Diagnostic checking of time series models

Kernel-based portmanteau diagnostic test for ARMA time series models

In this paper, the definition of the Toeplitz autocorrelation matrix is used to derive a kernel-based portmanteau test statistic for ARMA models. Under the null hypothesis of no serial correlation, the distribution of the test statistic is approximated by a standard normal using the kernel-based normalized spectral density estimator, without having to specify any alternative model. Unlike most ...

Testing for White Noise under Unknown Dependence and Its Applications to Diagnostic Checking for Time Series Models

Testing for white noise has been well studied in the literature of econometrics and statistics. For most of the proposed test statistics, such as the well-known Box–Pierce test statistic with fixed lag truncation number, the asymptotic null distributions are obtained under independent and identically distributed assumptions and may not be valid for dependent white noise. Because of recent popul...

Diagnostic Checking Periodic Autoregression Models With Application

An overview of model building with periodic autoregression (PAR) models is given emphasizing the three stages of model development: identification, estimation and diagnostic checking. New results on the distribution of residual autocorrelations and suitable diagnostic checks are derived. The validity of these checks is demonstrated by simulation. The methodology discussed is illustrated with an...

Diagnostic Model-Checking for Real-Time Systems

Uppaal is a new tool suit for automatic verification of networks of timed automata. In this paper we describe the diagnostic model-checking feature of Uppaal and illustrates its usefulness through the debugging of (a version of) the Philips Audio-Control Protocol. Together with a graphical interface of Uppaal this diagnostic feature allows for a number of errors to be more easily detected and c...

Diagnostic Checks in Time Series