Bayesian Hierarchical Models for Economic Forecasting (Richman Computational Economics) - Hardcover
Buch 6 von 29: Richman Computational Economics
Inhaltsangabe
Discover the power of Bayesian Hierarchical Models to transform your economic forecasting. This comprehensive guide demystifies Bayesian statistics and provides you with the tools to leverage these models for accurate and actionable economic insights. Whether you're a data scientist, economist, or researcher, elevate your analytical skills with practical Bayesian approaches.
Key Features:
- In-depth exploration of Bayesian statistical fundamentals.
- Step-by-step guidance on implementing Bayesian hierarchical models.
- Practical Python code examples for each chapter, facilitating hands-on learning.
- Advanced techniques for real-world economic forecasting challenges.
Book Description:
Unlock the full potential of Bayesian Hierarchical Models for economic forecasting. Begin with the foundational principles of Bayesian statistics and progress to sophisticated hierarchical models that capture complex economic structures. Dive deep into modeling techniques, including handling dynamic linear models and state space models, while mastering essential methods like Gibbs sampling and Metropolis-Hastings.
Gain practical skills through extensive Python code examples, designed to reinforce learning and ensure you can confidently apply these models to real-world data. With a focus on both theoretical understanding and practical application, this book equips you with the expertise to implement and adapt Bayesian methods in your forecasting efforts.
What You Will Learn:
- Understand Bayesian statistics essentials including prior, likelihood, and posterior distributions.
- Derive and apply Bayes' Theorem in economic modeling contexts.
- Select and formulate informative and non-informative prior distributions.
- Develop likelihood functions integral to Bayesian analysis.
- Construct posterior distributions by integrating prior knowledge with new data.
- Identify and utilize conjugate priors for popular probability distributions.
- Define and model hierarchical structures to enhance economic forecasting.
- Implement conditional independence within hierarchical models.
- Analyze multi-level models within a Bayesian framework.
- Formulate and interpret hierarchical priors specific to economic data.
- Derive posterior inference in complex hierarchical settings.
- Incorporate random effects into Bayesian hierarchical models.
- Differentiate between fixed and random effects in model construction.
- Employ quantitative methods for effective model selection and comparison.
- Master Gibbs sampling techniques tailored for hierarchical models.
- Utilize Metropolis-Hastings to tackle complex posterior distributions.
- Implement Markov Chain Monte Carlo (MCMC) methods for robust Bayesian inference.
- Utilize convergence diagnostics to ensure accuracy in MCMC algorithms.
- Apply empirical Bayes techniques to estimate parameters in hierarchical models.
- Formulate and estimate dynamic linear models using Bayesian techniques.
- Analyze state space models within a Bayesian context.
- Integrate Kalman Filter methods into Bayesian frameworks for time-series analysis.
- Develop robust Bayesian networks tailored for economic data analysis.
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