Exercise

# Simplify the model with t-statistics

Besides p-values, t-statistics can also help decide the necessity of model parameters. In this exercise, you will practice using t-statistics to assess the significance of model parameters.

The t-statistic is computed as the estimated parameter value subtracted by its expected mean (zero in this case), and divided by its standard error. The absolute value of the t-statistic is a distance measure, that tells you how many standard errors the estimated parameter is away from 0. As a rule of thumb, if the t-statistic is larger than 2, you can reject the null hypothesis.

You will work with the same GARCH model as the previous exercise. You can access the model fitting summary in `gm_result`

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Instructions

**100 XP**

- Get the model parameters, standard errors and t-statistic, and save them in the DataFrame
`para_summary`

. - Compute t-statistics manually using parameter values and their standard errors, and save the calculation result in
`calculated_t`

. - Print and review
`calculated_t`

. - Print and review
`para_summary`

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