Impulse Response Analysis in a Misspecified DSGE Model: A Comparison of Full and Limited Information Techniques
Sebastian Giesen, Rolf Scheufele
Applied Economics Letters,
No. 3,
2016
Abstract
In this article, we examine the effect of estimation biases – introduced by model misspecification – on the impulse responses analysis for dynamic stochastic general equilibrium (DSGE) models. Thereby, we use full and limited information estimators to estimate a misspecified DSGE model and calculate impulse response functions (IRFs) based on the estimated structural parameters. It turns out that IRFs based on full information techniques can be unreliable under misspecification.
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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Journal of Macroeconomics,
June
2016
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameter estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models
Sebastian Giesen, Rolf Scheufele
Abstract
In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.
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Evaluating the German (New Keynesian) Phillips Curve
Rolf Scheufele
North American Journal of Economics and Finance,
2010
Abstract
This paper evaluates the New Keynesian Phillips curve (NKPC) and its hybrid variant within a limited information framework for Germany. The main interest resides in the average frequency of price re-optimization by firms. We use the labor income share as the driving variable and consider a source of real rigidity by allowing for a fixed firm-specific capital stock. A GMM estimation strategy is employed as well as an identification robust method based on the Anderson–Rubin statistic. We find that the German Phillips curve is purely forward-looking. Moreover, our point estimates are consistent with the view that firms re-optimize prices every 2–3 quarters. These estimates seem plausible from an economic point of view. But the uncertainties around these estimates are very large and also consistent with perfect nominal price rigidity, where firms never re-optimize prices. This analysis also offers some explanation as to why previous results for the German NKPC based on GMM differ considerably. First, standard GMM results are very sensitive to the way in which orthogonality conditions are formulated. Further, model mis-specifications may be left undetected by conventional J tests. This analysis points out the need for identification robust methods to get reliable estimates for the NKPC.
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Evaluating the German (New Keynesian) Phillips Curve
Rolf Scheufele
IWH Discussion Papers,
No. 10,
2008
Abstract
This paper evaluates the New Keynesian Phillips Curve (NKPC) and its hybrid
variant within a limited information framework for Germany. The main interest rests on the average frequency of price re-optimization of firms. We use the labor income share as the driving variable and consider a source of real rigidity by allowing for a fixed firm-specific capital stock. A GMM estimation strategy is employed as well as an identification robust method that is based upon the Anderson-Rubin statistic. We find out that the German Phillips Curve is purely forward looking. Moreover, our point estimates are consistent with the view that firms re-optimize prices every two to three quarters. While these estimates seem plausible from an economic point of view, the uncertainties around these estimates are very large and also consistent with perfect nominal price rigidity where firms never re-optimize prices. This analysis also offers some explanations why previous results for the German NKPC based on GMM differ considerably. First, standard GMM results are very sensitive to the way how orthogonality conditions are formulated. Additionally, model misspecifications may be left undetected by conventional J tests. Taken together, this analysis points out
the need for identification robust methods to get reliable estimates for the NKPC.
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