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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.

Suggested Citation

  • 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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    1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    2. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    3. Michael B. Devereux & Philip R. Lane & Juanyi Xu, 2006. "Exchange Rates and Monetary Policy in Emerging Market Economies," Economic Journal, Royal Economic Society, vol. 116(511), pages 478-506, April.
    4. Tim Robinson & Andrew Stone & Marileze van Zyl, 2003. "The Real-time Forecasting Performance of Phillips Curves," RBA Research Discussion Papers rdp2003-12, Reserve Bank of Australia.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    7. Charles Engel, 1999. "Accounting for U.S. Real Exchange Rate Changes," Journal of Political Economy, University of Chicago Press, vol. 107(3), pages 507-538, June.
    8. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    9. Batini, Nicoletta & Jackson, Brian & Nickell, Stephen, 2005. "An open-economy new Keynesian Phillips curve for the U.K," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1061-1071, September.
    10. Orphanides, Athanasios & van Norden, Simon, 2005. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 583-601, June.
    11. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    12. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    13. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    15. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    16. De Gregorio, Jose & Giovannini, Alberto & Wolf, Holger C., 1994. "International evidence on tradables and nontradables inflation," European Economic Review, Elsevier, vol. 38(6), pages 1225-1244, June.
    17. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
    18. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    19. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    20. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    21. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
    22. Matheson, Troy, 2010. "Assessing the fit of small open economy DSGEs," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 906-920, September.
    23. Svensson, Lars E. O., 2000. "Open-economy inflation targeting," Journal of International Economics, Elsevier, vol. 50(1), pages 155-183, February.
    24. Alfred V. Guender, 2006. "Stabilising Properties of Discretionary Monetary Policies in a Small Open Economy," Economic Journal, Royal Economic Society, vol. 116(508), pages 309-326, January.
    25. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    26. Linde, Jesper, 2005. "Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1135-1149, September.
    27. Paul Conway & Ben Hunt, 1997. "Estimating potential output: a semi-structural approach," Reserve Bank of New Zealand Discussion Paper Series G97/9, Reserve Bank of New Zealand.
    28. Laxton, Douglas & Pesenti, Paolo, 2003. "Monetary rules for small, open, emerging economies," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 1109-1146, July.
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    Cited by:

    1. Nicoleta CIURILA & Bogdan MURARASU, 2008. "Inflation Dynamics in Romania – a New Keynesian Perspective," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 1(36), pages 155-160, May.
    2. 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.
    3. 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.
    4. Juselius, Mikael, 2008. "Testing the New Keynesian Model on U.S. and Euro Area Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-26.
    5. Matheson, Troy, 2010. "Assessing the fit of small open economy DSGEs," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 906-920, September.
    6. 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.
    7. 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.
    8. Man Wang & Kun Chen & Qin Luo & Chao Cheng, 2018. "Multi-Step Inflation Prediction with Functional Coefficient Autoregressive Model," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    9. Ramos Francia Manuel & Torres García Alberto, 2006. "Inflation Dynamics in Mexico: A Characterization Using the New Phillips Curve," Working Papers 2006-15, Banco de México.
    10. Mr. Sergi Lanau & Adrian Robles & Mr. Frederik G Toscani, 2018. "Explaining Inflation in Colombia: A Disaggregated Phillips Curve Approach," IMF Working Papers 2018/106, International Monetary Fund.
    11. 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.
    12. 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.
    13. Stefán Thórarinsson, 2022. "Analysing inflation dynamics in Iceland using a Bayesian structural vector autoregression model," Economics wp88, Department of Economics, Central bank of Iceland.

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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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