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on Econometric Time Series |
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Issue of 2025–11–10
nine papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Shovon Sengupta; Sunny Kumar Singh; Tanujit Chakraborty |
| Abstract: | Accurate macroeconomic forecasting has become harder amid geopolitical disruptions, policy reversals, and volatile financial markets. Conventional vector autoregressions (VARs) overfit in high dimensional settings, while threshold VARs struggle with time varying interdependencies and complex parameter structures. We address these limitations by extending the Sims Zha Bayesian VAR with exogenous variables (SZBVARx) to incorporate domain-informed shrinkage and four newspaper based uncertainty shocks such as economic policy uncertainty, geopolitical risk, US equity market volatility, and US monetary policy uncertainty. The framework improves structural interpretability, mitigates dimensionality, and imposes empirically guided regularization. Using G7 data, we study spillovers from uncertainty shocks to five core variables (unemployment, real broad effective exchange rates, short term rates, oil prices, and CPI inflation), combining wavelet coherence (time frequency dynamics) with nonlinear local projections (state dependent impulse responses). Out-of-sample results at 12 and 24 month horizons show that SZBVARx outperforms 14 benchmarks, including classical VARs and leading machine learning models, as confirmed by Murphy difference diagrams, multivariate Diebold Mariano tests, and Giacomini White predictability tests. Credible Bayesian prediction intervals deliver robust uncertainty quantification for scenario analysis and risk management. The proposed SZBVARx offers G7 policymakers a transparent, well calibrated tool for modern macroeconomic forecasting under pervasive uncertainty. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.23347 |
| By: | Bin Chen; Yuefeng Han; Qiyang Yu |
| Abstract: | In this paper, we consider diffusion index forecast with both tensor and non-tensor predictors, where the tensor structure is preserved with a Canonical Polyadic (CP) tensor factor model. When the number of non-tensor predictors is small, we study the asymptotic properties of the least-squared estimator in this tensor factor-augmented regression, allowing for factors with different strengths. We derive an analytical formula for prediction intervals that accounts for the estimation uncertainty of the latent factors. In addition, we propose a novel thresholding estimator for the high-dimensional covariance matrix that is robust to cross-sectional dependence. When the number of non-tensor predictors exceeds or diverges with the sample size, we introduce a multi-source factor-augmented sparse regression model and establish the consistency of the corresponding penalized estimator. Simulation studies validate our theoretical results and an empirical application to US trade flows demonstrates the advantages of our approach over other popular methods in the literature. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02235 |
| By: | Qiang Liu; Yiming Liu; Zhi Liu; Wang Zhou |
| Abstract: | In random matrix theory, the spectral distribution of the covariance matrix has been well studied under the large dimensional asymptotic regime when the dimensionality and the sample size tend to infinity at the same rate. However, most existing theories are built upon the assumption of independent and identically distributed samples, which may be violated in practice. For example, the observational data of continuous-time processes at discrete time points, namely, the high-frequency data. In this paper, we extend the classical spectral analysis for the covariance matrix in large dimensional random matrix to the spot volatility matrix by using the high-frequency data. We establish the first-order limiting spectral distribution and obtain a second-order result, that is, the central limit theorem for linear spectral statistics. Moreover, we apply the results to design some feasible tests for the spot volatility matrix, including the identity and sphericity tests. Simulation studies justify the finite sample performance of the test statistics and verify our established theory. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02660 |
| By: | Ollie Olby; Rory Baggott; Namid Stillman |
| Abstract: | The recent application of deep learning models to financial trading has heightened the need for high fidelity financial time series data. This synthetic data can be used to supplement historical data to train large trading models. The state-of-the-art models for the generative application often rely on huge amounts of historical data and large, complicated models. These models range from autoregressive and diffusion-based models through to architecturally simpler models such as the temporal-attention bilinear layer. Agent-based approaches to modelling limit order book dynamics can also recreate trading activity through mechanistic models of trader behaviours. In this work, we demonstrate how a popular agent-based framework for simulating intraday trading activity, the Chiarella model, can be combined with one of the most performant deep learning models for forecasting multi-variate time series, the TABL model. This forecasting model is coupled to a simulation of a matching engine with a novel method for simulating deleted order flow. Our simulator gives us the ability to test the generative abilities of the forecasting model using stylised facts. Our results show that this methodology generates realistic price dynamics however, when analysing deeper, parts of the markets microstructure are not accurately recreated, highlighting the necessity for including more sophisticated agent behaviors into the modeling framework to help account for tail events. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22685 |
| By: | Qiang Liu; Zhi Liu; Wang Zhou |
