Bootstrap based probability forecasting in multiplicative error models

Indeewara Perera and Mervyn J. Silvapulle

Journal of Econometrics, 2021, vol. 221, issue 1, 1-24

Abstract: As evidenced by an extensive empirical literature, multiplicative error models (MEM) show good performance in capturing the stylized facts of nonnegative time series; examples include, trading volume, financial durations, and volatility. This paper develops a bootstrap based method for producing multi-step-ahead probability forecasts for a nonnegative valued time-series obeying a parametric MEM. In order to test the adequacy of the underlying parametric model, a class of bootstrap specification tests is also developed. Rigorous proofs are provided for establishing the validity of the proposed bootstrap methods. The paper also establishes the validity of a bootstrap based method for producing probability forecasts in a class of semiparametric MEMs. Monte Carlo simulations suggest that our methods perform well in finite samples. A real data example illustrates the methods.

Keywords: Multiplicative error model; Bootstrap; Probability forecast; Goodness-of-fit; Multi-step forecast (search for similar items in EconPapers)
JEL-codes: C12 C52 C53 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:221:y:2021:i:1:p:1-24

DOI: 10.1016/j.jeconom.2020.01.022

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