Hypothesis testing for multivariate processes(ljung box white noise test)

However, MATLAB is limited to univariate processes, so for multivariate processes, you will need to use a different software like Stata. Both MATLAB and Stata have manuals that provide information on how to calculate critical values for these tests.
  • #1
yasharea
2
0
hi guys, I'm trying to understand how the critical value of Q-statistics is calculated in multivariate ljung-box test. or even more simply, in the univariate ljung-box test.
initially i used MATLAB for hypothesis testing, MATLAB as an output furnishes the critical values, but the modules in MATLAB are limited to univariate processes. i use STAT for multivariate processes. in stata i don't know if its possible to get critical values or not. i have already looked into manuals of STATA and MATLAB.
thanks beforehand
yashar
 
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  • #2
The critical value of the Q-statistic in the univariate Ljung-Box test is usually calculated using the chi-squared distribution. In the multivariate Ljung-Box test, the critical value is usually calculated using the F-distribution. You can use software such as MATLAB or Stata to calculate the critical values for both tests.
 

Related to Hypothesis testing for multivariate processes(ljung box white noise test)

1. What is a multivariate process?

A multivariate process refers to a statistical process that involves multiple variables or factors. This means that the outcome or response variable is affected by more than one independent variable.

2. What is the Ljung-Box test in hypothesis testing for multivariate processes?

The Ljung-Box test is a statistical test used to check the presence of autocorrelation in a multivariate process. It is based on the Ljung-Box Q statistic, which measures the overall autocorrelation of the process. This test is commonly used in time series analysis to determine if the residuals of a model are independent.

3. How is the Ljung-Box test performed?

The Ljung-Box test is performed by calculating the Ljung-Box Q statistic for a given set of residuals and comparing it to a critical value from the Chi-Squared distribution. If the calculated Q statistic is greater than the critical value, it indicates that there is significant autocorrelation in the residuals and the null hypothesis of no autocorrelation can be rejected.

4. What is the purpose of the White Noise test in hypothesis testing for multivariate processes?

The White Noise test, also known as the Box-Pierce test, is used to check the randomness of the residuals in a multivariate process. It is based on the Box-Pierce Q statistic and is used to determine if the residuals are independent and normally distributed. This test is important in ensuring that the assumptions of a model are met.

5. Can the Ljung-Box and White Noise tests be used together in hypothesis testing for multivariate processes?

Yes, the Ljung-Box and White Noise tests can be used together to thoroughly check the validity of a multivariate process model. The Ljung-Box test checks for autocorrelation while the White Noise test checks for randomness in the residuals. Both tests are important in identifying any issues with the model and ensuring the accuracy of the results.

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