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Erschienen in: Journal of Economics and Finance 1/2017

14.10.2015

Nominal GDP targeting under learning

verfasst von: George Waters

Erschienen in: Journal of Economics and Finance | Ausgabe 1/2017

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Abstract

Targeting Nominal GDP growth by monetary policymakers is equivalent to a restriction on policymaker preferences for an optimality condition derived under rational expectations. This paper reports the results of simulations of a calibrated model comparing Nominal GDP growth targeting with the optimal policy in an environment where public expectations are formed under learning and the interest rate rule is a function of public expectations. If the policymaker does not have full information about expectations, policy recommendations assuming rational expectations might lead to excess volatility. Nominal GDP growth targeting mitigates these problems in extreme cases, but cannot be recommended as a universal solution.

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Fußnoten
1
Policymaker preferences could be derived by welfare maximization as in Woodford (2003).
 
2
This condition is a special case of the price level targeting condition \( \widehat {p}_{t}=-\frac {\lambda }{\alpha }\widehat {x}_{t},\) which is consistent with the general optimality condition (4).
 
3
The issue is reminiscent or Rogoff’s (1985) argument for a conservative central banker in the presence of inflation bias.
 
4
The parameters are as follows: β=0.99, φ=0.4, λ=0.05,μ=0.95, ρ=0.8. The standard deviation w t is 0.02, the standard deviation \(\widetilde {w}_{t}\) is 0.224 and the standard deviation of u t is 0.005. The gain parameter in the recursive least squares specification is 0.05.
 
5
Mitra (2003) is a related stability analysis of nominal GDP targeting where the interest responds to expectations and contemporaneous values of output and inflation. It also uses an alternative perceived law of motion.
 
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Metadaten
Titel
Nominal GDP targeting under learning
verfasst von
George Waters
Publikationsdatum
14.10.2015
Verlag
Springer US
Erschienen in
Journal of Economics and Finance / Ausgabe 1/2017
Print ISSN: 1055-0925
Elektronische ISSN: 1938-9744
DOI
https://doi.org/10.1007/s12197-015-9337-3

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