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Phillips curve forecasting in a small open economy

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Abstract

Stock and Watson (1999) show that the Phillips curve is a good forecasting tool in the United States. We assess whether this good performance extends to two small open economies, with relatively large tradable sectors. Using data for Australia and New Zealand, we find that the open economy Phillips curve performs poorly relative to a univariate autoregressive benchmark. However, its performance improves markedly when sectoral Phillips curves are used which model the tradable and non-tradable sectors separately. Combining forecasts from these sectoral models is much better than obtaining forecasts from a Phillips curve estimated on aggregate data. We also find that a diffusion index that combines a large number of indicators of real economic activity provides better forecasts of non-tradable inflation than more conventional measures of real demand, thus supporting Stock and Watson's (1999) findings for the United States.

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  • Troy Matheson, 2006. "Phillips curve forecasting in a small open economy," Reserve Bank of New Zealand Discussion Paper Series DP2006/01, Reserve Bank of New Zealand.
  • Handle: RePEc:nzb:nzbdps:2006/01
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    Cited by:

    1. Croonenbroeck, Carsten & Stadtmann, Georg, 2012. "Evaluating Phillips curve based inflation forecasts in Europe: A note," Discussion Papers 329, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    2. Alfred Guender & Yu Xie, 2007. "Is there an exchange rate channel in the forward-looking Phillips curve? A theoretical and empirical investigation," New Zealand Economic Papers, Taylor & Francis Journals, vol. 41(1), pages 5-28.
    3. Matheson, Troy, 2010. "Assessing the fit of small open economy DSGEs," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 906-920, September.
    4. Bazán-Palomino, Walter & Rodríguez, Gabriel, 2018. "The New Keynesian framework for a small open economy with structural breaks: Empirical evidence from Peru," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 13-25.
    5. Ashley Dunstan & Troy Matheson & Hamish Pepper, 2009. "Analysing wage and price dynamics in New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/06, Reserve Bank of New Zealand.
    6. Claudio E. V. Borio & Andrew Filardo, 2007. "Globalisation and inflation: New cross-country evidence on the global determinants of domestic inflation," BIS Working Papers 227, Bank for International Settlements.
    7. Ernest Gnan & Maria Teresa Valderrama, 2006. "Globalization, Inflation and Monetary Policy," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 37-54.
    8. Man Wang & Kun Chen & Qin Luo & Chao Cheng, 2018. "Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model," Sustainability, MDPI, Open Access Journal, vol. 10(6), pages 1-16, May.
    9. Juselius, Mikael, 2008. "Testing the New Keynesian Model on U.S. and Euro Area Data," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 2, pages 1-26.
    10. Nicoleta Ciurila & Bogdan Murarasu, 2008. "Inflation Dynamics in Romania - a New Keynesian Perspective," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 31-38.
    11. Manuel Ramos-Francia & Alberto Torres García, 2006. "Inflation Dynamics in Mexico: A Characterization Using the New Phillips Curve," Working Papers 2006-15, Banco de México.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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