|
on Econometric Time Series |
|
Issue of 2026–01–19
thirteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | 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:bny:wpaper:0144 |
| By: | Daniel Lewis; Karel Mertens |
| Abstract: | We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers prioritizing robustness against weak instrument bias should favor vector autoregressions (VARs) over local projections (LPs). Existing testing procedures are ill-suited for assessing weak instrument bias in IRF estimates, and we propose a novel simple test based on the usual first stage F-statistic. We investigate instrument strength in several applications from the literature, and discuss to what extent structural parameters must be restricted ex-ante to reject meaningful bias due to weak identification. |
| Date: | 2026–01–05 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:01/26 |
| By: | Lucas, André; Schwaab, Bernd; Zhang, Xin; D’Innocenzo, Enzo |
| Abstract: | We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, i.e., integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs by their thresholds to obtain a more parsimonious model with only one time-varying parameter to describe the entire tail. We establish parameter regions for stationarity, ergodicity, and invertibility for the integrated time-varying parameter model and its filter, and formulate conditions for consistency and asymptotic normality of the maximum likelihood estimator. Using two cryptocurrency exchange rates, we illustrate how the simple single-parameter model is competitive in capturing the dynamics of VaR and ES, particularly in the extreme tail. JEL Classification: C22, G11 |
| Keywords: | dynamic tail risk, extreme value theory, integrated score-driven models |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263166 |
| By: | Fabio Canova; Luca Fosso |
| Abstract: | This paper studies the consequences of using a deterministic steady state in Vector Autoregressive (VAR) models, when the data may display structural breaks, transitional dynamics or low-frequency fluctuations. We document substantial upward biases in the estimated coefficients, with distortions further amplified by the identification scheme. Allowing the steady state to be stochastic, however, reduces these distortions. To address this issue, we propose a spike-and-slab prior to differentiate between two alternative long-run specifications. Finally, we apply our empirical framework to revisit two well-known debates in macro: (i) the dynamics of hours in response to technology shocks; (ii) the habit formation hypothesis and the humpshaped response of consumption to business cycle shocks. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:bny:wpaper:0145 |
| By: | Domenico Giannone; Michele Lenza; Giorgio Primiceri |
| Abstract: | It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In contrast, inference improves drastically when an uninformative prior is specified directly on the concentration parameter—the key nuisance parameter capturing instrument relevance. The resulting Bayesian credible intervals are asymptotically equivalent to the frequentist confidence intervals based on conditioning approaches, and remain robust to weak instruments. |
| JEL: | C01 C11 C12 C26 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34648 |
| By: | Charles Shaw |
| Abstract: | We introduce srvar-toolkit, an open-source Python package for Bayesian vector autoregression with shadow-rate constraints and stochastic volatility. The toolkit implements the methodology of Grammatikopoulos (2025, Journal of Forecasting) for forecasting macroeconomic variables when interest rates hit the effective lower bound. We provide conjugate Normal-Inverse-Wishart priors with Minnesota-style shrinkage, latent shadow-rate data augmentation via Gibbs sampling, diagonal stochastic volatility using the Kim-Shephard-Chib mixture approximation, and stochastic search variable selection. Core dependencies are NumPy, SciPy, and Pandas, with optional extras for plotting and a configuration-driven command-line interface. We release the software under the MIT licence at https://github.com/shawcharles/srvar-too lkit. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.19589 |
| By: | Pooyan Amir-Ahmadi; Marko Mlikota; Dalibor Stevanovi\'c |
| Abstract: | For a general class of dynamic and stochastic structural models, we show that (i) non-linearity in economic dynamics is a necessary and sufficient condition for time-varying parameters (TVPs) in the reduced-form VARMA process followed by observables, and (ii) all parameters' time-variation is driven by the same, typically few sources of stochasticity: the structural shocks. Our results call into question the common interpretation that TVPs are due to "structural instabilities". Motivated by our theoretical analysis, we model a set of macroeconomic and financial variables as a TVP-VAR with a factor-structure in TVPs. This reveals that most instabilities are driven by a few factors, which comove strongly with measures of macroeconomic uncertainty and the contribution of finance to real economic activity, commonly emphasized as important sources of non-linearities in macroeconomics. Furthermore, our model yields improved forecasts relative to the standard TVP-VAR where TVPs evolve as independent random walks. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.20152 |
| By: | Mokinski, Frieder; Roth, Markus |
| Abstract: | This note explores conditional forecasting under conditions on annual growth rates, where variables enter a (possibly structural) vector autoregressive (VAR) model in logarithms or logarithmic first differences. For example, imposing conditions on the annual growth rate of quarterly real GDP modeled in logarithms is challenging be- cause annual growth rates are nonlinear functions of the log variables. We address this by approximating the annual growth rate with a linear function of the model variables, enabling the use of standard conditional forecasting methods. An approximation error arises since the condition is not imposed directly; to mitigate this, we iteratively adjust the condition until the error is acceptable. We provide MATLAB companion code that also accepts other types of conditions: (1) conditions on the path of variables entering the VAR, (2) conditions on the path of structural shocks, and (3) conditions on sums of successive variable observations. |
