Hallo,
I am a student and new to Stata. For a university course I have to model a GARCH model from time series data (euro-dollar exchange rate by the ECB).
I am not allowed to use the built-in modeling procedure but should do the procedure on my own step-by-step.
1. I load the data.
tsset newdate
2. I generate the first difference of the exchange rate.
gen Dvalue = value - L.value
3. I estimate the mean equation as an AR(1) process.
reg Dvalue L.Dvalue
4. I predict the residuals and square them.
predict Dresid, resid
gen Dresid_sq = Dresid^2
5. Now I run a regression of the squared residuals on its lag as a first step to get to the GARCH model.
reg Dresid_sq L.Dresid_sq
6. From this I want to compute the conditional variance of the time series
gen hsq = _b[_cons] + _b[L1.Dresid_sq]*L.Dresid_sq
7. Now, I want to estimate the GARCH(1,1) model.
reg hsq L.hsq L.Dresid_sq
Unfortunately, I get only coefficients no standard errors or p-values.
Would it be an option to do step 7 as a Maximum Likelihood Estimation? How would this be done?
Any help is appreciated. Thanks.
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