A Simple Asymptotically F-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models With Uncorrelated Innovations

2020 ◽  
pp. 1-17
Author(s):  
Xuexin Wang ◽  
Yixiao Sun
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Diagnostic Checking

scholarly journals Mixed Portmanteau Test for Diagnostic Checking of Time Series Models

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Sohail Chand ◽  
Shahid Kamal
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Goodness Of Fit ◽  
Model Building ◽  
Main Idea ◽  
Dependence Structure ◽  
Time Series Models ◽  
Goodness Of Fit Tests ◽  
Diagnostic Checking ◽  
Portmanteau Tests

Model criticism is an important stage of model building and thus goodness of fit tests provides a set of tools for diagnostic checking of the fitted model. Several tests are suggested in literature for diagnostic checking. These tests use autocorrelation or partial autocorrelation in the residuals to criticize the adequacy of fitted model. The main idea underlying these portmanteau tests is to identify if there is any dependence structure which is yet unexplained by the fitted model. In this paper, we suggest mixed portmanteau tests based on autocorrelation and partial autocorrelation functions of the residuals. We derived the asymptotic distribution of the mixture test and studied its size and power using Monte Carlo simulations.

The Box-Jenkins approach to random coefficient autoregressive modelling

1986 ◽  
Vol 23 (A) ◽  
pp. 231-240 ◽  
Author(s):  
D. F. Nicholls
Keyword(s):  
Time Series ◽  
Recent Time ◽  
Random Coefficient ◽  
Autoregressive Models ◽  
Model Specification ◽  
Time Series Models ◽  
Autoregressive Modelling ◽  
Diagnostic Checking ◽  
Constant Coefficients ◽  
Random Coefficient Autoregressive

Recent time series research has been directed towards the relaxation of the assumption that time series models have constant coefficients. One class of models to emerge as a result of this has been that of random coefficient autoregressive models. This paper demonstrates how the Box-Jenkins three-step approach of model specification, estimation and diagnostic checking may be applied to this class of models.

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