Hi.
I'm working on an exercise where I have to obtain daily prices for the Norwegian and the US stock market indicies for the last 20 years. I used datastream to collect the numbers. For the Norwegian market I used OSEBX and SP500 for the US market.
One question that I have to answer is: Formally test for ARCH effects in the log returns series. Before I move on I must say that I only had estimation problem when testing for ARCH effects for SP500. The error msg I got: MA estimation requires a continuous sample.
My input when estimating equation: logrsp500 c ar(1) ma(1) (see workfile attahced)
logrsp500 = log return on sp500 data collected
Did the same procedure for my osebx data and I hade no problem with that.
eviews version: Std. edition jan 7 2010 build.
Thanks!
ARCH estimation error
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ARCH estimation error
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startz
- Non-normality and collinearity are NOT problems!
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Re: ARCH estimation error
You NAs in your sample. Hence it's not continuous.
Re: ARCH estimation error
So do I need to collect new data and try or can I do sometthing to my existing data ?
Also look at my osebx workfile I see that i have NAs in the sample too, but no error when estimating my ARCH equation.
Also look at my osebx workfile I see that i have NAs in the sample too, but no error when estimating my ARCH equation.
- Attachments
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- osebx.wf1
- osebxworkfile
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Re: ARCH estimation error
Missing values lead to a problem when they are scattered within the sample and hence resulting in discontinuity. EViews can handle them if they are only at the very beginning/end of the sample. If you like, you can get rid of missing values in your first data set as follows:
If you are going to work on return series, make sure you transform the data first and use it for contraction:
Code: Select all
pagecontract if sp500<>NACode: Select all
series ret = dlog(sp500)
pagecontract if ret<>NARe: ARCH estimation error
Thanks trubador, it worked now! :)Missing values lead to a problem when they are scattered within the sample and hence resulting in discontinuity. EViews can handle them if they are only at the very beginning/end of the sample. If you like, you can get rid of missing values in your first data set as follows:If you are going to work on return series, make sure you transform the data first and use it for contraction:Code: Select all
pagecontract if sp500<>NACode: Select all
series ret = dlog(sp500) pagecontract if ret<>NA
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