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Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries

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  • Qiao, Zhuo
  • Chu, Patrick Kuok-Kun

Abstract

This study provides the first attempt to examine the ability of the price of fine wine to forecast the Gross Domestic Product (GDP) for the major developed countries. Considering the limitation of a linear Granger causality test in detecting nonlinear causal relationships, a nonlinear Granger causality test is also employed. The results from our nonlinear causality test show that this new variable contains useful information to forecast GDP for the US, the UK, and Australia, suggesting that we may include it as a forecasting variable in GDP forecasting models, especially nonlinear models, for these three countries.

Suggested Citation

  • Qiao, Zhuo & Chu, Patrick Kuok-Kun, 2014. "Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries," Economic Modelling, Elsevier, vol. 38(C), pages 75-79.
    • Handle: RePEc:eee:ecmode:v:38:y:2014:i:c:p:75-79
    DOI: 10.1016/j.econmod.2013.12.006
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    Cited by:

    1. Elie Bouri & Tsangyao Chang & Rangan Gupta, 2016. "Testing the Efficiency of the Wine Market using Unit Root Tests with Sharp and Smooth Breaks," Working Papers 201664, University of Pretoria, Department of Economics.
    2. Samitas, Aristeidis & Papathanasiou, Spyros & Koutsokostas, Drosos & Kampouris, Elias, 2022. "Volatility spillovers between fine wine and major global markets during COVID-19: A portfolio hedging strategy for investors," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 629-642.
    3. Ben Ameur, Hachmi & Le Fur, Eric, 2020. "Volatility transmission to the fine wine market," Economic Modelling, Elsevier, vol. 85(C), pages 307-316.
    4. Aytaç, Beysül & Coqueret, Guillaume & Mandou, Cyrille, 2018. "Herding behavior among wine investors," Economic Modelling, Elsevier, vol. 68(C), pages 318-328.

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    More about this item

    Keywords

    Price of fine wine; GDP; Granger causality test; Forecast;
    All these keywords.

    JEL classification:

    • 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
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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