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Factor Model Forecasts for New Zealand

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  • Troy D. Matheson

    (Reserve Bank of New Zealand)

Abstract

This paper focuses on forecasting four key New Zealand macroeconomic variables using a dynamic factor model and a large number of predictors. We compare the (simulated) real-time forecasting performance of the factor model with a variety of other time-series models (including the Reserve Bank of New Zealand’s published forecasts), and we gauge the sensitivity of our results to alternative variable-selection algorithms. We find that the factor model performs particularly well at longer horizons.

Suggested Citation

  • Troy D. Matheson, 2006. "Factor Model Forecasts for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
  • Handle: RePEc:ijc:ijcjou:y:2006:q:2:a:6
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    References listed on IDEAS

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    4. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    5. 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.
    6. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    7. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    8. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. 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.
    10. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    11. Aaron Drew & Ben Hunt, 1998. "The Forecasting and Policy System: preparing economic projections," Reserve Bank of New Zealand Discussion Paper Series G98/7, Reserve Bank of New Zealand.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    13. 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.
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    Cited by:

    1. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    4. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," BORRADORES DE ECONOMIA 005273, BANCO DE LA REPÚBLICA.
    5. Domenico Giannone & Troy D. Matheson, 2007. "A New Core Inflation Indicator for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 145-180, December.
    6. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    7. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
    8. Karsten R. Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2009. "Evaluating ensemble density combination - forecasting GDP and inflation," Working Paper 2009/19, Norges Bank.
    9. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    10. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    11. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    12. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
    13. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
    14. Pincheira, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2017. "Forecasting Inflation in Latin America with Core Measures," MPRA Paper 80496, University Library of Munich, Germany.
    15. Michael Ryan & Kam Leong Szeto, 2009. "An Introduction to the New Zealand Treasury Model," Treasury Working Paper Series 09/02, New Zealand Treasury.
    16. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    17. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    18. Ercio Muñoz & Pablo Cruz, 2012. "Uso de un Modelo Favar para Proyectar el Precio del Cobre," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 84-95, December.
    19. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.

    More about this item

    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
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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