| Keywords: | conditional forecasting, annual growth rate constraints, log-linear approximation, structural vector autoregression |
| JEL: | C32 C53 E17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:334532 |
| By: | Agust\'in Garc\'ia-Garc\'ia; Pablo Hidalgo; Julio E. Sandubete |
| Abstract: | Explainable Artificial Intelligence (XAI) is increasingly required in computational economics, where machine-learning forecasters can outperform classical econometric models but remain difficult to audit and use for policy. This survey reviews and organizes the growing literature on XAI for economic time series, where autocorrelation, non-stationarity, seasonality, mixed frequencies, and regime shifts can make standard explanation techniques unreliable or economically implausible. We propose a taxonomy that classifies methods by (i) explanation mechanism: propagation-based approaches (e.g., Integrated Gradients, Layer-wise Relevance Propagation), perturbation and game-theoretic attribution (e.g., permutation importance, LIME, SHAP), and function-based global tools (e.g., Accumulated Local Effects); (ii) time-series compatibility, including preservation of temporal dependence, stability over time, and respect for data-generating constraints. We synthesize time-series-specific adaptations such as vector- and window-based formulations (e.g., Vector SHAP, WindowSHAP) that reduce lag fragmentation and computational cost while improving interpretability. We also connect explainability to causal inference and policy analysis through interventional attributions (Causal Shapley values) and constrained counterfactual reasoning. Finally, we discuss intrinsically interpretable architectures (notably attention-based transformers) and provide guidance for decision-grade applications such as nowcasting, stress testing, and regime monitoring, emphasizing attribution uncertainty and explanation dynamics as indicators of structural change. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12506 |
| By: | Neville Francis; Peter Reinhard Hansen; Chen Tong |
| Abstract: | We take a new perspective on identification in structural dynamic models: rather than imposing restrictions, we optimize an objective. This provides new theoretical insights into traditional Cholesky identification. A correlation-maximizing objective yields an Order- and Scale-Invariant Identification Scheme (OASIS) that selects the orthogonal rotation that best aligns structural shocks with their reduced-form innovations. We revisit a large number of SVAR studies and find, across 22 published SVARs, that the correlations between structural and reduced-form shocks are generally high. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17005 |
| By: | Marc-Oliver Pohle; Jan-Lukas Wermuth; Christian H. Wei{\ss} |
| Abstract: | Kendall's tau and Spearman's rho are widely used tools for measuring dependence. Surprisingly, when it comes to asymptotic inference for these rank correlations, some fundamental results and methods have not yet been developed, in particular for discrete random variables and in the time series case, and concerning variance estimation in general. Consequently, asymptotic confidence intervals are not available. We provide a comprehensive treatment of asymptotic inference for classical rank correlations, including Kendall's tau, Spearman's rho, Goodman-Kruskal's gamma, Kendall's tau-b, and grade correlation. We derive asymptotic distributions for both iid and time series data, resorting to asymptotic results for U-statistics, and introduce consistent variance estimators. This enables the construction of confidence intervals and tests, generalizes classical results for continuous random variables and leads to corrected versions of widely used tests of independence. We analyze the finite-sample performance of our variance estimators, confidence intervals, and tests in simulations and illustrate their use in case studies. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.14609 |
| By: | Pablo Guerron-Quintana (Boston College; Boston College); Alexey Khazanov (Hebrew University of Jerusalem); Molin Zhong Author-X-Name-First Molin (Board of Governors of the Federal Reserve System) |
| Abstract: | We propose a nonlinear dynamic factor model to examine the impact of external shocks and internal forces on macroeconomic and financial data fluctuations. This model permits nonlinear dynamics, enables asymmetric, state-dependent, and size- dependent responses to shocks, and generates time-varying volatility and asymmetric tail risk behavior. We find evidence of nonlinear dynamics in a U.S. application on measuring the credit cycle. The nonlinear factor stimulates credit growth during booms but hinders recovery after crises, with shocks having longer, amplified effects during credit crunches. An extended model that separates first- and second-order effects of the factor reveals that credit cycles exhibit state dependence in which shocks that spur growth during modest credit conditions trigger sharp busts during excessive expansions, thereby predicting how credit conditions can flip from boom to crisis. |
| Keywords: | Asymmetric dynamics, credit cycle, second-order factor, state dependence, tail risk |
| Date: | 2026–01–12 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1106 |
| By: | Siddhartha Chib; Fei Tan |
| Abstract: | We show how state-of-the-art large language models (LLMs), seemingly inapplicable to the small samples typical of macroeconomics, can be trained to learn the language of macroeconomy. We estimate a large-scale dynamic stochastic general equilibrium (DSGE) model on an initial segment of the data and obtain a posterior distribution over structural parameters. We sample from this posterior to generate millions of theory-consistent synthetic panels that, when mixed with actual macroeconomic data, form the training corpus for a time-series transformer with attention. The trained model is then used to forecast out-of-sample through 2025. The results show that this hybrid forecaster, which combines the theoretical coherence of DSGE models with the representational power of modern LLMs, successfully learns the macroeconomic language. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21031 |