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Panel Index Var Models: Specification, Estimation, Testing And Leading Indicators

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Author Info
Fabio Canova (Universitat Pompeu Fabra)
Matteo Ciccarelli (Universidad de Alicante)
Abstract

This paper integrates panel VARs and the index models into a unique framework where cross unit interdependencies and time variations in the coefficients are allowed for. The setup used is Bayesian and MCMC methods are used to estimate the posterior distribution of the features of interest and to verify hypothesis concerning the model specification. The approach reduces substantially the dimensionality of the problem, can be used to construct multiunit forecasts, leading indicators and to conduct policy analysis in a multiunit setups. The methodology is employed to construct leading indicators for inflation and GDP growth in the Euro area.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2002-21.pdf
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Publisher Info
Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2002-21.

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Length: 40 pages
Date of creation: Nov 2002
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Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2002-21

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Related research
Keywords: Panel VAR Bayesian methods Leading indicators Markov Chain Monte Carlo methods.

Find related papers by JEL classification:
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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  1. Michael Binder, Cheng Hsiao, and M. Hashem Pesaran, 2001. "Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration," Computing in Economics and Finance 2001 36, Society for Computational Economics. [Downloadable!]
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  2. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin Weale, 1999. "An Automatic Leading Indicator of Economic Activity: Forecasting GDP growth for European Countries," NIESR Discussion Papers 149, National Institute of Economic and Social Research. [Downloadable!]
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  3. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August. [Downloadable!] (restricted)
  4. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1993. "Nonlinear Dynamic Structures," Econometrica, Econometric Society, vol. 61(4), pages 871-907, July. [Downloadable!] (restricted)
  5. Otrok, Christopher & Whiteman, Charles H, 1998. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
    Other versions:
  6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-95, November. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
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  8. 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. [Downloadable!] (restricted)
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  9. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
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  10. Fabio Canova & Matteo Ciccarelli, 1999. "Forecasting and Turning Point Predictions in a Bayesian Panel VAR Model," Economics Working Papers 443, Department of Economics and Business, Universitat Pompeu Fabra. [Downloadable!]
    Other versions:
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