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Macroeconomic Forecasting with Independent Component Analysis

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  • Ruey Yau
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    Abstract

    This paper considers a factor model in which independent component analysis (ICA) is employed to construct common factors out of a large number of macroeconomic time series. The ICA has been regarded as a better method to separate unobserved sources that are statistically independent to each other. Two algorithms are employed to compute the independent factors. The first algorithm takes into account the kurtosis feature contained in the sample. The second algorithm accommodates the time dependence structure in the time series data. A straightforward forecasting model using the independent factors is then compared with the forecasting models using the principal components in Stock and Watson (2002). The results of this research can help us to gain more knowledge about the underlying economic sources and their impacts on the aggregate variables. The empirical findings suggest that the independent component method is a powerful method of macroeconomic data compression. Whether the ICA method is superior over the principal component method in forecasting the U.S. real output and inflation variables is however inconclusive

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    File URL: http://repec.org/esFEAM04/up.16628.1080763199.pdf
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    Bibliographic Info

    Paper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 741.

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    Date of creation: 11 Aug 2004
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    Handle: RePEc:ecm:feam04:741

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    Keywords: forecast; independent components; principal components;

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    1. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
    2. Singleton, Kenneth J, 1980. "A Latent Time Series Model of the Cyclical Behavior of Interest Rates," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(3), pages 559-75, October.
    3. Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
    4. Danny Quah & Thomas J. Sargent, 1992. "A dynamic index model for large cross sections," Discussion Paper / Institute for Empirical Macroeconomics 77, Federal Reserve Bank of Minneapolis.
    5. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
    6. Geweke, John F & Singleton, Kenneth J, 1981. "Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 37-54, February.
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