One assumption of OLS regression is that residuals are idependent, so that there is no autocorrelation.
When I checked the assumption, I noticed that autocorrelation is present. Now here are two questions:
Is it always naccessary to remove autocorrelation? Even, when there is small autocorrelation?
I tried to remove autocorrelation by incorporating seasonality (i.a. dummy variables for days / months). But it didn't work. The residual plots looks exactly the same (see figure). What else can be done?
Here's a mit more information: I do not really have time series data. My data set has many observations for each point in time. For example, for the 01.01.2018 I have 5 observations with 5 different residuals. For the 02.01.2018 I have 1 observation and 1 residual. In order to detect autocorrelation, I averaged residuals for each day.
Can this be problematic? Because days with one observation are more likely to take extreme values, than averaged days with a large number of observations.