Bayesian Econometrics & Time-Series Forecasting with Python: MCMC, Particle Filters, State-Space Models, and Hierarchical Forecasting for Economic and Financial Data

Reactive PublishingBayesian Econometrics & Time-Series Forecasting with Python is a modern, practitioner-focused guide to modeling uncertainty, learning from sparse or noisy economic data, and generating probabilistic forecasts for real-world decision-making. Built for readers operating at the intersection of data science, macroeconomics, and quantitative finance, this book provides a comprehensive blueprint for deploying Bayesian inference across the full forecasting stack.Through clear exposition and code-driven examples, readers learn how to build, tune, and interpret Bayesian models for economic and financial time series—ranging from state-space models and structural decompositions to hierarchical forecasting frameworks. The text bridges theory and implementation using Python, with workflows that scale from academic research to production settings.Topics include:• Bayesian regression, priors, and posterior inference• Markov Chain Monte Carlo (MCMC), Gibbs sampling, HMC, and NUTS• Sequential Monte Carlo and particle filtering for online updating• State-space models for trend, cycle, seasonality, and shock decomposition• Stochastic volatility models and latent factor processes• Hierarchical Bayesian forecasting for multi-level economic data• Bayesian VARs, DSGE-style structures, and dynamic factor models• Scenario analysis, uncertainty quantification, and density forecastsBy the end, readers will be able to design Bayesian forecasting systems that quantify uncertainty, learn from streaming data, incorporate hierarchical information, and ultimately produce forecasts aligned with how real economies behave—nonlinear, uncertain, and full of structural breaks.This book is ideal for economists, financial analysts, quants, data scientists, PhD students, and research-driven professionals seeking to advance beyond deterministic econometrics and into probabilistic, model-based forecasting using Bayesian methods and modern computational tools. Read more