DSGE Model Identification Slides

DSGE Model Identification Slides - Dynamic Identification...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Dynamic Identification of DSGE Models Ivana Komunjer and Serena Ng UCSD and Columbia University All UC Conference 2009 – Riverside 1 Why is the Problem Non-standard? 2 Setup Model Observables 3 Identification Analysis Observational Equivalence Rank and Order Conditions 4 Illustration Example 1: An and Schorfheide (2007) Example 2: Stochastic Growth Model 5 Conclusion Why is the Problem Non-standard? Motivation In estimation of DSGE models, some parameters are always fixed How many (if any) should we fix? Does fixing the parameters guarantee identification of the remaining parameters? Why is the Problem Non-standard? Models are dynamic with non-iid shocks . Likelihood may not exist because of stochastic singularity . Reduced form parameters are generally not identified. Why is the Problem Non-standard? Literature 1 multivariate ARMA models Hannan (1971), Zellner & Palm (1974), Hatanaka (1975), Wallis (1977) rule out ‘stochastically singular’ models 2 classical rank conditions Fisher (1966), Rothenberg (1971), Iskrev (2009) require the reduced form parameters to be identified 3 objective function approaches: likelihood or GMM Rothenberg (1971), Iskrev (2007, 2009), Canova & Sala (2009) depend on the population moments of the data 4 approaches based on observational equivalence linear simultaneous equations systems: Cowles (1950, 1953), linear SVARs: Rubio-Ramirez, Waggoner & Zha (2009) nonlinear models: this paper Why is the Problem Non-standard? Summary of the Results 1 show that the reduced form parameters of DSGE models are generally not identifiable 2 provide rank and order conditions for identifying the structural parameters from the observable spectrum exploits all information in the autocovariances (spectrum) of the observed variables exploits restrictions on observationally equivalent transformations of the model depends only on the system matrices in the solution equations should be evaluated prior to collecting data 3 analyze several examples Setup Model 1 Why is the Problem Non-standard? 2 Setup Model Observables 3 Identification Analysis Observational Equivalence Rank and Order Conditions 4 Illustration Example 1: An and Schorfheide (2007) Example 2: Stochastic Growth Model 5 Conclusion Setup Model Setup Reduced form Solution: K t + 1 ( n K × 1 ) = P ( θ ) ( n K × n K ) K t + Q ( θ ) ( n K × n Z ) Z t W t ( n W × 1 ) = R ( θ ) ( n W × n K ) K t + S ( θ ) ( n W × n Z ) Z t Z t + 1 ( n Z × 1 ) = Ψ ( θ ) ( n Z × n Z ) Z t + e t + 1 ( n Z × 1 ) K t : endogenous (state) variables W t : other endogenous (‘jump’) variables Z t : latent exogenous shocks θ : parameter vector of interest (dim n θ ) Setup Model Assumptions 1 For every θ ∈ Θ , { e t } ∼ WN ( 0, Σ ( θ )) and Σ ( θ ) nonsingular....
View Full Document

{[ snackBarMessage ]}

Page1 / 34

DSGE Model Identification Slides - Dynamic Identification...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online