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Bayesian Estimation and Forecasting of Time Series in statsmodels

Chad Fulton
Federal Reserve Board of Governors


Statsmodels, a Python library for statistical and econometric analysis, has traditionally focused on frequentist inference, including in its models for time series data. This paper introduces the powerful features for Bayesian inference of time series models that exist in statsmodels, with applications to model fitting, forecasting, time series decomposition, data simulation, and impulse response functions.


time series, forecasting, bayesian inference, Markov chain Monte Carlo, statsmodels



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