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The first step-by-step introduction to the methodology of agent-based models in economics, their mathematical and statistical analysis, and real-world applications.
Handbook of Computational Economics: Heterogeneous Agent Modeling, Volume Four, focuses on heterogeneous agent models, emphasizing recent advances in macroeconomics (including DSGE), finance, empirical validation and experiments, networks and related applications. Capturing the advances made since the publication of Volume Two (Tesfatsion & Judd, 2006), it provides high-level literature with sections devoted to Macroeconomics, Finance, Empirical Validation and Experiments, Networks, and other applications, including Innovation Diffusion in Heterogeneous Populations, Market Design and Electricity Markets, and a final section on Perspectives on Heterogeneity. - Helps readers fully understand the dynamic properties of realistically rendered economic systems - Emphasizes detailed specifications of structural conditions, institutional arrangements and behavioral dispositions - Provides broad assessments that can lead researchers to recognize new synergies and opportunities
The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.
A significant new edition of a text that offers both tools and sample applications; extensive revisions and seven new chapters improve and expand upon the original treatment.
Volume 32 of the NBER Macroeconomics Annual features six theoretical and empirical studies of important issues in contemporary macroeconomics, and a keynote address by former IMF chief economist Olivier Blanchard. In one study, SeHyoun Ahn, Greg Kaplan, Benjamin Moll, Thomas Winberry, and Christian Wolf examine the dynamics of consumption expenditures in non-representative-agent macroeconomic models. In another, John Cochrane asks which macro models most naturally explain the post-financial-crisis macroeconomic environment, which is characterized by the co-existence of low and nonvolatile inflation rates, near-zero short-term interest rates, and an explosion in monetary aggregates. Manuel Adelino, Antoinette Schoar, and Felipe Severino examine the causes of the lending boom that precipitated the recent U.S. financial crisis and Great Recession. Steven Durlauf and Ananth Seshadri investigate whether increases in income inequality cause lower levels of economic mobility and opportunity. Charles Manski explores the formation of expectations, considering the efficacy of directly measuring beliefs through surveys as an alternative to making the assumption of rational expectations. In the final research paper, Efraim Benmelech and Nittai Bergman analyze the sharp declines in debt issuance and the evaporation of market liquidity that coincide with most financial crises. Blanchard’s keynote address discusses which distortions are central to understanding short-run macroeconomic fluctuations.
How ideas in complexity can be used to develop more effective public policy Complexity science—made possible by modern analytical and computational advances—is changing the way we think about social systems and social theory. Unfortunately, economists' policy models have not kept up and are stuck in either a market fundamentalist or government control narrative. While these standard narratives are useful in some cases, they are damaging in others, directing thinking away from creative, innovative policy solutions. Complexity and the Art of Public Policy outlines a new, more flexible policy narrative, which envisions society as a complex evolving system that is uncontrollable but can be influenced. David Colander and Roland Kupers describe how economists and society became locked into the current policy framework, and lay out fresh alternatives for framing policy questions. Offering original solutions to stubborn problems, the complexity narrative builds on broader philosophical traditions, such as those in the work of John Stuart Mill, to suggest initiatives that the authors call "activist laissez-faire" policies. Colander and Kupers develop innovative bottom-up solutions that, through new institutional structures such as for-benefit corporations, channel individuals’ social instincts into solving societal problems, making profits a tool for change rather than a goal. They argue that a central role for government in this complexity framework is to foster an ecostructure within which diverse forms of social entrepreneurship can emerge and blossom.
This valuable book contributes substantively to the current state-of-the-art of macroeconomics. It provides a method for building models in which business cycles and economic growth emerge from the interactions of a large number of heterogeneous agents. Drawing from recent advances in agent-based computational modeling, the authors show how insights from dispersed fields can be fruitfully combined to improve our understanding of macroeconomic dynamics.
Recognising that the economy is a complex system with boundedly rational interacting agents, applies complexity modelling to economics and finance.