I am currently analyzing the following panel data set with STATA on a daily base:
  • N = 324 companies
  • T = 252 trading days
  • 6 social media variables from Facebook and Twitter data (6 for Facebook, 6 for Twitter; e.g., daily answer times, number of posts and replies)
  • 2 financial performance variables from CRSP data (daily abnormal return, idiosyncratic risk)
I tried both fixed effects estimation (xtreg fe) and panel vector autoregression (pvar) but neither of the approaches yields satisfying results, i.e., very few significant effects. I also tried the Arellano-Bond approach (xtabond) but was not quite sure about the endogenous and predetermined regressors. Varying these yielded very few significant results.I also varied the operationalization of the social media and financial variables and looked at sub-samples (e.g., single industries, particular time frames, Facebook vs. Twitter sample) etc.

Apparently, for large T panels, the bias apparent for fixed effects estimation - the rationale for dynamic panel analysis - declines with time and eventually becomes insignificant, thus rendering a consistent fixed effects estimator. In comparison with other studies, I would assume that my T is rather large, thus a fixed effects estimation might be more sensible than a panel vector autoregression.

Any thoughts on this topic?

Thanks,
Sarah