A Quantitative Comparison of Sticky-Price and Sticky-Information Models of Price Setting

Authors


  • This research has benefited from comments from the editor and referees as well as colleagues at the Federal Reserve and participants in the Federal Reserve Board's conference on “Quantitative Evidence on Price Determination”—especially discussions by Mark Gertler, Chris Sims, and Frank Smets. I would also like to thank Jean-Philippe Laforte for his views on ongoing research and assistance with computer code. The views expressed herein are those of the author, and do not represent those of the Federal Reserve Board or its staff.

Abstract

I estimate sticky-price and sticky-information models of price setting for the United States via maximum-likelihood techniques, reaching several conclusions. First, the sticky-price model fits best, and captures inflation dynamics as well as reduced-form equations once hybrid-behavior is allowed. Second, the importance of hybrid behavior in sticky-price models is potentially consistent with a role for some information imperfections, such as sticky information, as a complement to nominal price rigidities. Finally, the favorable results herein for the hybrid sticky-price model when evaluated by statistics that summarize the relative fit of different models is consistent with the existing literature that is both supportive and dismissive of such models, as this literature has largely ignored fit in evaluating such models. Many previous studies have focused on ancillary issues, such as the standard errors associated with certain parameters or Granger-causality tests that may not provide much information about sticky-price models.

Ancillary