Computational Macroeconomics for the Open Economy
Policymakers need quantitative as well as qualitative answers to pressing policy questions. Because of advances in computational methods, quantitative estimates are now derived from coherent nonlinear dynamic macroeconomic models embodying measures of risk and calibrated to capture specific characteristics of real-world situations. This text shows how such models can be made accessible and operational for confronting policy issues. The book starts with a simple setting based on market-clearing price flexibility. It gradually incorporates departures from the simple competitive framework in the form of price and wage stickiness, taxes, rigidities in investment, financial frictions, and habit persistence in consumption. Most chapters end with computational exercises; the Matlab code for the base model can be found in the appendix. As the models evolve, readers are encouraged to modify the codes from the first simple model to more complex extensions. Computational Macroeconomics for the Open Economy can be used by graduate students in economics and finance as well as policy-oriented researchers.
About the Authors
G. C. Lim is Professorial Research Fellow at the Melbourne Institute of Applied Economic and Social Research, University of Melbourne. She is the coauthor of Dynamic Economic Models in Discrete Time: Theory and Empirical Applications and An Introduction to Dynamic Economic Models (both with Brian Ferguson).
Paul D. McNelis is Robert Bendheim Chair of Economic and Financial Policy at Fordham University Graduate School of Business Administration. He is the author of Neural Networks in Finance: Gaining Predictive Edge in the Market.
—Jesús Fernández-Villaverde, Department of Economics, University of Pennsylvania
—Timothy J. Kehoe, Department of Economics, University of Minnesota