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Testing the rationality of the National Bank of New Zealand's survey data

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Abstract

We test the rationality of the National Bank of New Zealand's survey data of inflation expectations. We cannot reject the null hypotheses of unbiasedness, efficiency, and orthogonality for a sample from 1985Q1 to 1996Q4. The survey's predictive power is better than those of the random walk and ARIMA models. During the period 1992q1-1996q1, where inflation is low and stable, the predictive power of an ARIMA model is better than that of the survey data, and the predictive power of the survey data is as good as that of the random walk model. These results are not inconsistent with rationality.

Suggested Citation

  • W A Razzak, 1997. "Testing the rationality of the National Bank of New Zealand's survey data," Reserve Bank of New Zealand Discussion Paper Series G97/5, Reserve Bank of New Zealand.
  • Handle: RePEc:nzb:nzbdps:1997/05
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    1. Peter C. B. Phillips & Mico Loretan, 1991. "Estimating Long-run Economic Equilibria," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 407-436.
    2. Brown, Bryan W & Maital, Shlomo, 1981. "What Do Economists Know? An Empirical Study of Experts' Expectations," Econometrica, Econometric Society, vol. 49(2), pages 491-504, March.
    3. Gregory, Allan W. & Hansen, Bruce E., 1996. "Residual-based tests for cointegration in models with regime shifts," Journal of Econometrics, Elsevier, vol. 70(1), pages 99-126, January.
    4. Pearce, Douglas K, 1987. "Short-term Inflation Expectations: Evidence from a Monthly Survey: A Note," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 19(3), pages 388-395, August.
    5. Pearce, Douglas K, 1979. "Comparing Survey and Rational Measures of Expected Inflation: Forecast Performance and Interest Rate Effects," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 11(4), pages 447-456, November.
    6. Gregory, Allan W. & Hansen, Bruce E., 1996. "Residual-based tests for cointegration in models with regime shifts," Journal of Econometrics, Elsevier, vol. 70(1), pages 99-126, January.
    7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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    Cited by:

    1. repec:taf:nzecpp:v:50:y:2016:i:3:p:343-361 is not listed on IDEAS
    2. Hugo Oliveros C., 1999. "Expectativas:Una Aproximación A Través De Modelos De Escogencia Discreta," BORRADORES DE ECONOMIA 002697, BANCO DE LA REPÚBLICA.
    3. Chong, Lucy Lee-Yun & Puah, Chin-Hong & Md Isa, Abu Hassan, 2012. "Theory of rational expectations hypothesis: banks and other financial institutions in Malaysia," MPRA Paper 36657, University Library of Munich, Germany.
    4. L Christopher Plantier & Dean Scrimgeour, 2002. "Estimating a Taylor Rule for New Zealand with a time-varying neutral real rate," Reserve Bank of New Zealand Discussion Paper Series DP2002/06, Reserve Bank of New Zealand.
    5. Hasan Bakhshi & Anthony Yates, 1998. "Are UK inflation expectations rational?," Bank of England working papers 81, Bank of England.
    6. Michelle Lewis & C. John McDermott, 2016. "New Zealand's experience with changing its inflation target and the impact on inflation expectations," New Zealand Economic Papers, Taylor & Francis Journals, vol. 50(3), pages 343-361, September.
    7. Gerberding, Christina, 2001. "The information content of survey data on expected price developments for monetary policy," Discussion Paper Series 1: Economic Studies 2001,09, Deutsche Bundesbank.
    8. Hugo Oliveros, 1999. "Expectativas: Una Aproximación a Través de Modelos de Escogencia Discreta," Borradores de Economia 137, Banco de la Republica de Colombia.

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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