Reconciling microeconomic and macroeconomic estimates of price stickiness
Adam Cagliarini,
Tim Robinson and
Allen Tran
Journal of Macroeconomics, 2011, vol. 33, issue 1, 102-120
Abstract:
This paper attempts to reconcile the high estimates of price stickiness from macroeconomic estimates of a New-Keynesian Phillips curve (NKPC) with the lower values obtained from surveys of firms' pricing behaviour. This microeconomic evidence also suggests that the frequency with which firms adjust their prices varies across sectors. Building on the insights of Carvalho (2006), we present Monte Carlo evidence that suggests that in the presence of this heterogeneity estimates of the NKPC obtained using conventional methods, such as GMM, are likely to considerably overstate the degree of aggregate price stickiness. Furthermore, if roundabout production is a characteristic of the economy the NKPC will falsely suggest that a sizeable fraction of prices are indexed to past inflation. These problems arise because of a type of misspecification and a lack of suitable instruments.
Keywords: New-Keynesian; Phillips; Curves; Inflation; Heterogeneity (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0164-0704(10)00085-6
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Reconciling Microeconomic and Macroeconomic Estimates of Price Stickiness (2010) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:33:y:2011:i:1:p:102-120
Access Statistics for this article
Journal of Macroeconomics is currently edited by Douglas McMillin and Theodore Palivos
More articles in Journal of Macroeconomics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().