Table of contents
Notebooks on Time Series (subscribe via RSS)
The pages below document certain specific topics usually related to time series analysis in Python. Usually they are Jupyter Notebooks, rendered into HTML for this website.
Note: all notebooks are kept for historical context, and so as time has passed some have been superseded by others. Notes have been added to indicate where this has happened.
2020
 Large dynamic factor models, forecasting, and nowcasting
 TVPVAR, MCMC, and sparse simulation smoothing
 Chandrasekhar recursions in Statsmodels
2018
2017
 Implementing and estimating a simple Real Business Cycle (RBC) model
 Implementing and estimating a local level state space model
 Implementing and estimating an ARMA(1, 1) state space model
2016
2015
 Dynamic factors and coincident indices
 Bayesian state space estimation in Python via MetropolisHastings
 Estimating a Real Business Cycle DSGE Model by Maximum Likelihood in Python
 State space diagnostics
 Unobserved components
2014
 State space modeling in Python
 Implementing state space models for Statsmodels
 Kalman Filter Initialization  The Stationary Case
2013
 Bernoulli Trials in Python: Bayesian Estimation
 Bernoulli Trials in Python: Classical Estimation

Markovswitching  Filardo (1994) TimeVarying Transition Probabilities
(superseded by "Markov switching autoregression models")

MarkovSwitching  Kim, Nelson, and Startz (1998) Threestate Variance Switching
(superseded by "Markov switching autoregression models")

Markovswitching  Hamilton (1989) Markov Switching Model of GNP
(superseded by "Markov switching autoregression models")
 SETAR Model Functionality
 Developing with Python
Example notebooks for Statsmodels in 2021
Statsmodels is a Python library that contains many poweful statistical tools, but there aren't always enough examples of how and when to use it, and, worse, older examples can become outdated. This set of example notebooks is intended to showcase some of the ways that Statsmodels can be used for data science and econometrics.