Fast estimation of a large TVP-VAR model with score-driven volatilities

Tingguo Zheng, Shiqi Ye and Yongmiao Hong

Journal of Economic Dynamics and Control, 2023, vol. 157, issue C

Abstract: This paper proposes a fast approach to estimating a large time-varying parameter vector autoregressive (TVP-VAR) model. Based on a score-driven modeling framework, we first assume that the time-varying variances of random errors in each equation of the TVP-VAR are score-driven, and then propose filtering and smoothing procedures to estimate time-varying parameters and time-varying volatilities. We show that under the forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, significantly reducing the dimension of state space and thus delivering fast estimation. Moreover, a fast smoothing estimation can be derived, avoiding the inverse of the super-high dimensional state equation covariance matrix. We provide dynamic model averaging (selection) and maximum likelihood estimates for forecasting and inference. Our simulation study shows that the proposed method is more accurate than the popular methods and enjoys tremendous computational gain from the equation-by-equation estimator. Finally, we conduct an empirical study on the dynamic connectedness of global stock markets, demonstrating the merits of our methods in real-time and ex-post analysis.

Keywords: Large TVP-VAR model; Score-driven volatility; Equation-by-equation estimator; Smoothing estimation; Dynamic connectedness (search for similar items in EconPapers)
JEL-codes: C11 C22 C32 C53 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:157:y:2023:i:c:s0165188923001689

DOI: 10.1016/j.jedc.2023.104762

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