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Clustering and forecasting inflation expectations using the World Economic Survey: the case of the 2014 oil price shock on inflation targeting countries

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Abstract

This paper examines inflation expectations of the World Economic Survey for ten inflation targeting countries. First, by a Self Organizing Maps methodology, we cluster the trajectory of agents inflation expectations using the beginning of the oil price shock occurred in June of 2014 as a benchmark in order to discriminate between those countries that anticipated the shock smoothly and those with brisk changes in expectations. Then, the expectations are modeled by artificial neural networks forecasting models. Second, for each country we investigate the information content of the quantitative survey forecast by comparing it to the average annual inflation based on national consumer price indices. The results indicate the presence of heterogeneity among countries to anticipate inflation under the oil shock and, also different patterns of accuracy to predict average annual inflation were found depending on the observed inflation trend. Classification JEL: C02, C222, C45, C63, E27

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  • Hector M. Zarate-Solano & Daniel R. Zapata-Sanabria, 2017. "Clustering and forecasting inflation expectations using the World Economic Survey: the case of the 2014 oil price shock on inflation targeting countries," Borradores de Economia 993, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:993
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    File URL: http://repositorio.banrep.gov.co/bitstream/handle/20.500.12134/6306/be_993.pdf?sequence=1&isAllowed=y
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    References listed on IDEAS

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    1. Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, Open Access Journal, vol. 9(9), pages 1-21, August.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "A self-organizing map analysis of survey-based agents? expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, CEPII research center, issue 146, pages 40-58.
    3. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    4. Anna Stangl, 2007. "European Data Watch: Ifo World Economic Survey Micro Data," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(3), pages 487-496.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
    7. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2016. "A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, Elsevier, vol. 146(C), pages 40-58.
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    Cited by:

    1. Oscar Claveria, 2017. "“What really matters is the economic performance: Positioning tourist destinations by means of perceptual maps," IREA Working Papers 201713, University of Barcelona, Research Institute of Applied Economics, revised Jun 2017.

    More about this item

    Keywords

    Inflation expectations; machine learning; self-organizing maps; nonlinear autoregressive neural network; expectation surveys;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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