Numerical Methods in EconomicsMIT Press, 4. 4. 2023 - Počet stran: 656 To harness the full power of computer technology, economists need to use a broad range of mathematical techniques. In this book, Kenneth Judd presents techniques from the numerical analysis and applied mathematics literatures and shows how to use them in economic analyses. The book is divided into five parts. Part I provides a general introduction. Part II presents basics from numerical analysis on R^n, including linear equations, iterative methods, optimization, nonlinear equations, approximation methods, numerical integration and differentiation, and Monte Carlo methods. Part III covers methods for dynamic problems, including finite difference methods, projection methods, and numerical dynamic programming. Part IV covers perturbation and asymptotic solution methods. Finally, Part V covers applications to dynamic equilibrium analysis, including solution methods for perfect foresight models and rational expectation models. A website contains supplementary material including programs and answers to exercises. |
Obsah
4 | 35 |
Exercises | 50 |
Approximation Methods | 197 |
Numerical Integration and Differentiation | 251 |
Monte Carlo and Simulation Methods | 285 |
QuasiMonte Carlo Methods | 310 |
15 | 346 |
20 | 361 |
Regular Perturbations of Simple Systems | 447 |
26 | 479 |
Regular Perturbations in Multidimensional Systems | 487 |
Advanced Asymptotic Methods | 511 |
Solution Methods for Perfect Foresight Models | 537 |
Solving Rational Expectations Models | 573 |
29 | 618 |
| 624 | |
Další vydání - Zobrazit všechny
Běžně se vyskytující výrazy a sousloví
algorithm applied approach asset assume asymptotic basic bilinear interpolation capital stock change of variables chapter Chebyshev polynomials choice choose coefficients collocation compute concave construct consumption convergence defined denote derivatives deterministic differential equation dimension discrete discussed dynamic programming economic eigenvalues equidistributed sequences equilibrium error evaluate example expansion finite number first-order conditions fixed-point iteration function iteration Gauss-Hermite Gaussian gradient Hessian homotopy ideas implicit implies initial guess integral integrand interpolation least squares approximation matrix Monte Carlo methods multidimensional Newton-Cotes Newton-Cotes formulas Newton's method nodes nonlinear equation numerical analysis optimal policy orthogonal polynomials parameter parameterization perturbation methods points policy function procedure projection methods quadratic quasi-Monte Carlo methods random variable recursive reverse shooting rule sampling scheme simple solution solve spline steady step stochastic Suppose Table Taylor series theorem theory tion utility function value function vector weighting function zero