Arbeitspapier

How good are out of sample forecasting Tests on DSGE models?

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are increasingly used to check a) the specification b) the forecasting capacity of these models. We carry out a Monte Carlo experiment on a widely-used DSGE model to investigate the power of these tests. We find that in specification testing they have weak power relative to an in-sample indirect inference test; this implies that a DSGE model may be badly mis-specified and still improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on the lefthand tail. By contrast a model that passes an indirect inference test of specification will almost definitely also improve on VAR forecasts.

Language
Englisch

Bibliographic citation
Series: Cardiff Economics Working Papers ; No. E2014/11

Classification
Wirtschaft
General Aggregative Models: General
General Aggregative Models: Forecasting and Simulation: Models and Applications
Subject
Out of sample forecasts
DSGE
VAR
specification tests
indirect inference
forecast performance
DSGE-Modell
Prognoseverfahren
Modellierung
Statistischer Test
VAR-Modell
Theorie

Event
Geistige Schöpfung
(who)
Minford, Patrick
Xu, Yongdeng
Zhou, Peng
Event
Veröffentlichung
(who)
Cardiff University, Cardiff Business School
(where)
Cardiff
(when)
2014

Handle
Last update
10.03.2025, 11:41 AM CET

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Object type

  • Arbeitspapier

Associated

  • Minford, Patrick
  • Xu, Yongdeng
  • Zhou, Peng
  • Cardiff University, Cardiff Business School

Time of origin

  • 2014

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