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Evaluating multiplicative error models: A residual-based approach

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  • Ke, Rui
  • Lu, Wanbo
  • Jia, Jing

Abstract

This paper considers a residual-based approach to diagnose the adequacy of both the univariate and vector multiplicative error model (MEM). Two residual-based statistics are constructed based on the parameter estimates of the linear autoregressions with the standardized residuals as dependent variables. Since the autoregressions involve estimated standardized residuals, the correct asymptotic distributions of test statistics are obtained by taking into account the impact of parameter estimation uncertainty. Monte Carlo simulations indicate that the proposed test statistics perform well against their competitors in terms of empirical size and power. An empirical application further shows the usefulness of the proposed test in evaluating MEMs.

Suggested Citation

  • Ke, Rui & Lu, Wanbo & Jia, Jing, 2021. "Evaluating multiplicative error models: A residual-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:csdana:v:153:y:2021:i:c:s0167947320301778
    DOI: 10.1016/j.csda.2020.107086
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