scholarly journals Computing DSGE Models with Recursive Preferences and Stochastic Volatility

2012 ◽  
Author(s):  
Dario Caldara ◽  
Jesús Fernández-Villaverde ◽  
Juan Francisco Rubio-Ramirez ◽  
Wen Yao
Keyword(s):  
Stochastic Volatility ◽  
Dsge Models ◽  
Recursive Preferences

scholarly journals Computing DSGE Models with Recursive Preferences and Stochastic Volatility

2012 ◽  
Vol 2012 (04) ◽  
pp. 1-43
Author(s):  
Dario Caldara ◽  
◽  
Jesús Fernández-Villaverde ◽  
Juan Francisco Rubio-Ramírez ◽  
Yao Wen
Keyword(s):  
Stochastic Volatility ◽  
Dsge Models ◽  
Recursive Preferences

scholarly journals Computing DSGE models with recursive preferences and stochastic volatility

2012 ◽  
Vol 15 (2) ◽  
pp. 188-206 ◽  
Author(s):  
Dario Caldara ◽  
Jesús Fernández-Villaverde ◽  
Juan F. Rubio-Ramírez ◽  
Wen Yao
Keyword(s):  
Stochastic Volatility ◽  
Dsge Models ◽  
Recursive Preferences

Implementing Stochastic Volatility in DSGE Models: A Comment

2017 ◽  
Author(s):  
Lorenzo Bretscher ◽  
Alex C. Hsu ◽  
Andrea Tamoni
Keyword(s):  
Stochastic Volatility ◽  
Dsge Models

scholarly journals Computing DSGE Models with Recursive Preferences

2009 ◽  
Author(s):  
Dario Caldara ◽  
Jesús Fernández-Villaverde ◽  
Juan Rubio-Ramírez ◽  
Wen Yao
Keyword(s):  
Dsge Models ◽  
Recursive Preferences

scholarly journals Real-time forecast evaluation of DSGE models with stochastic volatility

2017 ◽  
Vol 201 (2) ◽  
pp. 322-332 ◽  
Author(s):  
Francis X. Diebold ◽  
Frank Schorfheide ◽  
Minchul Shin
Keyword(s):  
Real Time ◽  
Stochastic Volatility ◽  
Forecast Evaluation ◽  
Dsge Models

IMPLEMENTING STOCHASTIC VOLATILITY IN DSGE MODELS: A COMMENT

2018 ◽  
Vol 24 (4) ◽  
pp. 935-950
Author(s):  
Lorenzo Bretscher ◽  
Alex Hsu ◽  
Andrea Tamoni
Keyword(s):  
Numerical Simulation ◽  
Stochastic Volatility ◽  
State Vector ◽  
Approximation Error ◽  
Linear Representation ◽  
Dsge Models ◽  
State Variable ◽  
Implementation Approach ◽  
Innovation Dynamics ◽  
Nonlinear State

We highlight a state variable misspecification with one accepted method to implement stochastic volatility (SV) in DSGE models when transforming the nonlinear state-innovation dynamics to its linear representation. Although the technique is more efficient numerically, we show that it is not exact but only serves as an approximation when the magnitude of SV is small. Not accounting for this approximation error may induce substantial spurious volatility in macroeconomic series, which could lead to incorrect inference about the performance of the model. We also show that, by simply lagging and expanding the state vector, one can obtain the correct state-space specification. Finally, we validate our augmented implementation approach against an established alternative through numerical simulation.

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