Solving Nonlinear Stochastic Growth Models

Solving Nonlinear Stochastic Growth Models PDF Author: John B. Taylor (Professor of Economics)
Publisher:
ISBN:
Category :
Languages : en
Pages : 22

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Book Description

Solving Nonlinear Stochastic Growth Models

Solving Nonlinear Stochastic Growth Models PDF Author: John B. Taylor (Professor of Economics)
Publisher:
ISBN:
Category :
Languages : en
Pages : 22

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Book Description


Solving Nonlinear Stochastic Growth Models

Solving Nonlinear Stochastic Growth Models PDF Author: John B. Taylor
Publisher:
ISBN:
Category : Rational expectations (Economic theory)
Languages : en
Pages : 68

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Book Description
The purpose of this paper is to report on a comparison of several alternative numerical solution techniques for nonlinear rational expectations models. The comparison was made by asking individual researchers to apply their different solution techniques to a simple representative agent, optimal, stochastic growth model. Decision rules as well as simulated time series are compared. The differences among the methods turned out to be quite substantial for certain aspects of the growth model. Therefore, researchers might want to be careful not to rely blindly on the results of any chosen numerical solution method in applied work.

Solving nonlinear stochastic growth models

Solving nonlinear stochastic growth models PDF Author: John B. Taylor
Publisher:
ISBN:
Category :
Languages : es
Pages : 22

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Book Description


Solving Nonlinear Stochastic Growth Models

Solving Nonlinear Stochastic Growth Models PDF Author: David Edward Robinson
Publisher:
ISBN:
Category :
Languages : en
Pages : 212

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Book Description


Solving Nonlinear Stochastic Growth Models

Solving Nonlinear Stochastic Growth Models PDF Author: John B. Taylor
Publisher:
ISBN:
Category :
Languages : en
Pages : 47

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Book Description
The purpose of this paper is to report on a comparison of several alternative numerical solution techniques for nonlinear rational expectations models. The comparison was made by asking individual researchers to apply their different solution techniques to a simple representative agent, optimal, stochastic growth model. Decision rules as well as simulated time series are compared. The differences among the methods turned out to be quite substantial for certain aspects of the growth model. Therefore, researchers might want to be careful not to rely blindly on the results of any chosen numerical solution method in applied work.

Macroeconometrics

Macroeconometrics PDF Author: Kevin D. Hoover
Publisher: Springer Science & Business Media
ISBN: 940110669X
Category : Business & Economics
Languages : en
Pages : 575

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Book Description
Each chapter of Macroeconometrics is written by respected econometricians in order to provide useful information and perspectives for those who wish to apply econometrics in macroeconomics. The chapters are all written with clear methodological perspectives, making the virtues and limitations of particular econometric approaches accessible to a general readership familiar with applied macroeconomics. The real tensions in macroeconometrics are revealed by the critical comments from different econometricians, having an alternative perspective, which follow each chapter.

Economic Growth

Economic Growth PDF Author: Alfonso Novales
Publisher: Springer Nature
ISBN: 3662639823
Category : Business & Economics
Languages : en
Pages : 664

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Book Description
This is the third corrected and extended edition of a book on deterministic and stochastic Growth Theory and the computational methods needed to produce numerical solutions. Exogenous and endogenous growth, non-monetary and monetary models are thoroughly reviewed. Special attention is paid to the use of these models for fiscal and monetary policy analysis. Models under modern theories of the Business Cycle, New Keynesian Macroeconomics, and Dynamic Stochastic General Equilibrium models, can be all considered as special cases of economic growth models, and they can be analyzed by the theoretical and numerical procedures provided in the textbook. Analytical discussions are presented in full detail. The book is self-contained and it is designed so that the student advances in the theoretical and the computational issues in parallel. Spreadsheets are used to solve simple examples. Matlab files are provided on an accompanying website to illustrate theoretical results from all chapters as well as to simulate the effects of economic policy interventions. The logical structure of these program files is described in "Numerical exercise"-type of sections, where the output of these programs is also interpreted. The third edition corrects a few typographical errors, includes two new and original chapters on frequentist and Bayesian estimation, and improves some notation.

Nonlinear Stochastic Operator Equations

Nonlinear Stochastic Operator Equations PDF Author: George Adomian
Publisher: Academic Press
ISBN: 1483259099
Category : Science
Languages : en
Pages : 304

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Book Description
Nonlinear Stochastic Operator Equations deals with realistic solutions of the nonlinear stochastic equations arising from the modeling of frontier problems in many fields of science. This book also discusses a wide class of equations to provide modeling of problems concerning physics, engineering, operations research, systems analysis, biology, medicine. This text discusses operator equations and the decomposition method. This book also explains the limitations, restrictions and assumptions made in differential equations involving stochastic process coefficients (the stochastic operator case), which yield results very different from the needs of the actual physical problem. Real-world application of mathematics to actual physical problems, requires making a reasonable model that is both realistic and solvable. The decomposition approach or model is an approximation method to solve a wide range of problems. This book explains an inherent feature of real systems—known as nonlinear behavior—that occurs frequently in nuclear reactors, in physiological systems, or in cellular growth. This text also discusses stochastic operator equations with linear boundary conditions. This book is intended for students with a mathematics background, particularly senior undergraduate and graduate students of advanced mathematics, of the physical or engineering sciences.

Handbook of Computable General Equilibrium Modeling

Handbook of Computable General Equilibrium Modeling PDF Author: Peter B. Dixon
Publisher: Newnes
ISBN: 044462631X
Category : Business & Economics
Languages : en
Pages : 1886

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Book Description
Top scholars synthesize and analyze scholarship on this widely used tool of policy analysis in 27 articles, setting forth its accomplishments, difficulties, and means of implementation. Though CGE modeling does not play a prominent role in top U.S. graduate schools, it is employed universally in the development of economic policy. This collection is particularly important because it presents a history of modeling applications and examines competing points of view. Presents coherent summaries of CGE theories that inform major model types Covers the construction of CGE databases, model solving, and computer-assisted interpretation of results Shows how CGE modeling has made a contribution to economic policy

Computational Methods for the Study of Dynamic Economies

Computational Methods for the Study of Dynamic Economies PDF Author: Ramon Marimon
Publisher: OUP Oxford
ISBN: 0191522392
Category : Business & Economics
Languages : en
Pages : 298

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Book Description
Macroeconomics increasingly uses stochastic dynamic general equilibrium models to understand theoretical and policy issues. Unless very strong assumptions are made, understanding the properties of particular models requires solving the model using a computer. This volume brings together leading contributors in the field who explain in detail how to implement the computational techniques needed to solve dynamic economics models. A broad spread of techniques are covered, and their application in a wide range of subjects discussed. The book provides the basics of a toolkit which researchers and graduate students can use to solve and analyse their own theoretical models.