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Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method

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  • Lahiri, Kajal
  • Zhao, Yongchen

Abstract

This paper provides a critical review of the popular Carlson–Parkin (CP) quantification method using household-level data from the University of Michigan’s Survey of Consumers. We find strong evidence against the threshold constancy, symmetry, homogeneity, and overall unbiasedness assumptions of the CP method. To address these violations, we generalize the CP method using a hierarchical ordered probit (HOPIT) model. By comparing the quantified inflation expectations with quantitative expectations obtained from the same set of households directly, we show that the generalized model performs better than the CP method. In particular, when the CP unbiasedness assumption is replaced by a time-varying calibration, the resulting quantified series is found to track the quantitative benchmark well, over diverse time periods.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:1:p:51-62 DOI: 10.1016/j.ijforecast.2014.06.003
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    References listed on IDEAS

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    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    2. Murasawa, Yasutomo, 2017. "Measuring the Distributions of Public Inflation Perceptions and Expectations in the UK," MPRA Paper 76244, University Library of Munich, Germany.
    3. repec:spr:qualqt:v:51:y:2017:i:6:d:10.1007_s11135-016-0416-0 is not listed on IDEAS
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.

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    Keywords

    HOPIT model; Household data; Inflation rate;

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