What is the correct number of lags in arch LM test?

What is the correct number of lags in arch LM test?

I would like to ask you, what is the correct number of Lags in ARCH LM Test? I am referring to ArchTest in FinTS package, but other ArchTest (such as the one in Eviews) provide same results. In many time series, when I choose Lags between 1:5 the p.value is usually higher than 0.05, but with increasing of Lags, p.value becomes smaller.

Is the arch LM test a portmanteau test?

1 Answer 1. The ARCH-LM test is a portmanteau test. It tests a number of lags (lag 1 through lag $q$) at once as a group and tells you whether the average ARCH effect within the group is large.

What is the Engle’s arch multiplier test for?

Engle’s ARCH test is a Lagrange multiplier test to assess the significance of ARCH effects [1]. where is the conditional mean of the process, and is an innovation process with mean zero. where zt is an independent and identically distributed process with mean 0 and variance 1. Thus,

Is there an alternative to Engle’s arch test?

A large critical value indicates rejection of the null hypothesis in favor of the alternative. As an alternative to Engle’s ARCH test, you can check for serial dependence (ARCH effects) in a residual series by conducting a Ljung-Box Q-test on the first m lags of the squared residual series with lbqtest.

How to choose the optimal lag length in a time series?

If the LR test decision goes in favor of a more restrcited model, we should estimate the VAR model using fewer lags. Hence appropriate lag lengths can be chosen by paring down the ‘lag length’ using the ‘likelyhood ratio’ test, especially when the sample size is not small.

How is lag length selection using information criteria?

You can also see the annotations of others: click the in the upper right hand corner of the page The selection of lag lengths in AR and ADL models can sometimes be guided by economic theory. However, there are statistical methods that are helpful to determine how many lags should be included as regressors.