Stochastic Recursive Algorithms for Optimization

Macroeconomics offers a modern and comprehensive introduction to the tools and methods that define contemporary graduate training in the field. Written for first-year PhD students and advanced MA/MS students, the book provides a rigorous yet accessible foundation in dynamic optimization, recursive methods, competitive equilibrium, stochastic modelling, and welfare analysis. It emphasizes clarity and intuition, guiding readers through the logic of intertemporal decision-making that underlies modern macro theory. A key feature of the text is its explicit connection to data. Throughout the book, theory is used to interpret empirical patterns, illustrating how macroeconomic models help us understand real-world behavior and policy-relevant facts. Quantitative and computational tools are integrated throughout to show how models are taken to the data. The book develops frameworks with heterogeneity and frictions, including incomplete markets, borrowing constraints, labor-market search frictio