Regime switching models

springcoil · April 7, 2018, 1:02pm

I’ve not had time to delve into this yet - so putting this here.

Has anyone built a model like this?

junpenglao · April 7, 2018, 2:26pm

It might be a bit challenging to rewrite the autoregressive model using tt.scan, but overall it is possible - would be great if somebody pick it up, it can be a nice exercise/example.

junpenglao · April 8, 2018, 9:01am

I gave it a stab:

github.com

junpenglao/Planet_Sakaar_Data_Science/blob/master/Ports/Regime-switching models in PyMC3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://modernstatisticalworkflow.blogspot.ch/2018/02/regime-switching-models-in-stan.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"

This file has been truncated. show original

Comments welcome

ffgg · September 15, 2020, 1:25pm

I find this topic very interesting, was wondering if anybody has done any more work since 2018?

Working with regime shifts and tipping points in environmental science, having a Bayesian model to approach these issues would be invaluable.

However, unfortunately I am really not an expert in Pymc (as most environmental scientists I am sure), so it would be very useful to have a simplified version.

any comments welcome, thanks!

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Regime switching models

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