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on Econometric Time Series |
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Issue of 2026–05–11
twelve papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Andre Lucas (Vrije Universiteit Amsterdam); Yicong Lin (Vrije Universiteit Amsterdam) |
| Abstract: | This paper proposes a quasi-likelihood ratio (QLR) test for the null of constant parameters against the alternative of score-driven parameter dynamics. Score-driven models have been widely used in the literature to capture time variation in parameters across a diverse range of both continuous and discrete, univariate and multivariate time series models, with or without random regressors. A formal testing procedure, however, is lacking thus far. Our QLR test addresses two key challenges: (i) parameters may lie on the boundary of the parameter space, and (ii) nuisance parameters are not identified under the null. The test statistic’s non-standard asymptotic distribution takes a simple form that only depends on the specified parameter space and is invariant to the specific formulation of the score-driven model and its degree of nonlinearity. Consequently, the asymptotic distribution applies to a wide range of score-driven models and can easily be simulated to conduct inference. We illustrate the new test using several models from the score-driven literature and show that the limiting distribution provides an adequate approximation for inference in finite samples. |
| Keywords: | parameter constancy, score-driven models, quasi-likelihood ratio test, parameters on the boundary, nonidentification |
| JEL: | C10 C12 C32 |
| Date: | 2025–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250063 |
| By: | Markku Lanne; Jani Luoto; Adam Rybarczyk |
| Abstract: | We propose a new approach to inference in tightly identified and large-scale structural vector autoregressions based on a reparameterization that enables imposing identifying inequality restrictions through continuously differentiable mappings. Permitted inequality restrictions include shape and ranking restrictions as well as bounds on economically relevant elasticities, and the approach is also able to accommodate zero restrictions in a straightforward manner. We implement a Hamiltonian Monte Carlo algorithm and show how the posterior density can be rapidly evaluated under the reparameterization, thus facilitating inference in high-dimensional settings. Two empirical applications demonstrate that our approach tends to result in lower serial dependence in Markov chains, larger effective sample sizes and reduced computation time relative to existing methods. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.22445 |
| By: | Chaoyi Chen; Elena Pesavento; Balazs Vonnak |
| Abstract: | Local projections (LP) and vector autoregressions (VAR) are the two standard tools for impulse response analysis, but they often display a finite-sample trade-off: LP is typically less biased but more volatile, while VAR is more precise but can be biased under misspecification. We propose an easy-to-implement estimator-averaging approach that combines LP and VAR at each horizon by minimizing the mean squared error of the impulse response itself, rather than in-sample fit. We derive closed-form oracle weights for this finite-sample risk problem, develop feasible AR-sieve-bootstrap procedures, and compare them against an Rsquare-based model-averaging benchmark. For a benchmark class of short-memory linear data generating processes in which LP and VAR are both consistent, we establish the consistency and limiting distribution of the feasible averaged estimator. Monte Carlo results show meaningful risk reductions relative to LP and VAR alone. In an empirical application revisiting Bauer and Swanson (2023), estimator averaging delivers stable and economically intuitive responses for yields, activity, prices, and credit spreads. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.05456 |
| By: | Simon Donker van Heel (Erasmus University Rotterdam); Neil Shephard (Harvard University) |
| Abstract: | We propose using a discounted version of a convex combination of the log-likelihood with the corresponding expected log-likelihood such that when they are maximized they yield a filter, predictor and smoother for time series. This paper then focuses on working out the implications of this in the case of the canonical exponential family. The results are simple exact filters, predictors and smoothers with linear recursions. A theory for these models is developed and the models are illustrated on simulated and real data. |
| Keywords: | Exponential family, EWMA, Filtering, Likelihood, Time Series |
| JEL: | C1 C32 |
| Date: | 2025–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250074 |
| By: | Sicco Kooiker (Vrije Universiteit Amsterdam); Janneke van Brummelen (Vrije Universiteit Amsterdam); Julia Schaumburg (Vrije Universiteit Amsterdam); Marcin Zamojski (Vrije Universiteit Amsterdam) |
| Abstract: | We propose a factor model with time-varying loadings for term structure modeling and forecasting. While maintaining the interpretation of the factors as level, slope, and curvature through explicit identification restrictions, we allow the loadings to take flexible shapes by specifying them as neural networks that evolve over time using a “self-driving†updating scheme based on past forecast errors, with gradient scaling to improve robustness. Using an empirically calibrated simulation study and an application to U.S. Treasury yields across 24 maturities, we show that flexible and dynamic factor loadings improve forecasting performance relative to standard benchmarks, including Nelson-Siegel models and the random walk. The gains are strongest at medium maturities and shorter forecast horizons, highlighting the importance of capturing curvature dynamics. In-sample results further illustrate how time-varying loadings provide insight into changes in yield curve shape beyond traditional parametric specifications. |
| Keywords: | time-varying neural networks, observation-driven dynamics, yield curve |
| JEL: | C38 C45 E43 |
| Date: | 2026–02–26 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260007 |
| By: | Aleksey Kolokolov; Shifan Yu |
| Abstract: | We develop a continuous-time penalized regression framework for the estimation of time-varying coefficients and variable selection when both the response and covariates are It\^o semimartingales with jumps. The coefficient paths are approximated by spline basis expansions and estimated via least squares from truncated high-frequency increments. In a finite-dimensional setting, we establish consistency and derive a feasible asymptotic distribution for the integrated coefficient estimator under infill asymptotics. We then extend the framework to high-dimensional settings in which the number of candidate covariates diverges, and show that a group-wise penalized estimator with a truncated $\ell_1$-penalty attains the oracle property, which delivers both consistent model selection and coefficient estimation. An empirical application to a large panel of more than two hundred high-frequency factors documents sparse factor structure across a large cross-section of stocks and industry portfolios. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.23023 |
| By: | Sukhbir Kaur; Sukhbir Singh; Kanchan Jain; Pooja Soni |
| Abstract: | In this paper, a Mixed Data Sampling (MIDAS) model is studied when both low and high frequency variables are contaminated with measurement error. It is shown that the profile likelihood estimator becomes inconsistent in the presence of measurement error. Using the corrected score approach along with profile likelihood approach, a consistent estimator for parameters of MIDAS Measurement Error model is proposed. Small and large sample properties of the estimator are examined by performing a monte carlo simulation study and considering the effect of sample size, number of lags and profiling parameter. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.23469 |
| By: | Jan Rovirosa; Jesse Schmolze |
| Abstract: | Modeling the dynamics of non-stationary stochastic systems requires balancing the representational power of deep learning with the mathematical transparency of classical models. While classical Markov transition operators provide explicit, theoretically grounded rules for system evolution, their empirical estimation collapses due to severe data sparsity when applied to high-resolution, high-noise environments. We explore this statistical barrier using financial time series as a canonical, real-world testbed. To overcome the degeneracy of empirical counting, we introduce a framework that utilizes neural networks strictly as parameterization engines to generate explicit, time-varying Markov transition matrices. By constraining the neural network to output its predictions as a formal stochastic operator, we maintain complete structural interpretability. We demonstrate that these learned operators successfully capture complex regime shifts: the state-conditioned model achieves mean row heterogeneity $\bar{\rho} = 0.0073$ while the state-free ablation collapses to exactly zero, and operator row entropy correlates with realized variance at $r = -0.62$ ($p \approx 10^{-251}$), revealing that high-volatility regimes homogenize transition dynamics rather than diversify them. Furthermore, rather than enforcing the Chapman-Kolmogorov equations as a rigid structural requirement, we repurpose them as a localized diagnostic tool to pinpoint specific temporal windows where first-order memory assumptions break down. Ultimately, this framework demonstrates how neural networks can be constrained to make rigorous, classical operator analysis viable for complex real-world time series. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.04690 |
