For 100 companies, I have collected (i) tweets
and (ii) corporate website pageviews
for 148
days. The tweetvolume and pageviews per day are two independent variables corpaired against the stock trading volume
for each company, resulting in 100 x 148 = 14,800 observations. My data is structured like this:
company date tweetVol pageviewVol tradingVol
------------------------------------------------
1 1 200 150 2423325
1 2 194 152 2455343
1 3 214 199 3100429
. . . . .
. . . . .
1 148 205 233 2563463
2 1 752 932 7434124
2 2 932 2423 7464354
2 3 600 1435 5324323
. . . . .
. . . . .
. . . . .
100 148 3 155 32324
Because there is much difference in company-size (some companies only receive 2 tweets per day, where others like Apple get over 10,000 per day), all variables are logged to smoothen distribution. (This is in line with previous research - this is for my thesis).
I just performed a linear regression on this data, including both independend variables. R-Squared is .411 but Durbin-Watson only .141 (!) Without looking for the exact bounderies, I know this directly means my residuals are non-linear, eg. auto-correlated, right?
My question is: how can I solve this? When I think about it, this data should not be autocorrelated, so I don't really understand. Is it due to this actually being a timeseries analysis? I wouldn't think that either, since for instance trading volume today is independent of yesterdays trading volume. Can somebody explain this to me?
P.S. At my university, we use SPSS/PASW without additional modules, so I am unable to perform a timeseries analysis on this like you could in STATA or R.
stockvol0Log
with variableswikiLog
, 'svi' and 'hbVolLog' returns a Durbin-Watson of 0.276, see here for SPSS output: i.imgur.com/Nq0YI.png. SPSS does not support calculating p-value for the Durbin-Watson. I found an Excel-plugin that could (bit.ly/OMZZZ0) but with my list of residuals, it froze. I applied for a student license for SHAZAM (econometrics.com), a statistical package that does calculate p-value. $\endgroup$return0Pct
andvolatility0
are the other two dependent variables in my research. 'hbBull' and 'hbAgree' are independent variables, who can be added to the regression. I'm planning on performing a regression of all 5 independent variables (wikilog
,svi
,hbVolLog
,hbBull
, andhbAgree
) on all three dependent variables (return0Pct
,stockvol0Log
, andvolatility0
). In reality, I have more independent variables, but these are the most important ones. $\endgroup$