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A Note on Forecasting Alcohol Demand

  • Derby Voon (a1) and James Fogarty (a2)

Abstract

A recent study in the Journal of Wine Economics presented forecasts of future alcohol consumption derived using the ARIMA (Box–Jenkins) method. Alcohol consumption forecasts can be developed using many different methodologies. In this Note we highlight the value of using multiple methods to develop alcohol consumption forecasts, and demonstrate the capability of the R software platform as a general forecasting tool. (JEL Classifications: D12, C53)

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The authors thank Karl Storchmann, an anonymous referee, and the editorial team at JWE for their assistance with this Note.

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References

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Aizenman, J., and Brooks, E. (2008). Globalization and taste convergence: The cases of wine and beer. Review of International Economics, 16(2), 217233.
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Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. Hoboken, NJ: John Wiley & Sons.
Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559583.
Colen, L., and Swinnen, J. (2016). Economic growth, globalisation and beer consumption. Journal of Agricultural Economics, 67(1), 186207.
De Livera, A. M., Hyndman, R. J., and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 15131527.
Fogarty, J., and Voon, D. (2018). Alcohol consumption in the United States: Past, present, and future trends. Journal of Wine Economics, 13(2), 121143.
Hart, J., and Alston, J. M. (2019). Evolving consumption patterns in the U.S. alcohol market: Disaggregated spatial analysis. Journal of Wine Economics, forthcoming.
Holmes, A. J., and Anderson, K. (2017). Convergence in national alcohol consumption patterns: New global indicators. Journal of Wine Economics, 12(2), 117148.
Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Berlin, Germany: Springer Science & Business Media.
Hyndman, R., Lee, A., Wang, E., and Wickramasuriya, S. (2018). hts: Hierarchical and grouped time series. R package version 5.1.5.
Hyndman, R. J. (2017). forecast: Forecasting functions for time series and linear models. R package version 8.2.
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Hyndman, R. J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
LaVallee, R. A., Kim, T., and Yi, H.-y. (2014). Surveillance report# 98: Apparent per capita alcohol consumption: National, state, and regional trends, 1977–2012. US Department of Health and Human Services (DHHS).
Mills, T. C. (2018). Is there convergence in national alcohol consumption patterns? Evidence from a compositional time series approach. Journal of Wine Economics, 13(1), 9298.
Smith, D. E., and Mitry, D. J. (2007). Cultural convergence: Consumer behavioral changes in the European wine market. Journal of Wine Research, 18(2), 107112.

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A Note on Forecasting Alcohol Demand

  • Derby Voon (a1) and James Fogarty (a2)

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