I ran a mixed effect model in R by using lme() to analyze the following data (an excerpt is shown in the following):
name day daily_amount lag_raised_amount inv_count length <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 abcde 2 232 1901 20 58 2 abcde 3 1023 2133 23 58 3 abcde 4 101 3156 24 58 4 abcde 5 0 3257 24 58 5 abcde 6 0 3257 24 58 6 abcde 7 4500 3257 24 58
In sum I have an unbalanced panel with the following metrics
$n=name=131, T=day=8-102, N=6680$
Background of the data set: I observed funding campaigns of companies on a daily basis. In a first step I want to measure some kind of "herding", specifically whether the
daily_amount raised on day $t$ depends on the total
lag_raised_amount observed on day $t-1$ (plus controlled for various company-sepcific fixed effects such as (campaign)length and time-varying effects such as investor count).
I ran the following (here abbreviated) model to account for this structure and auto-correlation:
mixed <- lme(log(daily_amount+1) ~ lag_raised_amount_log + I(lag_raised_amount_log^2) + log(lag_inv_count+1) + I(log(lag_inv_count^2+1)) + log(length) + day + I(day^2) , random = list(~day|name), data = panel, correlation = corAR1())
Assessing the the fit of this model gives me now a headache for some time:
The normal residuals plot, however, looks a bit weird to me: it is somewhat turned by 45 degrees and the straight "line" looks off.
- Is the residual plot as alarming as I think?
- What can I do about it, specifically, how can I better account for the zero-inflated DV? I know quite a bit about modelling zero-inflated continuous DVs with hurdle models, but I haven't found anything comparable in context of mixed effects models (+ I am not sure whether this is necessary at all).