| By: | Joel M. David; Raffaella Giacomini; Xiyu Jiao; Weining Wang |
| Abstract: | State-dependent local projections (LPs) are widely used to estimate how responses to exogenous aggregate shocks vary as a function of observable state variables, yet their causal interpretation remains unclear. We show that this interpretation obtains under the sufficient condition that the conditional mean is linear in the aggregate shock at each horizon, and that this condition holds in a broad class of canonical micro-macro environments, including first-order perturbation solutions of heterogeneous-agent models and macro-finance models. Under this condition, LPs recover causal impulse responses without requiring specification of the full data-generating process. We further show that the causal interpretation of state-dependent LPs is robust to the choice of state variable. By contrast, commonly used linear interaction LPs generally fail to recover causal objects. We therefore develop a sieve-based nonparametric LP estimator that restores causal interpretation and delivers valid pointwise and uniform inference in micro-macro panels. Empirically, allowing for nonparametric state dependence materially changes both the pattern of heterogeneous firm investment responses and their aggregate implications for the transmission of monetary policy shocks. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.05404 |
| By: | Martin Bruns; Helmut Lütkepohl; James McNeil |
| Abstract: | Several recent studies consider a set of proxies to identify different monetary policy shocks for different regions in the world. We show that the way the proxies are used to identify the monetary policy shocks may lead to correlated shocks and dubious structural analysis and we demonstrate how to overcome the problem of correlated shocks. We illustrate that, if correlated shocks are used in applied studies, key statistics of interest such as impulse responses and forecast error variance decompositions can be severely distorted and we consider benchmark studies on monetary policy in the euro area (EA), the US and the UK to demonstrate the problems. |
| Keywords: | Structural vector autoregression, proxy VAR, GMM, correlated structural shocks |
| JEL: | C32 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2163 |
| By: | Yusuke Oh (Deputy Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: yuusuke.ou@boj.or.jp)); Mototsugu Shintani (The University of Tokyo (E-mail: shintani@e.u-tokyo.ac.jp)) |
| Abstract: | We forecast Japanese recessions by integrating machine learning methods, mixed-frequency data, and text-based indicators within an unrestricted mixed data sampling (U-MIDAS) framework. The model combines monthly macroeconomic variables with weekly financial indicators and newspaper-based text indicators. A pseudo-real-time forecasting exercise over three decades shows that machine learning models consistently outperform traditional logit benchmarks. The model confidence set (MCS) suggests horizon dependence: Text indicators are more informative at short horizons, while financial variables are more informative at longer horizons. To improve interpretability, we apply sparse principal component analysis (Sparse PCA) to the text indicators and identify three economic narratives: 'Corporate Distress, ' 'Financial Distress, ' and 'Deflationary Pressure.' Furthermore, SHAP (SHapley Additive exPlanations) analysis indicates that different recession episodes are associated with different combinations of these narratives, underscoring the heterogeneous nature of economic downturns. |
| Keywords: | business cycles, mixed data sampling, model confidence set, text analysis, recession forecasting |
| JEL: | C32 C53 E37 O53 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:26-e-07 |
| By: | Daniel de Abreu Pereira Uhr; Guilherme Valle Moura |
| Abstract: | This paper develops a doubly robust extension of local-projections difference-in-differences (LP-DiD) for staggered absorbing treatments. The resulting estimator, DRLPDID, preserves the LP-DiD local-stack ATT target and is consistent when either the local untreated-outcome regression or the local treatment-probability model is correctly specified. It also delivers influence-function-based inference for post-treatment summaries and multiplier-bootstrap bands for dynamic paths. In Monte Carlo designs with covariate-driven selection, DRLPDID matches regression-adjusted LP-DiD under outcome-model alignment and clearly outperforms the IPT-only variant under propensity-score misspecification. In the no-fault-divorce application, DRLPDID tracks robust staggered-adoption estimators and is less negative than unadjusted LP-DiD. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.27035 |