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Testing the Rationality of Survey Data Using the Weighted Double-Bootstrapped Method of Moments

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  • Jeong, Jinook
  • Maddala, G S

Abstract

Conventional tests for rationality of survey data on expectations are not valid in the presence of measurement errors. However, if two or more survey measures of expectations are available on the true unobserved expectational variables, we can devise the appropriate FIML estimation methods and Wald tests for rationality. This paper uses this method for survey data on expectations for the 90-day Treasury bill rates. However, the Wald tests would be based on inaccurate standard errors in the presence of heteroskedasticity, and also be subject to size distortions if asymptotic critical values are used. The present paper corrects these two problems using a weighted double bootstrap method. Copyright 1996 by MIT Press.

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  • Jeong, Jinook & Maddala, G S, 1996. "Testing the Rationality of Survey Data Using the Weighted Double-Bootstrapped Method of Moments," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 296-302, May.
    • Handle: RePEc:tpr:restat:v:78:y:1996:i:2:p:296-302
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    Cited by:

    1. Markiewicz, Agnieszka & Pick, Andreas, 2014. "Adaptive learning and survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 685-707.
    2. Roman Horváth & Jakub Matějů, 2011. "How Are Inflation Targets Set?," International Finance, Wiley Blackwell, vol. 14(2), pages 265-300, June.
    3. Pierre‐Daniel Sarte, 2014. "When Is Sticky Information More Information?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1345-1379, October.
    4. Paul Bennett & In Sun Geoum & David S. Laster, 1997. "Rational bias in macroeconomic forecasts," Staff Reports 21, Federal Reserve Bank of New York.
    5. Santiago Pinto & Pierre-Daniel G. Sarte & Robert Sharp, 2015. "Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever," Working Paper 15-9, Federal Reserve Bank of Richmond.
    6. Higgins, Matthew L. & Mishra, Sagarika, 2014. "State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth," Economic Modelling, Elsevier, vol. 38(C), pages 627-632.
    7. Paul Bennett & In Sun Geoum & David S. Laster, 1996. "Rational bias in macroeconomic forecasts," Research Paper 9617, Federal Reserve Bank of New York.
    8. Ali, Syed Zahid & Anwar, Sajid, 2017. "Exchange rate pass through, cost channel to monetary policy transmission, adaptive learning, and the price puzzle," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 69-82.
    9. Pierre-Daniel G. Sarte, 2010. "Learning about informational rigidities from sectoral data and diffusion indices," Working Paper 10-09, Federal Reserve Bank of Richmond.
    10. Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.

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