Local Identification in DSGE Models

The issue of parameter identification arises whenever structural models are estimated. This paper develops a simple condition for local identification in linearized DSGE models. The condition is necessary and sufficient for identification with likelihood-based methods under normality, or with limited information methods that utilize only second moments of the data. Using the methodology developed in the paper researchers can answer, prior to estimation, the following questions: which parameters are locally identified and which are not; is the identification failure due to data limitations, such as a lack of observations for some variables, or is it intrinsic to the structure of the model.

Are asset price data informative about news shocks? A DSGE perspective

Standard economic intuition suggests that asset prices are more sensitive to news than other economic aggregates. This has led many researchers to conclude that asset price data would be very useful for the estimation of business cycle models containing news shocks. This paper shows how to formally evaluate the information content of observed variables with respect to unobserved shocks in structural macroeconomic models. The proposed methodology is applied to two different real business cycle models with news shocks. The contribution of asset prices is found to be relatively small. The methodology is general and can be used to measure the informational importance of observables with respect to latent variables in DSGE models. Thus, it provides a framework for systematic treatment of such issues, which are usually discussed in an informal manner in the literature.info:eu-repo/semantics/publishedVersio

Calibration and the estimation of macroeconomic models

We propose two measures of the impact of calibration on the estimation of macroeconomic models. The first quantifies the amount of information introduced with respect to each estimated parameter as a result of fixing the value of one or more calibrated parameters. The second is a measure of the sensitivity of parameter estimates to perturbations in the calibration values. The purpose of the measures is to show researchers how much and in what way calibration affects their estimation results – by shifting the location and reducing the spread of the marginal posterior distributions of the estimated parameters. This type of analysis is often appropriate since macroeconomists do not always agree on whether and how to calibrate structural parameters in macroeconomic models. The methodology is illustrated using the models estimated in Smets and Wouters (2007) and Schmitt-Groh´e and Uribe (2012).info:eu-repo/semantics/publishedVersio

Evaluating the strength of identification in DSGE models. An a priori approach

This paper presents a new approach to parameter identification analysis in DSGE models wherein the strength of identification is treated as property of the underlying model and studied prior to estimation. The strength of identification reflects the empirical importance of the economic features represented by the parameters. Identification problems arise when some parameters are either nearly irrelevant or nearly redundant with respect to the aspects of reality the model is designed to explain. The strength of identification therefore is not only crucial for the estimation of models, but also has important implications for model development. The proposed measure of identification strength is based on the Fisher information matrix of DSGE models and depends on three factors: the parameter values, the set of observed variables and the sample size. By applying the proposed methodology, researchers can determine the effect of each factor on the strength of identification of individual parameters, and study how it is related to structural and statistical characteristics of the economic model. The methodology is illustrated using the medium-scale DSGE model estimated in Smets and Wouters (2007).

European Community project MONFISPOL (grant agreement SSH-CT-2009-225149) : Deliverable 3.1.2 Algorithms for identification analysis under the DYNARE environment: final version of software

In this report we document in detail the Identification package developed for the DYNARE environment. The package implements methodologies and collects developed algorithms to assess identification of DSGE models in the entire prior space of model deep parameters, by combining `classical' local identification methodologies and global tools for model analysis, like global sensitivity analysis.JRC.G.3-Econometrics and applied statistic

Essays on Identification and Estimation of Dynamic Stochastic General Equilibrium Models.

The estimation of dynamic stochastic general equilibrium (DSGE) models is the subject of a rapidly growing literature. This dissertation contributes to the existing body of work by focusing on issues related to parameter identification. In the first essay I show that DSGE models are characterized by a set of cross-equation and covariance restrictions, which can be used to determine the identifiability, and estimate the parameters of such models. I derive conditions for identification, and propose a two-step minimum distance method for estimating the parameters of DSGE models. I show that the estimator is asymptotically efficient, and provide simulation evidence that it has good small sample properties. In the second essay I show how the Information matrix of DSGE models can be evaluated analytically. This is achieved by a factorization of the matrix as a product of two terms: the Jacobian matrix of the mapping from deep to reduced-form parameters, and the Information matrix of the reduced-form model. I show that both terms can be derived analytically. This result is useful for the estimation of DSGE models, as well as for detecting identification problems. In the third essay I develop a methodology for analyzing identification in linearized DSGE models. Specifically, I show how to address the following questions: are the parameters of the model identifiable; how strong is identification; if there are identification problems, do they originate in the model or in the data; which parameters are not well-identified and why. I apply this methodology to study identification of a model estimated in Smets and Wouters (2007). I find that identification is generally very weak, and the problems are largely in the structure of the model, and thereby cannot be resolved by using more informative data. I estimate the model with maximum likelihood, and find substantial differences with the estimates obtained with Bayesian methods. I conclude that the use of DSGE models for policy analysis should be done with caution since the results are likely to be strongly influenced by the prior distribution.Ph.D.EconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60811/1/niskrev_1.pd

Choosing the variables to estimate singular DSGE models: Comment

In a recent article Canova et al. (2014) study the optimal choice of variables to use in the estimation of a simplified version of the Smets and Wouters (2007) model. In this comment we examine their conclusions by applying a different methodology to the same model. Our results call into question most of Canova et al. (2014) conclusions

Choosing the variables to estimate singular DSGE models: Comment

In a recent article Canova et al. (2014) study the optimal choice of variables to use in the estimation of a simplified version of the Smets and Wouters (2007) model. In this comment we examine their conclusions by applying a different methodology to the same model. Our results call into question most of Canova et al. (2014) conclusions

On Identification Issues in Business Cycle Accounting Models

Since its introduction by Chari et al. (2018), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models, despite the methodology typically involving estimating relatively large scale dynamic stochastic general equilibrium models. In this paper we investigate whether such issues are of concern in the original methodology and in an extension proposed by Sustek (2011) called Monetary BCA. We resort to two types of identification tests in population. One concerns strict identification as theorized by Komuner and Ng (2011), while the other deals both with strict and weak identification as in Iskrev (2015). As to the former, when restricting the estimation to the parameters governing the latent variable's laws of motion, we find that both in the BCA and MBCA framework, all parameters fulfill the requirements for strict identification. If instead we estimate all structural parameters of the model jointly, both frameworks show strict identification failures in several parameters. These results hold for both tests. We show that restricting estimation of some deep parameters can obviate such failures. When we explore weak identification issues, we find that they affect both models. They arise from the fact that many of the estimated parameters do not have a distinct effect on the likelihood. Most importantly, we explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, we compute some statistics of interest to practitioners of the BCA methodology