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#134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns image

#134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns

S1 E134 · Learning Bayesian Statistics
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Takeaways:

  • Setting appropriate priors is crucial to avoid overfitting in models.
  • R-squared can be used effectively in Bayesian frameworks for model evaluation.
  • Dynamic regression can incorporate time-varying coefficients to capture changing relationships.
  • Predictively consistent priors enhance model interpretability and performance.
  • Identifiability is a challenge in time series models.
  • State space models provide structure compared to Gaussian processes.
  • Priors influence the model's ability to explain variance.
  • Starting with simple models can reveal interesting dynamics.
  • Understanding the relationship between states and variance is key.
  • State-space models allow for dynamic analysis of time series data.
  • AI can enhance the process of prior elicitation in statistical models.

Chapters:

10:09 Understanding State Space Models

14:53 Predictively Consistent Priors

20:02 Dynamic Regression and AR Models

25:08 Inflation Forecasting

50:49 Understanding Time Series Data and Economic Analysis

57:04 Exploring Dynamic Regression Models

01:05:52 The Role of Priors

01:15:36 Future Trends in Probabilistic Programming

01:20:05 Innovations in Bayesian Model Selection

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