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Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach

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  • Huang, Y-F.
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    Abstract

    This study compares several Bayesian vector autoregressive (VAR) models for forecasting price inflation and output growth in China. The results indicate that models with shrinkage and model selection priors, that restrict some VAR coefficients to be close to zero, perform better than models with Normal prior.

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    File URL: http://mpra.ub.uni-muenchen.de/41933/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 41933.

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    Date of creation: Oct 2012
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    Handle: RePEc:pra:mprapa:41933

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    Keywords: BVAR; factor model; shrinkage priors;

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    1. Mehrotra , Aaron & Sánchez-Fung, José R., 2008. "Forecasting Inflation in China," BOFIT Discussion Papers 2/2008, Bank of Finland, Institute for Economies in Transition.
    2. Korobilis, Dimitris, 2009. "VAR forecasting using Bayesian variable selection," MPRA Paper 21124, University Library of Munich, Germany.
    3. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
    4. Dimitris Korobilis, 2009. "Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models," Working Paper Series 35_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
    5. Korobilis, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," MPRA Paper 30380, University Library of Munich, Germany.
    6. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Gary Koop & Dimitris Korobilis, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Working Paper Series 47_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
    8. Philipp Maier, 2011. "Mixed Frequency Forecasts for Chinese GDP," Working Papers 11-11, Bank of Canada.
    9. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.
    10. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
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