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Jinhe Center Fall 2018 Weekly seminar #3
Source:   Date: 2018-09-28   Visits:

Heterogeneous Price Rigidity: empirical evidences and theoretical models

 

TimeSep 25th (Tuesday) 16:45-18:15 PM

LocationJinhe 874

SpeakerDr. TIAN Kun

 

Introduction

Dr. Tian is a Lecturer at the Norwich Business School, University of East Anglia in the UK. He graduated from Cardiff University in 2012. His main research interests is New Keynesian DSGE model, Price rigidity and wage rigidity, and Firms’ price-setting behaviour and consumers’ responses. Dr. Tian has published several papers in journals including Applied Economics, European Journal of Marketing and Oxford Bulletin of Economics and Statistics. His work has received media coverage including The Economist and BBC. He is currently the principle investigator of two provincial and ministerial-level funded research projects.

Abstract

In this paper, I assess the price rigidity in a unified framework a la Dixon (2010), using CPI micro data in the UK from 1996 to 2008. I find that, on average, the frequency of posted price changes is 19% per month for identical items; it falls to about 15% per month if we exclude the sales; while it rises to about 21% per month if we include product substitution. The fraction of price decreasing in our CPI data accounts for about 40%, which do not support the downward price rigidity in pricing theory. I estimate the hazard function by using non-parametric method, treating censoring in estimation carefully. I also derive out the price duration distribution across firm which is consistent with the average frequency of price changes. I find that heterogeneity is a significant feature in measuring price rigidity. Furthermore, I investigate the implication on workhorse macro models. I introduce multiple Cavlo and multiple menu cost model, given the empirical findings in micro data. I compare the simulation results with the stylized facts in micro data, suggesting that new generation of sticky price models are needed to meet the main empirical findings in micro data.

 

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