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Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis

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  • Sunil Kumar
  • Zakir Husain
  • Diganta Mukherjee

Abstract

In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created. This paper describes how latent class analysis may be used to check the consistency of responses and ensure a parsimonious questionnaire. In particular, we examine how temporal changes may be incorporated into the model. Our methodology is illustrated using three rounds of Consumer Confidence Survey (CCS) conducted by Reserve Bank of India (RBI).

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  • Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    • Handle: RePEc:arx:papers:1509.01215
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    1. Mick, David Glen, 1996. "Are Studies of Dark Side Variables Confounded by Socially Desirable Responding? The Case of Materialism," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 23(2), pages 106-119, September.
    2. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    3. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    4. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    5. Piotr Bialowolski, 2013. "Patterns of credit ownership in Poland – A multi-group latent class approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the Sixth IFC Conference on "Statistical issues and activities in a changing environment", Basel, 28-29 August 2012., volume 36, pages 444-464, Bank for International Settlements.
    6. Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119, February.
    7. Dean Harper, 1972. "Local dependence latent structure models," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 53-59, March.
    8. Edmund S. Phelps, 1968. "Money-Wage Dynamics and Labor-Market Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 76(4), pages 678-678.
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