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Lack-Of-Fit Testing Of The Conditional Mean Function In A Class Of Markov Multiplicative Error Models

Listed author(s):
  • Koul, Hira L.
  • Perera, Indeewara
  • Silvapulle, Mervyn J.
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    The family of multiplicative error models, introduced by Engle ( 2002 , Journal of Applied Econometrics 17, 425–446), has attracted considerable attention in recent literature for modeling positive random variables, such as the duration between trades at a stock exchange, volume transactions, and squared log returns. Such models are also applicable to other positive variables such as waiting time in a queue, daily/hourly rainfall, and demand for electricity. This paper develops a new method for testing the lack-of-fit of a given parametric multiplicative error model having a Markov structure. The test statistic is of Kolmogorov–Smirnov type based on a particular martingale transformation of a marked empirical process. The test is asymptotically distribution free, is consistent against a large class of fixed alternatives, and has nontrivial asymptotic power against a class of nonparametric local alternatives converging to the null hypothesis at the rate of O ( n –1/2 ). In a simulation study, the test performed better overall than the general purpose Ljung–Box Q -test, a Lagrange multiplier type test, and a generalized moment test. We illustrate the testing procedure by considering two data examples.

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    Article provided by Cambridge University Press in its journal Econometric Theory.

    Volume (Year): 28 (2012)
    Issue (Month): 06 (December)
    Pages: 1283-1312

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    Handle: RePEc:cup:etheor:v:28:y:2012:i:06:p:1283-1312_00
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