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Some New Evidence on the Timing of Consumption Decisions and on Their Generating Process

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  • Ermini, Luigi

Abstract

While quarterly consumption data are known to be well fitted by an integrated first-order moving average process--IMA(1, 1)--with a positive coefficient, monthly consumption data are found to be well fitted by an IMA(1, 1) process with a negative coefficient. Without measurement errors, one implication is that, if R. Hall's (1978) random walk model of consumption behavior is true, then the agents' decision interval must be greater than a month. (In particular, this evidence rejects the possibility of continuously taken decisions.) Another implication is that, if consumption decisions are generated by an IMA(1, 1) process at intervals shorter than a month, the coefficient must be negative. The paper also discusses the case of monthly data corrupted by measurement errors. Copyright 1989 by MIT Press.

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  • Ermini, Luigi, 1989. "Some New Evidence on the Timing of Consumption Decisions and on Their Generating Process," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 643-650, November.
    • Handle: RePEc:tpr:restat:v:71:y:1989:i:4:p:643-50
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    Cited by:

    1. Jeffrey C. Fuhrer, 1998. "An optimizing model for monetary policy analysis: can habit formation help?," Working Papers 98-1, Federal Reserve Bank of Boston.
    2. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    3. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    4. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
    5. Hari Luitel & Mehmet Tosun, 2014. "A reexamination of state fiscal health and amnesty enactment," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(5), pages 874-893, October.
    6. Luigi Ermini, 1993. "Shock Persistence and Stochastic Trends in Australian Aggregate Output and Consumption," The Economic Record, The Economic Society of Australia, vol. 69(1), pages 34-43, March.
    7. Malgarini, Marco & Margani, Patrizia, 2005. "Psychology, consumer sentiment and household expenditures: a disaggregated analysis," MPRA Paper 42443, University Library of Munich, Germany.
    8. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    9. Wu, C.C. & Lee, Jack C., 2007. "Estimation of a utility-based asset pricing model using normal mixture GARCH(1,1)," Economic Modelling, Elsevier, vol. 24(2), pages 329-349, March.
    10. Tony Wirjanto, 1997. "Aggregate consumption behaviour with time-nonseparable preferences and liquidity constraints," Applied Financial Economics, Taylor & Francis Journals, vol. 7(1), pages 107-114.

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