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Quantification and characteristics of household inflation expectations in Switzerland

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  • Rina Rosenblatt-Wisch
  • Rolf Scheufele

Abstract

Inflation expectations are a key variable in conducting monetary policy. However, these expectations are generally unobservable and only certain proxy variables exist, such as surveys on inflation expectations. This article offers guidance on the appropriate quantification of household inflation expectations in the Swiss Consumer Survey, where answers are qualitative in nature. We apply and evaluate different variants of the probability approach and the regression approach; we demonstrate that models that include answers on perceived inflation and allow for time-varying response thresholds yield the best results; and we show why the originally proposed approach of Fluri and Spörndli (1987) has resulted in heavily biased inflation expectations since the mid-1990s. Furthermore, we discuss some of the key features of Swiss household inflation expectations, i.e. the fact that there has been a shift in expectation formation since 2000 (expectations are better anchored and less adaptive, and there is lower disagreement of expectations). We suggest that this may be linked to the Swiss National Bank's adjustment of its monetary policy framework around this time. In addition, we outline how expectation formation in Switzerland is in line with the sticky information model, where information disseminates slowly from professional forecasters to households.

Suggested Citation

  • Rina Rosenblatt-Wisch & Rolf Scheufele, 2015. "Quantification and characteristics of household inflation expectations in Switzerland," Applied Economics, Taylor & Francis Journals, vol. 47(26), pages 2699-2716, June.
  • Handle: RePEc:taf:applec:v:47:y:2015:i:26:p:2699-2716
    DOI: 10.1080/00036846.2015.1008773
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    1. Dasgupta, Susmita & Lahiri, Kajal, 1992. "A Comparative Study of Alternative Methods of Quantifying Qualitative Survey Responses Using NAPM Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 391-400, October.
    2. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
    3. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    4. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    5. Doepke Joerg & Dovern Jonas & Fritsche Ulrich & Slacalek Jiri, 2008. "The Dynamics of European Inflation Expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-23, March.
    6. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
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    Cited by:

    1. Łyziak, Tomasz & Paloviita, Maritta, 2017. "Anchoring of inflation expectations in the euro area: Recent evidence based on survey data," European Journal of Political Economy, Elsevier, vol. 46(C), pages 52-73.
    2. Tomasz Lyziak, 2016. "Financial crisis, low inflation environment and short-term inflation expectations in Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 47(3), pages 285-300.
    3. Mellina, Sathya & Schmidt, Tobias, 2018. "The role of central bank knowledge and trust for the public's inflation expectations," Discussion Papers 32/2018, Deutsche Bundesbank.
    4. Thomas Nitschka & Nikolay Markov, 2016. "Semi-Parametric Estimates of Taylor Rules for a Small, Open Economy – Evidence from Switzerland," German Economic Review, Verein für Socialpolitik, vol. 17(4), pages 478-490, November.
    5. Das, Abhiman & Lahiri, Kajal & Zhao, Yongchen, 2019. "Inflation expectations in India: Learning from household tendency surveys," International Journal of Forecasting, Elsevier, vol. 35(3), pages 980-993.
    6. Alain Galli, 2017. "How Reliable are Cointegration-Based Estimates for Wealth Effects on Consumption? Evidence from Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 153(4), pages 437-479, October.
    7. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, Open Access Journal, vol. 3(1), pages 1-22, February.
    8. Tomasz Łyziak, 2016. "The impact of financial crisis and low inflation environment on short-term inflation expectations in Poland," NBP Working Papers 235, Narodowy Bank Polski, Economic Research Department.
    9. Koichiro Kamada & Jouchi Nakajima & Shusaku Nishiguchi, 2015. "Are Household Inflation Expectations Anchored in Japan?," Bank of Japan Working Paper Series 15-E-8, Bank of Japan.
    10. Gießler, Stefan, 2020. "The evolution of monetary policy in Latin American economies: Responsiveness to inflation under different degrees of credibility," IWH Discussion Papers 9/2020, Halle Institute for Economic Research (IWH).

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General

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