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on Econometric Time Series |
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Issue of 2025–10–06
nine papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Gianluca Cubadda (CEIS & DEF, Università di Roma "Tor Vergata") |
| Abstract: | The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI], originally proposed by Reinsel (1983), and their applications to economic and ?nancial time series. MAI has recently gained momentum because it can be seen as a link between two popular but distinct multivariate time series approaches: vector autoregressive modeling [VAR] and the dynamic factor model [DFM]. Indeed, on the one hand, the MAI is a VAR model with a peculiar reduced-rank structure; on the other hand, it allows for identi?cation of common components and common shocks in a similar way as the DFM. The focus is on recent developments of the MAI, which include extending the original model with individual autoregressive structures, stochastic volatility, time-varying parameters, high-dimensionality, and cointegration. In addition, new insights on previous contributions and a novel model are also provided. |
| Keywords: | Multivariate autoregressive index models, vector autoregressive models, dynamic factor models, reduced-rank regression |
| Date: | 2025–09–22 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:611 |
| By: | Xiyuan Liu (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Liangjun Su (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China) |
| Abstract: | We study a novel time-varying (TV) factor-augmented (FA) forecasting model, where the forecast target is driven by a strict subset of all the latent factors driving the predictors. To consistently select the target-related factors and estimate the TV parameters simultaneously, we first obtain the unobserved common factors via the local principal component analysis. Next, we conduct a variable selection procedure via a time-varying weighted group least absolute shrinkage and selection operator to select relevant factors. The identification restrictions used in this paper permit asymptotically rotation-free estimation of both factors and loadings. The asymptotic properties, such as consistency, sparsity and the oracle property of these two-step estimators are established. Simulation studies demonstrate the excellent finite sample performance of the proposed estimators. In an empirical application to the U.S. macroeconomic dataset, we show that the penalized TV-FA forecasting model outperforms the conventional TV-FAVAR model in predicting certain key macroeconomic series |
| Keywords: | Factor-augmented forecasting models; Local-linear smoothing; Structural change; Weighted group LASSO, Time-varying modeling |
| JEL: | C13 C23 C33 C38 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202515 |
| By: | Gergely Ganics (BANCO DE ESPAÑA); Lluc Puig Codina (UNIVERSITY OF ALICANTE AND BANCO DE ESPAÑA) |
| Abstract: | We propose a simplified framework for evaluating conditional predictive densities based on the probability integral transform (PIT). The approach accommodates a wide range of estimation schemes, including expanding and rolling windows, and applies to both stationary and non-stationary processes. By treating the PIT as a primitive, our approach enables researchers to apply widely used tests in settings where their validity was previously uncertain. Monte Carlo simulations demonstrate favorable size and power properties of the tests. In an empirical application, we show that incorporating stochastic volatility into an unobserved components model is essential for generating correctly calibrated density forecasts of US industrial production growth at both monthly and quarterly frequencies. |
| Keywords: | predictive density, forecast evaluation, probability integral transform, Kolmogorov–Smirnov test, Cramér–von Mises test |
| JEL: | C22 C52 C53 |
| Date: | 2035–09 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2535 |
| By: | Mayer, Alexander; Wied, Dominik; Troster, Victor |
| JEL: | C12 C22 C52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325369 |
| By: | Lam, Clifford; Cen, Zetai |
| Abstract: | We introduce the matrix-valued time-varying Main Effects Factor Model (MEFM). MEFM is a generalization to the traditional matrix-valued factor model (FM). We give rigorous definitions of MEFM and its identifications, and propose estimators for the time-varying grand mean, row and column main effects, and the row and column factor loading matrices for the common component. Rates of convergence for different estimators are spelt out, with asymptotic normality shown. The core rank estimator for the common component is also proposed, with consistency of the estimators presented. As time series, the row and column main effects { α t } and { β t } can be non-stationary without affecting the estimation accuracy of our estimators. The number of main effects factors contributing to row or column main effects is also consistently estimated by our proposed estimators. We propose a test for testing if FM is sufficient against the alternative that MEFM is necessary, and demonstrate the power of such a test in various simulation settings. We also demonstrate numerically the accuracy of our estimators in extended simulation experiments. A set of NYC Taxi traffic data is analyzed and our test suggests that MEFM is indeed necessary for analyzing the data against a traditional FM. |