| Abstract: | We study the estimation of leverage effect and volatility of volatility by using high-frequency data with the presence of jumps. We first construct spot volatility estimator by using the empirical characteristic function of the high-frequency increments to deal with the effect of jumps, based on which the estimators of leverage effect and volatility of volatility are proposed. Compared with existing estimators, our method is valid under more general jumps, making it a better alternative for empirical applications. Under some mild conditions, the asymptotic normality of the estimators is established and consistent estimators of the limiting variances are proposed based on the estimation of volatility functionals. We conduct extensive simulation study to verify the theoretical results. The results demonstrate that our estimators have relative better performance than the existing ones, especially when the jump is of infinite variation. Besides, we apply our estimators to a real high-frequency dataset, which reveals nonzero leverage effect and volatility of volatility in the market. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00944 |
| By: | Ye Shen; Rui Song; Alberto Abadie |
| Abstract: | The Synthetic Control method (SC) has become a valuable tool for estimating causal effects. Originally designed for single-treated unit scenarios, it has recently found applications in high-dimensional disaggregated settings with multiple treated units. However, challenges in practical implementation and computational efficiency arise in such scenarios. To tackle these challenges, we propose a novel approach that integrates the Multivariate Square-root Lasso method into the synthetic control framework. We rigorously establish the estimation error bounds for fitting the Synthetic Control weights using Multivariate Square-root Lasso, accommodating high-dimensionality and time series dependencies. Additionally, we quantify the estimation error for the Average Treatment Effect on the Treated (ATT). Through simulation studies, we demonstrate that our method offers superior computational efficiency without compromising estimation accuracy. We apply our method to assess the causal impact of COVID-19 Stay-at-Home Orders on the monthly unemployment rate in the United States at the county level. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22828 |
| By: | Zhexiao Lin; Peng Ding |
| Abstract: | Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, recent research has studied time-series experiments from the design-based perspective, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods, we examine the design-based properties of regression-based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Our analysis allows for estimating a diverging number of treatment effects simultaneously, and establishes the consistency and asymptotic normality of the regression-based estimators. Additionally, we show that asymptotically, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true, design-based variances. Importantly, although our approach relies on regression, our design-based framework allows for misspecification of the regression model. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22864 |
| By: | Jose Barrales-Ruiz (Faculty of Economics and Government, Universidad San Sebastian); Gyeongho Kim (Department of Economics, University of Utah); Ivan Mendieta-Munoz (Department of Economics, University of Utah) |
| Abstract: | This paper provides an empirical analysis of the dynamic determinants of US labor productivity growth by considering that the latter is an endogenous outcome, mainly influenced by changes in the size of the economy and relative labor costs. Specifically, we consider that changes in GDP, real wages, wages relative to the price of capital, and investment effect simultaneously the evolution of labor productivity growth. We focus on studying whether the response of the latter to these effects has been stable or time-varying by adopting a flexible hybrid time-varying parameter Bayesian vector autoregression with stochastic volatility empirical framework. This allows us identify whether none, some, or all lagged and contemporaneous coefficients in the equations in the model are constant or time-varying via model selection. We find: (i) evidence supporting the view of time-varying endogenous labor productivity growth dynamics; (ii) that the response of labor productivity growth to GDP growth has tended to increase over time; and (iii) that the response of labor productivity growth to real wage growth has tended to decrease over time. Our findings have important policy recommendations that can help to improve the future performance of labor productivity growth in the USA. |
| Keywords: | Labor productivity growth, induced technical change effect, Kaldor-Verdoorn effect, model selection, time-varying parameter vector autoregressions |
| JEL: | B50 C11 C32 C52 E12 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:new:wpaper:2515 |
| By: | Marta Garcia-Rodriguez (Bank of Spain); Roman Horvath (Institute of Economic Studies, Charles University); Clemente Pinilla-Torremocha (Bank of England and European Research University-ERC-London) |
| Abstract: | We examine how the macroeconomic effects of temperature shocks have evolved in the United States since 1947. Using a time-varying parameter vector autoregression with stochastic volatility estimated on monthly data, we document a structural shift in the propagation of temperature shocks. Before the 1980s, higher temperatures induced demand-like dynamics—output and prices rose together. Since the 1980s, responses have become supply-like: real activity declines persistently while prices rise on impact and turn negative thereafter. A sectoral decomposition confirms shifts in agriculture, manufacturing, and services, with the services sector the primary driver of recent GDP dynamics. Our results reveal that food, services, and energy prices drive most of the aggregate price adjustments, while core prices remain muted. Temperature shocks now explain a rising share of medium-run output and price variation, and greater ex-ante temperature uncertainty depresses equity valuations on impact. Overall, temperature shocks have become increasingly contractionary and inflationary in nature. |
| Keywords: | Temperature shocks, Time-varying VAR, US economy |
| JEL: | C22 E30 E32 Q54 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2025_21 |