Titlebook: Research Papers in Statistical Inference for Time Series and Related Models; Essays in Honor of M Yan Liu,Junichi Hirukawa,Yoshihide Kakiza

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Portmanteau Tests for Semiparametric Nonlinear Conditionally Heteroscedastic Time Series Models,l framework, the usual Box–Pierce portmanteau test statistic, based on the sum of the squares of the first residual autocorrelations, cannot be accurately approximated by a parameter-free distribution. A first solution is to estimate from the data the complicated asymptotic distribution of the Box–P

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Parameter Estimation of Standard AR(1) and MA(1) Models Driven by a Non-I.I.D. Noise,e intervals in parameter estimation. We consider AR(1) and MA(1) models and motivate the need for correction of standard errors when these are generated by a non-i.i.d. noise. The impact of the noise on the standard errors and confidence intervals is illustrated with Monte Carlo simulations using va

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Rank Tests for Randomness Against Time-Varying MA Alternative,by [., .]. We use the linear serial rank statistics and apply the notion of the contiguity [.] for the testing problem. Under the null hypothesis, the joint asymptotic normality of the proposed rank test statistics and log-likelihood ratio is established by making use of the local asymptotic normal

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