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

Learning Bayesian Statistics

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Navigating R-squared in Bayesian Time Series

This chapter explores the complexities of model fitting in time series analysis, particularly focusing on the role of R-squared in autoregressive models within a Bayesian framework. The speakers discuss the importance of careful prior selection to mitigate overfitting risks and emphasize the significance of variance decomposition to understand model contributions. They also highlight practical implementations using PyMC and the necessity of validating model assumptions through prior predictive checks.

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