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Short-Term Forecasting of the Japanese Economy Using Factor Models

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  • Claudia Godbout
  • Marco J. Lombardi

Abstract

While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. To do so, we construct factor models to forecast Japanese GDP and its subcomponents, using 38 data series (including daily, monthly and quarterly variables) over the period 1991 to 2010. Overall, we find that factor models perform well at tracking GDP movements and anticipating turning points. For most of the components, we report that factor models yield lower forecasting errors than a simple AR process or an indicator model based on Purchasing Managers’ Indicators (PMIs). In line with previous studies, we conclude that the largest improvements in terms of forecasting accuracy are found for more volatile periods, such as the recent financial crisis. However, unlike previous studies, we do not find evident links between the volatility of the components and the relative advantage of using factor models. Finally, we show that adding the PMI index as an independent explanatory variable improves the forecasting properties of the factor models.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 12-7.

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Length: 35 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:bca:bocawp:12-7

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Keywords: Econometric and statistical methods; International topics;

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References

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  1. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
  2. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  3. D'Agostino, Antonello & Domenico, Giannone & Surico, Paolo, 2006. "(Un)Predictability and Macroeconomic Stability," Research Technical Papers 5/RT/06, Central Bank of Ireland.
  4. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre.
  5. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(3), pages 589-612.
  6. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series, European Central Bank 0633, European Central Bank.
  7. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005, Society for Computational Economics 431, Society for Computational Economics.
  8. Evan F. Koenig, 2002. "Using the Purchasing Managers' Index to assess the economy's strength and the likely direction of monetary policy," Economic and Financial Policy Review, Federal Reserve Bank of Dallas.
  9. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2010(1), pages 1-22.
  10. Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series, European Central Bank 1189, European Central Bank.
  11. Nikita Perevalov & Philipp Maier, 2010. "On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment," Working Papers, Bank of Canada 10-10, Bank of Canada.
  12. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank, Research Centre.
  13. Shin-ichi Fukuda & Takashi Onodera, 2001. "A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance? ," CIRJE F-Series CIRJE-F-101, CIRJE, Faculty of Economics, University of Tokyo.
  14. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 191-221, January.
  15. 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.
  16. Audrone Jakaitiene & Stephane Dees, 2012. "Forecasting the World Economy in the Short Term," The World Economy, Wiley Blackwell, vol. 35(3), pages 331-350, 03.
  17. Mototsugu Shintani, 2010. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Levine's Working Paper Archive 506439000000000168, David K. Levine.
  18. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
  19. James Rossiter, 2010. "Nowcasting the Global Economy," Discussion Papers 10-12, Bank of Canada.
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Cited by:
  1. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers, Czech National Bank, Research Department 2013/06, Czech National Bank, Research Department.
  2. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.

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