| Keywords: | large-scale dependent data; time-varying row and column effects; MEFM and FM interchange; sufficiency of FM over MEFM; Tucker decomposition |
| JEL: | J1 C1 |
| Date: | 2025–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129557 |
| By: | Igor L. Kheifets (UNC Charlotte); Peter C. B. Phillips (Yale University) |
| Abstract: | Optimal estimation is explored in long run relations that are modeled within a semiparametric triangular multicointegrated system. In nonsingular cointegrated systems, where there is no multicointegration, optimal estimation is well understood (Phillips, 1991a). This paper establishes corresponding optimal results for singular systems, thereby accommodating a wide class of multicointegrated nonstationary time series with nonparametric transient dynamics. The optimality and sub-optimality of existing estimators are considered and new optimal estimators of both the cointegrating and multicointegrating coefficients are introduced that are based on spectral regression. |
| Date: | 2025–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2463 |
| By: | Fekria Belhouichet; Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines the long-memory properties of returns of exchange-traded funds (ETFs) specializing in robotics and artificial intelligence (AI) listed on the US market, as well as those of other assets such as the WTI crude oil price (West Texas Intermediate), Bitcoin, the S&P 500 index, 10-year US Treasury bonds, and the VIX volatility index. The frequency is daily and the sample period goes from 1 January 2023 to 23 June 2025. The adopted fractional integration framework is more general and flexible than those previously used in related studies, and sheds light on the degree of persistence of returns. The evidence suggests that all returns series examined are highly persistent, regardless of the error structure assumed, and that in general a linear model is appropriate to capture their evolution over time. The implications are that that the newly developed assets do not offer to investors additional hedging and diversification opportunities compared to more traditional ones, and that the creation of these additional financial instruments does not pose fresh challenges to policy makers tasked with financial stability. |
| Keywords: | persistence, fractional integration, long memory, trends, robotics ETFs, AI ETFs |
| JEL: | C22 G11 G12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12171 |
| By: | Rehim Kılıç |
| Abstract: | This paper investigates whether the “virtue of complexity” (VoC), documented in equity return prediction, extends to exchange rate forecasting. Using nonlinear Ridge regressions with Random Fourier Features (Ridge–RFF), we compare the predictive performance of complex models against linear regression and the robust random walk benchmark. Forecasts are constructed across three sets of economic fundamentals—traditional monetary, expanded monetary and non-monetary, and Taylor-rule predictors—with nominal complexity varied through rolling training windows of 12, 60, and 120 months. Our results offer a cautionary perspective. Complexity delivers only modest, localized gains: in very small samples with rich predictor sets, Ridge–RFF can outperform linear regression. Yet these improvements never translate into systematic gains over the random walk. As training windows expand, Ridge–RFF quickly loses ground, while linear regression increasingly dominates, at times even surpassing the random walk under expanded fundamentals. Market-timing analyses reinforce these findings: complexity-based strategies yield occasional short-sample gains but are unstable and prone to sharp drawdowns, whereas simpler linear and random walk strategies provide more robust and consistent economic value. By incorporating formal forecast evaluation tests—including Clark–West and Diebold–Mariano—we show that apparent gains from complexity are fragile and rarely statistically significant. Overall, our evidence points to a limited virtue of complexity in FX forecasting: complexity may help under narrowly defined conditions, but parsimony and the random walk benchmark remain more reliable across samples, predictor sets, and economic evaluations. |
| Keywords: | Foreign exchange rate; Exchange rate disconnect puzzle; Predictability; Complexity; Machine learning; Ridge; RFF |
| JEL: | F41 C50 G11 G15 |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-89 |
| By: | Wiechers, Lukas |
| JEL: | C14 C22 G01 G12 G14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325420 |