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@jstac @ChrisRackauckas @thomassargent30
This is a placeholder for the last of the 3 lectures we discussed. Here we can develop the theory around continuous-time markov chains, and explain where the ODE approximation of aggregate dynamics comes from. In addition, there are tools economists are not aware of (e.g. the gillepsie stuff)which provides a brownian motion approximation for the dynamics with granularity. Chris, you had a great outline for a lot of the development of the theory of these approximations
While I am not sure I am convinced that this is the source of volatlity in the covid data (since the volatility doesn't disappear when you go to larger countries with less granularity issues) it is still a great example to go through.
@jstac You had some thoughts on what we could do here. I moved the old text we had from the https://julia.quantecon.org/tools_and_techniques/numerical_linear_algebra.html#Continuous-Time-Markov-Chains-%28CTMCs%29 lecture into this new one, so we can use some of that as a base (or throw it out).