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The Nordhaus test with many zeros

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  • Lahiri, Kajal
  • Zhao, Yongchen
Abstract
We reformulate the Nordhaus test as a friction model where the large number of zero revisions are treated as censored, i.e., unknown values inside a small region of “imperceptibility.” Using Blue Chip individual forecasts of U.S. real GDP growth, inflation, and unemployment over 1985–2020, we find pervasive over-reaction to news at most of the monthly forecast horizons from 24 to 1, but the degree of inefficiency is very small. The updaters, i.e., those who make non-zero revisions, are not found to perform better than their “inattentive” peers.

Suggested Citation

  • Lahiri, Kajal & Zhao, Yongchen, 2020. "The Nordhaus test with many zeros," Economics Letters, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:ecolet:v:193:y:2020:i:c:s0165176520302056
    DOI: 10.1016/j.econlet.2020.109308
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    References listed on IDEAS

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    1. Pedro Bordalo & Nicola Gennaioli & Yueran Ma & Andrei Shleifer, 2020. "Overreaction in Macroeconomic Expectations," American Economic Review, American Economic Association, vol. 110(9), pages 2748-2782, September.
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    3. Dräger, Lena & Lamla, Michael J., 2012. "Updating inflation expectations: Evidence from micro-data," Economics Letters, Elsevier, vol. 117(3), pages 807-810.
    4. Isiklar, Gultekin, 2005. "On aggregation bias in fixed-event forecast efficiency tests," Economics Letters, Elsevier, vol. 89(3), pages 312-316, December.
    5. Dovern, Jonas, 2013. "When are GDP forecasts updated? Evidence from a large international panel," Economics Letters, Elsevier, vol. 120(3), pages 521-524.
    6. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    7. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    8. Zhao, Yongchen, 2019. "Updates to household inflation expectations: Signal or noise?," Economics Letters, Elsevier, vol. 181(C), pages 95-98.
    9. Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2020. "Heterogeneity, Inattention, and Bayesian Updates," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 282-309, January.
    10. Michael J. Lamla & Samad Sarferaz, 2012. "Updating inflation expectations," KOF Working papers 12-301, KOF Swiss Economic Institute, ETH Zurich.
    11. Binder, Carola, 2017. "Consumer forecast revisions: Is information really so sticky?," Economics Letters, Elsevier, vol. 161(C), pages 112-115.
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    Cited by:

    1. An, Zidong & Liu, Dingqian & Wu, Yuzheng, 2021. "Expectation formation following pandemic events," Economics Letters, Elsevier, vol. 200(C).
    2. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    Friction model; Expectations updating; Forecast efficiency; Fixed-event forecasts; Inattentive forecasters;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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