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Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies

The primary objective of this paper is to propose two nonlinear extensions for macroeconomic forecasting using large datasets. First, we propose an alternative technique for factor estimation, i.e., kernel principal component analysis, which allows the factors to have a nonlinear relationship to the input variables. Second, we propose artificial neural networks as an alternative to the factor augmented linear forecasting equation. These two extensions allow us to determine whether, in general, there is empirical evidence in favor of nonlinear methods and, in particular, to verify whether the nonlinearity occurs in the estimation of the factors or in the functional form that links the target variable to the factors. In an effort to verify the empirical performances of the methods proposed, we conducted several pseudo forecasting exercises on the industrial production index and consumer price index for the Euro area and US economies. These methods were employed to construct the forecasts at 1-, 3-, 6-, and 12-month horizons using a large dataset containing 259 predictors for the Euro area and 131 predictors for the US economy. The results obtained from the empirical study suggest that the estimation of nonlinear factors, using kernel principal components, significantly improves the quality of forecasts compared to the linear method, while the results for artificial neural networks have the same forecasting ability as the factor augmented linear forecasting equation.

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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 255.

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Length: 30 pages
Date of creation: 08 Nov 2012
Date of revision: 08 Nov 2012
Handle: RePEc:rtv:ceisrp:255
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  1. Mototsugu Shintani, 2003. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Vanderbilt University Department of Economics Working Papers 0322, Vanderbilt University Department of Economics, revised Apr 2004.
  2. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
  3. 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.
  4. D'Agostino, Antonello & Giannone, Domenico, 2007. "Comparing Alternative Predictors Based on Large-Panel Factor Models," CEPR Discussion Papers 6564, C.E.P.R. Discussion Papers.
  5. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  6. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers.
  7. Altissimo, Filippo & Cristadoro, Riccardo & Forni, Mario & Lippi, Marco & Veronese, Giovanni, 2006. "New EuroCOIN: Tracking Economic Growth in Real Time," CEPR Discussion Papers 5633, C.E.P.R. Discussion Papers.
  8. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, 03.
  9. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre.
  10. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  11. Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, School of Economics and Management, University of Aarhus.
  12. White, Halbert, 2006. "Approximate Nonlinear Forecasting Methods," Handbook of Economic Forecasting, Elsevier.
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