Keynesian Behavioral Macroeconomics

Keynesian Behavioral Macroeconomics analyzes Keynes's landmark contributions in behavioral economics and develops a new and fresh genre of macroeconomic analysis. It compels us to consider seriously the earlier-generation warnings about the impact of investors' animal spirits and financiers' liquidity-presence using cognitive-based and social psychology heuristics. Innovative in its subject matter, approach, theoretical development and policy prescriptions, this constructivist pluralist approach can contribute to important debates. This fresh look in macroeconomics can fruitfully be applied by macroeconomists, policymakers, and market participants to prevent effective demand shortages, stimulate the economy, preserve job creation, and impact redistribution, sustainability and social inclusion.In this timely book, Theodore Koutsobinas develops a synthesis of Keynes's macroeconomic theory with contemporary developments in behavioral macroeconomics to analyze urgent real-world challenges, and he proposes successful solutions for macroeconomic policy. This volume uniquely explains how Keynes's magnificent, crucial, but long-forgotten dynamics can be analyzed on the basis of behavioral foundations to explain amplified global finance cycles, booms and busts, and macroeconomic instability with harmful effects on incomes and jobs. - Demonstrates the dynamics of asset yields in financial markets for global macroeconomic modeling in a standard model by re-generating Keynes's liquidity-preference theory as a liquidity-premium approach in a financial portfolio and macroeconomic equilibrium framework - Showcases for the first time the implications of radical Keynesian uncertainty through attribute-heuristic substitution and intuitive judgment - Proposes and utilizes highly relevant cognitive-based heuristics and attributes in cognitive and social psychology as agents' behavioral expectations in macroeconomic modeling for better diagnosis and forecasting in difficult macroeconomic situations, compared to only probability-based techniques