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Virginia Tech

Confronting Theory with Data: the Case of DSGE Modeling

Abstract

dc:description.abstract

The primary objective of this is to confront the DSGE model (Ireland, 2011) with data in an attempt to evaluate its empirical adequacy. The perspective used for this evaluation is based on unveiling the statistical model (structural VAR) behind the DSGE model, with a view to test its probabilistic assumptions vis-a-vis the data. It is shown that the implicit statistical model is seriously misspecified and the information from mis-specification (M-S) testing is then used to respecify the original structural VAR in an attempt to achieve statistical adequacy. The latter provides a precondition for the reliability of any inference based on the statistical model. Once the statistical adequacy of the respecified model is secured through thorough M-S testing, inferences like the likelihood-ratio test for the overidentifying restrictions, forecasting, impulse response analysis are applied to the original DSGE model to evaluate its empirical adequacy. At the end, the same inferential procedure is applied to the CAPM model.

Degree

thesis:*
Name thesis:degree_name
Ph. D.
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Economics
Department dc:contributor.department
Economics, Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2012

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Poudyal, Niraj
Chair dc:contributor.committeechair
  • Spanos, Aris
Committee members dc:contributor.committeemember
  • Tsang, Kwok Ping
  • Haller, Hans H.
  • Ashley, Richard A.

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:101
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/19197

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Poudyal, Niraj. Confronting Theory with Data: the Case of DSGE Modeling. doctoral thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/19197