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I really need some help here since I feel I have no one to ask this.

I am doing a research to find the relationship between the amount of Weibos (Chinese Twitter/Social Media) on a subject and the volatility on market prices, as keywords that the Weibo contain I chose 'gold' and 'stock'.

I got 2 samples, one around 30 days with a little over 4000 weibos and one around 38 days with a little over 6000 weibos, they are not random samples, in which I got them from the 100 most influential (by follower count) finance related accounts in weibo.

At one point my teacher asked me to check for stationarity and do a regression, however I had never done anything besides simple regressions.

So I start to use Eviews for this, do the unit root panel test, to which I find that both my samples, on both keywords, the vast majority of tests find a p lower than 1%, this means I can do a linear regression right?

I do ordinary least squares regression but the p I find for most samples is quite high, both stock keywords sample regress to the stock price index volatility (absolute change in price) with a p of 0.22 and 0.35 (sample 1 and 2), gold absolute market price change against gold keywords in weibo regresses at 0.32 and 0.01 in sample 1 and 2.

I try on the general linear model (GLM) functions, and I notice that if I choose newey west covariance method, family normal, link identity, my p value lowers considerably.

Is it ok for me to use the newey west covariance method in my case? I am a begginer and any tips or pointing in the right direction on the regression that can/should be used in my case would be very much appreciated!

Thank you very much, Daniel

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