How can economists make sense of complex and noisy macroeconomic data and turn it into credible evidence for research, forecasting, and policy?

Macroeconometrics provides a rigorous yet accessible guide to the tools used to analyse dynamic economic systems in a rapidly evolving empirical environment.

The book takes readers from the foundations of univariate time series analysis to the multivariate and structural methods that define modern macroeconometrics. It covers core topics such as stationarity, unit roots, cointegration, ARIMA and GARCH models, VARs, local projections, shock identification, Bayesian methods, and DSGE models, while also introducing recent advances in high-dimensional data, machine learning, nonlinearities, mixed-frequency analysis, quantile methods, Growth-at-Risk, and multi-country policy modelling. Structured as a progressive learning journey, it combines theoretical explanation with practical guidance, empirical applications, summary sections, key equations, exercises, and companion code.

Merging analytical rigor with real-world relevance, Macroeconometrics is an essential resource for advanced students, PhD researchers, and policy economists seeking a clear, modern, and comprehensive guide to macroeconomic data analysis.