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The NIRCU and the Phillips curve: an approach based on micro data

Author

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  • Eva M. Köberl
  • Sarah M. Lein

Abstract

In this paper we propose a straightforward method to derive a non-inflationary rate of capacity utilization (NIRCU) based on micro data. We condition the current capacity utilization of firms on their current and planned price adjustments. The non-inflationary capacity utilization rate is then defined as the rate where a firm feels no price adjustment pressure. One of the main advantages is that this methodology uses structural aspects and does not make it necessary to operate with - often rather arbitrary - statistical filters. We show that our aggregate NIRCU performs remarkably well as an indicator of inflationary pressure in a Phillips curve estimation.

Suggested Citation

  • Eva M. Köberl & Sarah M. Lein, 2011. "The NIRCU and the Phillips curve: an approach based on micro data," Canadian Journal of Economics, Canadian Economics Association, vol. 44(2), pages 673-694, May.
    • Handle: RePEc:cje:issued:v:44:y:2011:i:2:p:673-694
    DOI: 10.1111/j.1540-5982.2011.01649.x
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    Cited by:

    1. Francesca Rubiconto, 2023. "Is environmental innovation the key to addressing the dual economic and sustainability challenge of the Italian economy?," Journal of Evolutionary Economics, Springer, vol. 33(3), pages 869-892, July.
    2. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    3. Pirmin Fessler & Fabio Rumler & Gerhard Schwarz, 2014. "A micro-based non-inflationary rate of capacity utilisation as a measure of inflationary pressure: evidence for Austria," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 23-36, February.
    4. Alex Contreras M. & Pablo Del Aguila R. & Fernando Alonso Regalado S. & F. Martín Martinez P., 2017. "Brecha de la capacidad de utilización como medida alternativa de la brecha producto: Un enfoque para Perú basado en micro datos," Working Papers 94, Peruvian Economic Association.
    5. Maritta Paloviita & Matti Viren, 2013. "How do individual forecasters change their views? An analysis with micro panel data," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 295/2013 - Financial Markets and Macroprudential Policy, edition 1, volume 127, chapter 5, pages 79-92, University of Lodz.
    6. Crosilla, Luciana & Leproux, Solange & Malgarini, Marco, 2014. "The impact of the crisis on italian industrial capacity: an assessment based on the istat manufacturing survey," MPRA Paper 67531, University Library of Munich, Germany.
    7. Michael F. Bryan & Brent Meyer & Nicholas B. Parker, 2014. "The inflation expectations of firms: what do they look like, are they accurate, and do they matter?," FRB Atlanta Working Paper 2014-27, Federal Reserve Bank of Atlanta.
    8. Richard Ashley & Randal J. Verbrugge, 2019. "The Intermittent Phillips Curve: Finding a Stable (But Persistence-Dependent) Phillips Curve Model Specification," Working Papers 19-09R2, Federal Reserve Bank of Cleveland, revised 14 Feb 2023.
    9. G. Bruno & L. Crosilla & P. Margani, 2019. "Inspecting the Relationship Between Business Confidence and Industrial Production: Evidence on Italian Survey Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 1-24, April.

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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