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

Learning Bayesian Statistics

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Exploring State Space Models in Time Series Analysis

This chapter provides an in-depth discussion of state space modeling and its application in time series analysis, focusing on autoregressive components and their role in enhancing model performance. The speakers highlight the importance of incorporating covariates and structural elements to effectively capture trends and interactions between multiple correlated series. Additionally, they explore advanced concepts such as impulse response functions and the integration of dynamic regression components, illustrating their significance in forecasting and causal analysis.

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