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The Relationship between the Hybrid New Keynesian Phillips Curve and the NAIRU over Time

Lena Vogel ()
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Lena Vogel: Department for Economics and Politics, University of Hamburg

Authors registered in the RePEc Author Service: Lena Dräger

No 200803, Macroeconomics and Finance Series from University of Hamburg, Department of Socioeconomics

Abstract: New Keynesian models of the Phillips curve in the spirit of Galí and Gertler (1999) generally assume a short-run trade-off between inflation and a measure of excess demand due to nominal rigidities, while in the long run inflation is constant at the Non-Accelerating Inflation Rate of Unemployment (NAIRU). By contrast, Gordon (1997) in his "triangle model" of inflation models a time-varying NAIRU. We combine both approaches and estimate state-space models of the hybrid New Keynesian Phillips curve (NKPC), where excess demand is measured by the unemployment gap and the NAIRU is allowed to vary over time as in Gordon (1997). Moreover, inflation expectations are measured directly from surveys on household?s inflation expectations and not instrumented for. Our model is estimated for the US, the UK, Italy and Spain and we find considerable variation in the NAIRU over time with NAIRU estimates significantly different from HP-filter derived measures such as usually employed in dynamic stochastic general equilibrium (DSGE) models. In contrast to GMM results for the hybrid NKPC, we find that backward looking behaviour generally seems to be quantitatively more important for inflation than forward looking behaviour.

Keywords: Hybrid New Keynesian Phillips curve; time-varying NAIRU; state-space models (search for similar items in EconPapers)
JEL-codes: C32 E31 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2008-10
New Economics Papers: this item is included in nep-cba and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://www.wiso.uni-hamburg.de/repec/hepdoc/macppr_3_2008.pdf First version, 2008 (application/pdf)

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