How to use nonlinear dynamic models in policy analysis. 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.
How to use nonlinear dynamic models in policy analysis. 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.
How to use nonlinear dynamic models in policy analysis. 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.
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.