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on Econometric Time Series |
| By: | Oriol González-Casasús; Frank Schorfheide |
| Abstract: | VARs are often estimated with Bayesian techniques to cope with model dimensionality. The posterior means define a class of shrinkage estimators, indexed by hyperparameters that determine the relative weight on maximum likelihood estimates and prior means. In a Bayesian setting, it is natural to choose these hyperparameters by maximizing the marginal data density. However, this is undesirable if the VAR is misspecified. In this paper, we derive asymptotically unbiased estimates of the multi-step forecasting risk and the impulse response estimation risk to determine hyperparameters in settings where the VAR is (potentially) misspecified. The proposed criteria can be used to jointly select the optimal shrinkage hyperparameter, VAR lag length, and to choose among different types of multi-step-ahead predictors; or among IRF estimates based on VARs and local projections. The selection approach is illustrated in a Monte Carlo study and an empirical application. |
| JEL: | C11 C32 C52 C53 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33474 |
| By: | Angelo Milfont; Alvaro Veiga |
| Abstract: | We aim to develop a time series modeling methodology tailored to high-dimensional environments, addressing two critical challenges: variable selection from a large pool of candidates, and the detection of structural break points, where the model's parameters shift. This effort centers on formulating a least squares estimation problem with regularization constraints, drawing on techniques such as Fused LASSO and AdaLASSO, which are well-established in machine learning. Our primary achievement is the creation of an efficient algorithm capable of handling high-dimensional cases within practical time limits. By addressing these pivotal challenges, our methodology holds the potential for widespread adoption. To validate its effectiveness, we detail the iterative algorithm and benchmark its performance against the widely recognized Path Algorithm for Generalized Lasso. Comprehensive simulations and performance analyses highlight the algorithm's strengths. Additionally, we demonstrate the methodology's applicability and robustness through simulated case studies and a real-world example involving a stock portfolio dataset. These examples underscore the methodology's practical utility and potential impact across diverse high-dimensional settings. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.20816 |
| By: | Hossein Hassani; Leila Marvian Mashhad; Manuela Royer-Carenzi (I2M - Institut de Mathématiques de Marseille - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Mohammad Reza Yeganegi; Nadejda Komendantova |
| Abstract: | This paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on striking differences in sample ACF and PACF. Such findings prove particularly important when assessing model adequacy and discerning between residuals of different models, especially ARMA processes. This study addresses issues involving testing procedures, for instance, the Ljung–Box test, to select the correct time series model determined in the review. With the improvement in understanding the features of white noise, this work enhances the accuracy of modeling diagnostics toward real forecasting practice, which gives it applied value in time series analysis and signal processing. |
| Keywords: | time series analysis, model selection, Hassani -1/2 theorem, white noise, ARMA, Gaussian, Ljung-Box test |
| Date: | 2025–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04937317 |
| By: | Xiangdong Liu; Sicheng Fu; Shaopeng Hong |
| Abstract: | Volatility forecasting in financial markets is a topic that has received more attention from scholars. In this paper, we propose a new volatility forecasting model that combines the heterogeneous autoregressive (HAR) model with a family of path-dependent volatility models (HAR-PD). The model utilizes the long- and short-term memory properties of price data to capture volatility features and trend features. By integrating the features of path-dependent volatility into the HAR model family framework, we develop a new set of volatility forecasting models. And, we propose a HAR-REQ model based on the empirical quartile as a threshold, which exhibits stronger forecasting ability compared to the HAR-REX model. Subsequently, the predictive performance of the HAR-PD model family is evaluated by statistical tests using data from the Chinese stock market and compared with the basic HAR model family. The empirical results show that the HAR-PD model family has higher forecasting accuracy compared to the underlying HAR model family. In addition, robustness tests confirm the significant predictive power of the HAR-PD model family. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.00851 |
| By: | Chen Tong; Peter Reinhard Hansen |
| Abstract: | We introduce a new dynamic factor correlation model with a novel variation-free parametrization of factor loadings. The model is applicable to high dimensions and can accommodate time-varying correlations, heterogeneous heavy-tailed distributions, and dependent idiosyncratic shocks, such as those observed in returns on stocks in the same subindustry. We apply the model to a "small universe" with 12 asset returns and to a "large universe" with 323 asset returns. The former facilitates a comprehensive empirical analysis and comparisons and the latter demonstrates the flexibility and scalability of the model. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.01080 |