This chapter introduces the scope and motivation of macroeconomics by tracing key historical developments and intellectual shifts in the field. It begins with major macroeconomic events—including the Great Depression, the stagflation of the 1970s, and the Great Recession—and discusses how each episode shaped the evolution of models, empirical methods, and policy thinking. It emphasizes the importance of long-run growth, business cycle fluctuations, and macroeconomic inequality. The chapter highlights the emergence of dynamic stochastic models, the role of expectations, and the use of empirical methods in shaping modern research. It also touches on macroeconomic dimensions of fiscal policy, international economics, and the environment, setting the stage for the topics covered in the second part of the book. The final sections reflect on where the discipline stands today and the challenges that lie ahead. The goal is to provide readers with an overarching perspective on what macroeconomics studies, why it matters, and how it is approached in practice.

This chapter introduces the core conceptual and empirical framework used throughout the book to study macroeconomic aggregates. It begins with a data-driven exploration of output, labor, and capital, emphasizing long-run trends in growth and input shares. The chapter presents a neoclassical interpretation of macroeconomic dynamics, laying the groundwork for formal modeling in later chapters. It discusses the role of productivity in accounting for output growth, introduces growth accounting techniques, and highlights the dynamic system underlying capital accumulation and labor supply decisions. The chapter also outlines how individual saving and labor choices aggregate to macro-level outcomes, bridging microeconomic behavior with macroeconomic phenomena. In its concluding section, the chapter offers a roadmap for how the analytical tools developed in Part I will be applied across subsequent chapters to address a wide range of macroeconomic questions.

This chapter formalizes the concept of dynamic competitive equilibrium in macroeconomics. It introduces several equilibrium definitions—Arrow-Debreu, sequential, and recursive—and demonstrates their application in both endowment and production economies. The neoclassical growth model is revisited under each equilibrium concept to highlight differences in information structure, timing, and tractability. The chapter also examines steady-state and transitional dynamics in recursive equilibrium, providing a link to the methods developed in Chapter 4. A final section introduces overlapping generations (OLG) models, comparing their equilibrium properties with those of representative-agent models and illustrating intergenerational trade. Throughout, the emphasis is on the internal consistency and micro foundations of equilibrium allocations and prices. These concepts form the analytical backbone for studying welfare, policy, and aggregate dynamics in the rest of the book.

This chapter examines the concept of welfare in dynamic macroeconomic environments. It begins with the First Welfare Theorem and explores conditions under which competitive equilibria are Pareto efficient. The analysis then turns to tracing out the Pareto frontier and characterizing efficient allocations across individuals and time. The chapter identifies various sources of inefficiency—including taxes, externalities, borrowing constraints, lack of commitment, and market power—and develops frameworks to quantify associated welfare losses. It also considers welfare analysis in overlapping generations models, where dynamic inefficiencies may arise. The tools presented here provide a foundation for evaluating real-world policies and for designing macroeconomic models that account for social welfare.

This chapter introduces empirical methods used to connect macroeconomic models with data. It begins by outlining the identification challenges inherent in macroeconomic analysis and discusses strategies to overcome them using natural experiments, structural vector autoregressions (SVARs), and local projection methods. The chapter then presents a structural model of fiscal policy and illustrates how to use impulse responses to estimate fiscal multipliers. It also covers the use of structural estimation and calibration to discipline macroeconomic models, highlighting the strengths and limitations of each approach. Emphasis is placed on the role of quantitative models in interpreting empirical patterns and in evaluating counterfactual scenarios. The chapter concludes by assessing how different empirical strategies complement each other in informing macroeconomic theory and policy. Keywords: empirical methods, identification, SVAR, local projections, fiscal policy, structural estimation, calibration, quantitative macroeconomics.

This chapter introduces continuous-time methods that are widely used in modern macroeconomic theory. It begins by establishing the basic tools and notation of continuous-time analysis and then presents the maximum principle as the core optimization technique. The chapter applies these tools to standard macroeconomic models, including the Solow and neoclassical growth models, reformulated in continuous time. Phase diagrams are used to visualize the dynamics of these systems and to analyze stability and transitional paths. The chapter also introduces settings with uncertainty using Poisson processes, providing a foundation for modeling stochastic events in continuous-time frameworks. By comparing continuous- and discrete-time formulations, the chapter helps clarify the trade-offs between tractability and realism. This is particularly relevant for Chapter 13.

This chapter presents numerical methods essential for solving macroeconomic models that lack closed-form solutions. It covers a range of computational tools, beginning with function approximation techniques such as interpolation and projection. Methods for root finding and optimization—including bisection, Newton-Raphson, and golden-section search—are introduced and linked to their applications in solving equilibrium conditions. The chapter also explains how to discretize stochastic processes using the Tauchen and Rouwenhorst methods. Value function iteration is presented as a method for solving dynamic programming problems, both in deterministic and stochastic settings. Linearization techniques are discussed as an alternative approach, including the use of the Blanchard-Kahn conditions for assessing solution stability. These tools are foundational for implementing and simulating modern macroeconomic models, particularly in environments with uncertainty, heterogeneity, or nonlinearity.  Computation is an essential part of modern macroeconomic research, and this part is especially relevant to Chapter 15.

