scholarly journals Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility

2015 ◽  
Author(s):  
Francis X. Diebold ◽  
Frank Schorfheide ◽  
Minchul Shin
Keyword(s):  
Real Time ◽  
Stochastic Volatility ◽  
Forecast Evaluation ◽  
Dsge Models

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

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

scholarly journals Survey Expectations and Learning

2021 ◽  
pp. 3-27
Author(s):  
Sergey Slobodyan ◽  
◽  
Raf Wouters ◽  
◽  
Keyword(s):  
Real Time ◽  
Time Variation ◽  
Autoregressive Model ◽  
Dsge Models ◽  
Inflation Expectations ◽  
Time Varying ◽  
Time Data ◽  
Varying Coefficients ◽  
Real Time Data ◽  
Time Varying Coefficients

In this paper, we evaluate a model that describes real-time inflation data together with the inflation expectations measured by the Survey of Professional Forecasters (SPF). We work with a second-order autoregressive model in which the agents learn over time the intercept and persistence coefficients based on real-time data. To model the process of revisions in real time data, we allow for news and noise disturbances. In contrast to the usual time-varying parameter vector autoregression, we use non-linear Kalman filter techniques to estimate the time-varying coefficients of the underlying inflation process. We identify systematic changes in the persistence of the inflation process and in the long-run expected inflation rate that are implied by the model. The inflation forecasts implied by the model are then compared with the SPF forecasts. As we cannot reject the hypothesis that the SPF forecasts are produced based on our model, we re-estimate the model using Survey nowcasts and forecasts as additional observables. This augmented model does not change the nature and magnitude of the time variation in the coefficients of the autoregressive model, but it does help to reduce the uncertainty in the estimates. Overall, the estimated time-variation confirms our results on the perceived inflation process present in estimated DSGE models with learning (Slobodyan and Wouters, 2012a, 2012b).

Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise

2009 ◽  
Vol 28 (7) ◽  
pp. 595-611 ◽  
Author(s):  
G. Rünstler ◽  
K. Barhoumi ◽  
S. Benk ◽  
R. Cristadoro ◽  
A. Den Reijer ◽  
...  
Keyword(s):  
Real Time ◽  
Large Datasets ◽  
Forecast Evaluation ◽  
Short Term ◽  
Evaluation Exercise ◽  
Short Term Forecasting

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.

Real time estimation of stochastic volatility processes

2011 ◽  
Vol 200 (1) ◽  
pp. 223-246 ◽  
Author(s):  
László Gerencsér ◽  
Zsanett Orlovits
Keyword(s):  
Real Time ◽  
Stochastic Volatility ◽  
Time Estimation ◽  
Real Time Estimation

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
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