چکیده:
In this paper we study the identification problem of parameters of Dynamic Stochastic
General Equilibrium Models with emphasis on structural constraints, in order to make the
number of observable variables is equal to the number of exogenous variables. We derive
a set of identifiability conditions and suggest a procedure for a thorough analysis of
identification at each point in the parameters space. The procedure can be applied, before
DSGE models are estimated, to determine where identification fails. We also use a Monte
Carlo simulation and study the effect of restrictions on the estimate. The results show that
the use of restrictions for estimation, when identification is reduced, leads us to inaccurate
estimates and unreliable inference even when the number of observations is large.
خلاصه ماشینی:
Identifiability of Dynamic Stochastic General Equilibrium Models with Covariance Restrictions {مراجعه شود به فایل جدول الحاقی} In this paper we study the identification problem of parameters of Dynamic Stochastic General Equilibrium Models with emphasis on structural constraints, in order to make the number of observable variables is equal to the number of exogenous variables.
In this paper, we present an efficient approach for determining identifiability of the parameters of linearized DSGE models, by using restrictions on the reduced form.
In particular, we present necessary and sufficient conditions for local identification of deep parameters and discuss when its global identifiability can be ascertained.
The early literature on identification of rational expectations (RE) models started with the works by Sargent (1976) and McCallum (1979) on observational equivalence and was extended to more general setups by Wallis (1980), Pesaran (1981), and Pudney (1982).
In general, a linearized model of DSGE can be expressed in the following form: {مراجعه شود به فایل جدول الحاقی} In the literature, various approaches are proposed for solving linear rational expectations models like (1) (see for instance Blanchard and Kahn (1980), Anderson and Moore (1985), Klein (2000) and Sims (2002)).
A necessary condition for to be locally identifiable by the {مراجعه شود به فایل جدول الحاقی} Theorem 2 tells us when the mean restrictions (6) are sufficient for the identification of .
(View the image of this page) 5 Conclusion In this paper, we take a starting point with the cross-equation and covariance restrictions that characterize linearized DSGE models and show how they can be used to study the identification of the parameters of such models.