There has been steady income growth in advanced economies since the Industrial Revolution and there are now large differences in average income across countries. These facts have huge importance to human welfare, what explains them? A first set of explanations hinges on the accumulation of more materials and tools and working harder. The chapter explains that these forces can only explain a small part of the facts. The accumulation of knowledge is key and the chapter presents the standard framework for modeling investments in knowledge.

This chapter develops the real business cycle (RBC) approach to studying short-run macroeconomic fluctuations. It begins with a historical overview of business cycle research and presents stylized empirical facts, including cyclical behavior of output, consumption, investment, and labor. The chapter introduces common data-filtering techniques and conditional moments used in evaluating models. It then formulates the core RBC model, rooted in the neoclassical growth framework with stochastic productivity shocks, and demonstrates its calibration and quantitative implications. Extensions to the baseline model—including indivisible labor, capital adjustment costs, and variable capacity utilization—are explored to address specific empirical shortcomings. The chapter also introduces the concept of business cycle accounting and discusses frontier areas of current research. The RBC model serves as a benchmark for evaluating other macroeconomic models that incorporate frictions, policy, or nominal rigidity.

This chapter examines the role of money in macroeconomic models. It begins with overlapping generations frameworks in which fiat money can facilitate intertemporal trade and improve welfare. Extensions to production economies are introduced to study the interaction between money, growth, and fiscal policy. The analysis then turns to dynastic models, showing conditions under which fiat money has value and how liquidity services can sustain monetary equilibria. Policy analysis highlights the Friedman rule as a benchmark for optimal monetary policy and discusses departures from it in more general settings. The chapter also explores environments with multiple currencies and exchange rate indeterminacy, illustrating how monetary arrangements affect equilibrium uniqueness. It considers the possibility of equilibrium indeterminacy under Taylor-type policy rules and introduces the fiscal theory of the price level as an alternative perspective on price determination. Finally, the chapter examines models in which money serves as a medium of exchange, providing a unified framework for understanding the role of money in the macroeconomy.

This chapter studies how nominal rigidities shape the transmission of shocks and the conduct of monetary policy. It begins by reviewing microeconomic and aggregate evidence on price rigidity and then introduces the New Keynesian model as a framework for analyzing monetary non-neutralities. Policy analysis covers objectives such as inflation stabilization and output smoothing, highlighting the “divine coincidence” that arises under certain conditions. Alternative strategies—including inflation versus price-level targeting, and the role of expectations, commitment, and time consistency—are also explored. The chapter examines the macroeconomic effects of monetary policy shocks, using empirical evidence to assess the degree of nominal rigidity. Extensions to the basic framework include sticky wages and additional nominal frictions that amplify or alter policy trade-offs. By linking theory to empirical patterns, the chapter shows how departures from price flexibility provide a rationale for active stabilization policy and frame debates on optimal monetary design.

This chapter analyzes the role of inequality in macroeconomics. It begins by presenting empirical evidence on income, wealth, and wage distributions, and documents long-run and cross-country patterns. The theoretical analysis explores how macroeconomic models account for inequality, covering the labor share, the capital–output ratio, wage dispersion, and wealth concentration. The chapter then examines why inequality matters for aggregate outcomes, focusing on its effects on long-run growth, business cycle dynamics, and aggregate demand. It highlights mechanisms through which heterogeneity at the micro level translates into macroeconomic implications, such as credit constraints, incomplete markets, and heterogeneous-agent general equilibrium models. By integrating theory with data, the chapter emphasizes both the measurement and the consequences of inequality, setting the stage for later work on heterogeneity across households and firms..

This chapter examines the implications of firm heterogeneity for macroeconomic performance. It begins by presenting empirical evidence on the distribution of firm size, productivity, and growth. A simple model illustrates how differences across firms affect aggregate outcomes through reallocation, and how frictions can lead to misallocation of input factors across producers. The analysis then embeds heterogeneous firms into general equilibrium, allowing for evaluation of policies such as firing taxes and entry barriers. Alternative market structures—including monopolistic competition and oligopoly with endogenous markups—are introduced to study how competition shapes outcomes in the presence of heterogeneity. The chapter also considers the macroeconomic consequences of firm size distributions and economic granularity, showing how shocks to large firms can have aggregate effects. Finally, it explores models in which productivity is endogenously determined by firm behavior, innovation, and market selection. By linking micro-level firm dynamics to aggregate efficiency and volatility, the chapter highlights the central role of heterogeneity in shaping growth and business cycles.

This chapter examines the interaction between the economy and the environment, focusing on the challenge of sustainability. It begins by presenting the natural-science foundations of climate change, including the climate system, the carbon cycle, and the carbon budget. The economic consequences of greenhouse gas emissions are analyzed through damages to output, welfare, and long-run growth. An “Integrated assessment model” is introduced to connect climate dynamics with economic decision-making, starting from static formulations and extending to fully dynamic frameworks. The chapter also addresses the role of natural resource depletion, presenting data on finite supplies of fossil fuels and metals, and developing models of optimal extraction. Complementarities between capital and energy, as well as the role of technical change, are explored in shaping the transition to sustainable growth. By combining theory, data, and policy analysis, the chapter illustrates how macroeconomics can contribute to understanding climate change and the management of natural resources.