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Estimating Information Rigidity Using Firms' Survey Data

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  • Carrera Cesar

    () (Banco Central de Reserva del Peru ()

Abstract

The slope of the sticky information Phillips curve proposed by Mankiw and Reis (2002) is based on the degree of information rigidity on the part of firms. Carroll (2003) uses an epidemiology model of expectations and finds evidence for the U.S. of a one-year lag in the transmission of information from professional forecasters to households. Using financial institutions’ and firms’ survey data from Peru and the model proposed by Carroll, I estimate the degree of information rigidity for the Peruvian economy. This paper also considers heterogeneous responses and explores the cross-sectional dimension of these survey forecasts. I find that the degree of information stickiness ranges between one and two quarters, a result that is robust to different specifications.

Suggested Citation

  • Carrera Cesar, 2012. "Estimating Information Rigidity Using Firms' Survey Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-34, June.
  • Handle: RePEc:bpj:bejmac:v:12:y:2012:i:1:n:13
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    References listed on IDEAS

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    1. Reis, Ricardo, 2006. "Inattentive consumers," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1761-1800, November.
    2. Ricardo Reis, 2006. "Inattentive Producers," Review of Economic Studies, Oxford University Press, vol. 73(3), pages 793-821.
    3. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters,in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270 National Bureau of Economic Research, Inc.
    4. Ricardo Nunes, 2009. "On the Epidemiological Microfoundations of Sticky Information," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 643-657, October.
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    6. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
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    Cited by:

    1. repec:mbr:jmonec:v:10:y:2015:i:1:p:1-23 is not listed on IDEAS
    2. Mendoza, Liu & Morales, Daniel, 2013. "Construyendo un índice coincidente de recesión: Una aplicación para la economía peruana," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 81-100.
    3. Mendoza, Liu & Morales, Daniel, 2012. "Constructing a real-time coincident recession index: an application to the Peruvian economy," Working Papers 2012-020, Banco Central de Reserva del Perú.
    4. Carrera, César & Ramírez-Rondán, Nelson, 2013. "Inflation, Information Rigidity, and the Sticky Information Phillips Curve," Working Papers 2013-017, Banco Central de Reserva del Perú.
    5. César Carrera & Jairo Flores, 2017. "Modelling and forecasting money demand: divide and conquer," Working Papers 2017-91, Peruvian Economic Association.

    More about this item

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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