Learning Bayesian Statistics

#124 State Space Models & Structural Time Series, with Jesse Grabowski

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Jan 22, 2025
Jesse Grabowski, PhD candidate at Paris 1 Pantheon-Sorbonne and principal data scientist at PyMC Labs, dives into the intricate world of state space models in time series analysis. He discusses the powerful adaptability of Bayesian methods in econometrics, emphasizing how they enhance forecasting accuracy. Grabowski highlights the balance between model complexity and simplicity, the significance of understanding trends, and the practical applications of innovations and latent states. Plus, he unwraps the role of the Kalman filter in managing dynamic data.
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ANECDOTE

Jesse's Bayesian Journey

  • Jesse Grabowski's Bayesian journey started unconventionally, given economics' resistance to Bayesian methods.
  • His diverse background includes farming, teaching, and finance, eventually leading him to discover Bayes through PyMC.
INSIGHT

Power of the Posterior

  • Bayesian inference's strength lies in its single, robust estimator: the posterior.
  • This posterior handles any functional form, offering uncertainty quantification and a focus on causality.
ADVICE

Prior Setting Tip

  • Use pm.find_constraint_prior for easier prior setting.
  • Specify desired distribution properties instead of manually adjusting parameters, simplifying model development.
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