Working Paper: NBER ID: w13404
Authors: Andreas Beyer; Roger E. A. Farmer; Jérôme Henry; Massimiliano Marcellino
Abstract: DSGE models are characterized by the presence of expectations as explanatory variables. To use these models for policy evaluation, the econometrician must estimate the parameters of expectation terms. Standard estimation methods have several drawbacks, including possible lack or weakness of identification of the parameters, misspecification of the model due to omitted variables or parameter instability, and the common use of inefficient estimation methods. Several authors have raised concerns over the implications of using inappropriate instruments to achieve identification. In this paper we analyze the practical relevance of these problems and we propose to combine factor analysis for information extraction from large data sets and GMM to estimate the parameters of systems of forward looking equations. Using these techniques, we evaluate the robustness of recent findings on the importance of forward looking components in the equations of a standard New-Keynesian model.
Keywords: New Keynesian models; Factor analysis; GMM; Monetary transmission mechanism
JEL Codes: E5; E52; E58
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Combination of factor analysis and GMM (C38) | Improvement in estimation of forward-looking components (C51) |
| Single equation methods (C20) | Biased estimates due to omitted variables (C20) |
| Using a system approach (P41) | More efficient parameter estimates (C51) |
| Inclusion of factors extracted from large datasets (C38) | Alleviation of omitted variable problems (C20) |
| Alleviation of omitted variable problems (C20) | More accurate and significant estimates of forward-looking coefficients (C51) |
| Weak instruments (C26) | Complication of identification process (D91) |