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- W3010104562 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." @default.
- W3010104562 created "2020-03-13" @default.
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- W3010104562 date "2021-03-01" @default.
- W3010104562 modified "2023-09-23" @default.
- W3010104562 title "Bootstrap based probability forecasting in multiplicative error models" @default.
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- W3010104562 doi "https://doi.org/10.1016/j.jeconom.2020.01.022" @default.
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