Skip to content

Markov chains in lectures #215

@jlperla

Description

@jlperla

Most require:

  • simulation from a distributional or discrete initial condition
  • access to the transition probability distribution, sometimes just mat-vec, occasionally rows/columns
  • access to the values associated with the discrete states
  • Most are small which might even work best as statics. Otherwise, all are dense.
    Tauchen could be truncated and made banded with very little loss, but probably not crucial here.
  • Could have "names" for states, which would be useful downstream for plotting, but not crucial.
  • calculation and access to the stationary distribution

The lectures which those apply to include:

Other notes
https://julia.quantecon.org/tools_and_techniques/finite_markov.html

  • All of the main onces
  • manually shows simulation with a categorical value switching between them each time.
  • check for irreducibility and get communication classes
  • check periodicity

https://julia.quantecon.org/multi_agent_models/lake_model.html

  • This also calculates the forward forecast distribution iterating forward as a discrete system, and could allow exploring ergodicity for absorbing states/etc.

https://julia.quantecon.org/multi_agent_models/aiyagari.html

  • Simulation
  • Also indirectly through the DiscreteDP

https://julia.quantecon.org/dynamic_programming/discrete_dp.html

  • Only indirectly through the DiscreteDP

Tauchen is used in:

Dynamic Programming Squared lectures, all just need simulation

Metadata

Metadata

Assignees

No one assigned

    Type

    No type

    Projects

    No projects

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions