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Estimating Information Rigidity using Firms’ Survey Data

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  • Carrera, César

    (Banco Central de Reserva del Perú)

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 is such that it takes between one and three quarters for updating information, a result that is robust to different specifications.

Suggested Citation

  • Carrera, César, 2012. "Estimating Information Rigidity using Firms’ Survey Data," Working Papers 2012-004, Banco Central de Reserva del Perú.
  • Handle: RePEc:rbp:wpaper:2012-004
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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.
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    Citations

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    Cited by:

    1. Miguel Saldarriaga & Pablo del Aguila & Kevin Gershy-Damet, 2017. "Has inflation targeting anchored inflation expectations? Evidence from Peru," Working Papers 2017-103, Peruvian Economic Association.
    2. Marco A. Acosta, 2017. "Anchoring of Inflation Expectations in Mexico," Monetaria, Centro de Estudios Monetarios Latinoamericanos, CEMLA, vol. 0(1), pages 95-132, January-J.
    3. Carrera, César & Ramírez-Rondán, N.R., 2019. "Inflation, Information Rigidity, And The Sticky Information Phillips Curve," Macroeconomic Dynamics, Cambridge University Press, vol. 23(7), pages 2597-2615, October.
    4. Hematy , Maryam & Pedram , Mehdi, 2015. "Threshold Effects in Sticky Information Philips Curve: Evidence from Iran," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 10(1), pages 1-23, January.
    5. 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.
    6. 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.
    7. César Carrera & Jairo Flores, 2017. "Modelling and forecasting money demand: divide and conquer," Working Papers 2017-91, Peruvian Economic Association.
    8. César Carrera & Miguel Puch, 2019. "Consumption dynamics and the expectation channel in a Small Open Economy," Working Papers 144, Peruvian Economic Association.

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    More about this item

    Keywords

    Inflation expectations; Heterogeneous expectations; Survey expectations; Epidemiology; Sticky Information;
    All these keywords.

    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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