I'm trying to use a Kalman Filter to estimate an online dynamic regression coefficient between two variables (e.g. http://www.thealgoengineer.com/2014/online_linear_regression_kalman_filter/)
In the example in the blog post, there is one new observation per time step, which is used to update the Kalman filter. What if you have N observations per time step - is there a generalized form of the Kalman filter you can use to update the filter with all N observations at once?
Or do you just do some type of averaging over the N observations and use the classic Kalman filter?
Edit: Let's say I have a universe of 3000 stocks and I have 2 alpha factors (value and momentum) computed for each stock in the universe at every time step
I want to use a Kalman filter to estimate FwdReturn = beta1 * momentum + beta2 * value + epsilon_i