Name: Finance and Economics Discussion Series: Computing Dsge Models with Recursive Preferences and Stochastic Volatility (en Inglés)
Price: 95.80 PEN
Availability: InStock
Author: United States Federal Reserve Board ; Et Al ; Caldara, Dario
ISBN: 9781288697380

portada Finance and Economics Discussion Series: Computing Dsge Models with Recursive Preferences and Stochastic Volatility (en Inglés)

Formato

Libro Físico

Autor

United States Federal Reserve Board

Dario Caldara

Editorial

Bibliogov

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

24.6 x 18.9 x 0.3 cm

Peso

0.10 kg.

ISBN13

9781288697380

Categorías

Política y gobierno

Finance and Economics Discussion Series: Computing Dsge Models with Recursive Preferences and Stochastic Volatility (en Inglés)

United States Federal Reserve Board (Autor) · Dario Caldara (Autor) · Bibliogov · Tapa Blanda

Libro Nuevo

S/ 95,80

S/ 191,59

Ahorras: S/ 95,80

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 29 de Julio y el Miércoles 07 de Agosto.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Finance and Economics Discussion Series: Computing Dsge Models with Recursive Preferences and Stochastic Volatility (en Inglés)"

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991) and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.