series; Linear Interpolation; Variance Ratio; Stationary Series; Nonstationary Series; Periodic Non-stationarity; Missing Observations; Macroeconomic Stabilization; Economic Policy |
| JEL: | C02 C18 C22 C82 E01 E32 N10 |
| Date: | 2026–02–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128031 |
| By: | Matthew Read; Dan Zhu |
| Abstract: | We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially speeds up sampling when identification is tight. It also facilitates implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in an oil-market model identified using a rich set of sign, elasticity and narrative restrictions. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.27088 |
| By: | Stéphane Goutte (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement); Konstantinos N. Konstantakis (University of Piraeus); Dimitris Konstantios (ALBA Graduate Business School [Athens, Greece]); Panayotis G. Michaelides (NTUA - National Technical University of Athens); Arsenios‐georgios N. Prelorentzos |
| Abstract: | This paper surveys quantile modelling from its theoretical origins to current advances. We organize the literature and present core econometric formulations and estimation methods for: (i) cross‐sectional quantile regression; (ii) quantile time series models and their time series properties; (iii) quantile vector autoregressions for multivariate data; (iv) quantile panel models for longitudinal data; and (v) quantile factor‐augmented models for information compression in data‐rich environments. Each section outlines theoretical foundations and developments, followed by representative empirical applications. Finally, the survey highlights open gaps in quantile modelling. By studying distributional dynamics beyond averages, quantile methods provide policymakers and regulators with tools to design interventions that are robust to risks and effective across the entire spectrum of possible outcomes. |
| Keywords: | Quantile, Quantile regression, Estimation, Econometric model, Multivariate statistics, Series (stratigraphy) |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05503058 |
| By: | Matthew Read (Reserve Bank of Australia) |
| Abstract: | I propose identifying structural vector autoregressions using 'shock-percentile' restrictions. These restrictions require the realisation of a structural shock in a selected episode to lie in the tail of the shock's historical distribution, representing the belief that a relatively large shock has occurred. I argue that shock-percentile restrictions are an attractive alternative to imposing numeric bounds on shock magnitudes, which are difficult to credibly elicit. Simulations demonstrate the potential for shock-percentile restrictions to provide identifying information. In two empirical applications, I exploit shock-percentile restrictions to disentangle the relationship between uncertainty and real activity, and to sharpen identification of the macroeconomic effects of US monetary policy. |
| Keywords: | monetary policy; narrative restrictions; set identification; structural vector autoregression; uncertainty |
| JEL: | C32 D80 E32 E44 E52 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:rba:rbardp:rdp2026-01 |
| By: | Pierre Andreoletti (IDP) |
| Abstract: | We study trajectory forecasting under squared loss for time series with weak conditional structure, using highly expressive prediction models. Building on the classical characterization of squared-loss risk minimization, we emphasize regimes in which the conditional expectation of future trajectories is effectively degenerate, leading to trivial Bayes-optimal predictors (flat for prices and zero for returns in standard financial settings). In this regime, increased model expressivity does not improve predictive accuracy but instead introduces spurious trajectory fluctuations around the optimal predictor. These fluctuations arise from the reuse of noise and result in increased prediction variance without any reduction in bias. This provides a process-level explanation for the degradation of Transformerbased forecasts on financial time series. We complement these theoretical results with numerical experiments on high-frequency EUR/USD exchange rate data, analyzing the distribution of trajectory-level forecasting errors. The results show that Transformer-based models yield larger errors than a simple linear benchmark on a large majority of forecasting windows, consistent with the variance-driven mechanism identified by the theory. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00064 |
| By: | Roberto Fuentes-Mart\'inez; Irene Crimaldi |
| Abstract: | A model-free measure of Granger causality in expectiles is proposed, generalizing the traditional mean-based measure to arbitrary positions of the conditional distribution. Expectiles are the only law-invariant risk measures that are both coherent and elicitable, making them particularly well-suited for studying distributional Granger causality where risk quantification and forecast evaluation are both relevant. Based on this measure, a test is developed using M-vine copula models that accounts for multivariate Granger causality with $d+1$ series under non-linear and non-Gaussian dependence, without imposing parametric assumptions on the joint distribution. Strong consistency of the test statistic is established under some regularity conditions. In finite samples, simulations show accurate size control and power increasing with sample size. A key advantage is the joint testing capability: causal relationships invisible to pairwise tests can be detected, as demonstrated both theoretically and empirically. Two applications to international stock market indices at the global and Asian regional level illustrate the practical relevance of the proposed framework. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.23294 |
