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Bootstrap based probability forecasting in multiplicative error models
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DOI：10.1016/j.jeconom.2020.01.022.png)
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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. (c) 2020 Elsevier B.V. All rights reserved.
Keyword:
Multiplicative error model
Bootstrap
Probability forecast
Goodness-of-fit
Multi-step forecast
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