| By: | Ms. Sunwoo Lee |
| Abstract: | In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to high-frequency growth data. This paper examines the application of the Bayesian Structural Time Series (BSTS) model to the case of nowcasting quarterly economic growth in Tanzania, leveraging a range of high-frequency economic indicators. The BSTS model provides a flexible framework that incorporates trends, seasonal variations, and regression effects, while its spike-and-slab variable selection helps identify relevant indicators. This paper outlines a framework for model selection and evaluation, including robustness checks and sensitivity analysis, and demonstrate the model’s relative performance. Additionally, the model’s capacity to adapt to longer forecast horizons and dynamic regressors enhances its utility for understanding growth trends in changing economic environments. |
| Keywords: | Nowcasting; Bayesian models; economic activity; GDP; low-income countries |
| Date: | 2026–03–20 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/049 |
| By: | Denis Chetverikov; Jesper R. -V. S{\o}rensen; Aleh Tsyvinski |
| 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 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20134 |
| By: | Emanuele Lopetuso; Massimiliano Caporin |
| Abstract: | Traditional econometric analyzes represent observations as vectors despite the inherent complexity of empirical data structures. When data are organized along dual classification dimensions, a matrix representation provides a more natural and interpretable framework. Building on recent advances in matrix autoregressive (MAR) modeling, this study introduces a novel error correction representation tailored for matrix-structured data. Through comparative analysis with existing methodologies, we demonstrate two critical advancements. First, the proposed model preserves the interpretative foundations of conventional cointegration analysis, with coefficients that explicitly capture dynamics rooted in adjustment toward steady-state positions. Second, in contrast to previous formulations, our error correction framework allows for an equivalent matrix autoregressive representation, preserving the fundamental structure of the data in both specifications. This ensures that the matrix representation reflects an intrinsic characteristic of the data. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00723 |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Wei Long (Department of Economics, Tulane University, New Orleans, LA 70118, USA) |
| Abstract: | This paper develops a persistence-robust inferential framework for predictive expectile regression with highly persistent regressors. We combine expectile score equations with IVX instruments to construct an IVX-expectile estimator that preserves the distributional interpretation of expectile regression while regularizing the nonstandard effects of near-unit-root regressors, endogeneity, and conditional heteroscedasticity. For fixed expectile levels, we establish consistency and asymptotic normality of the estimator and show that the associated Wald statistic converges to a standard chi-square distribution. Simulation evidence indicates that the proposed procedure delivers accurate size for regressors with differential persistence, with only a modest local-power cost relative to conventional methods. In an application to monthly and quarterly U.S. stock return predictability, the method detects substantially asymmetric predictive ability across expectiles, showing that IVX-expectile regression provides a useful tool for studying heterogeneous predictive effects and downside tail risk when predictors are highly persistent. |
| Keywords: | IVX inference; Persistent predictors; Predictive expectile regression; Stock return |
| JEL: | C32 C51 C58 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202610 |
| By: | Demetrio Lacava |
| Abstract: | This paper introduces a new extension of the Conditional Autoregressive Value at Risk (CAViaR) model aimed at improving tail risk forecasting across assets. The proposed component-based model, CAViaR with Spillover Effects (CAViaR-SE), decomposes the conditional Value at Risk into a proper-risk component and a spillover component driven by a linear combination of tail risks from influential assets. These assets are selected via a recursive partial correlation algorithm, allowing multiple spillover sources with minimal parameterization. The spillover component acts as a predictable quantile shifter, directly affecting the conditional quantile dynamics rather than the volatility scale. Empirical results on Dow Jones Industrial Average stocks show that spillover effects account for a substantial share of total tail risk and significantly improve out-of-sample tail risk forecasts. Backtesting procedures, together with Model Confidence Set (MCS) analysis, confirm that CAViaR-SE provides well-calibrated risk measures and statistically superior forecasts compared to standard and augmented CAViaR models. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25217 |