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The panel conditionally homogenous vectorautoregressive model

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  • Georgiadis, Georgios

Abstract

In the panel conditionally homogenous vectorautoregressive model, the cross-sectional units' dynamics are generally heterogenous, but homogenous if units share the same structural characteristics. The panel conditionally homogenous vectorautoregressive model thus allows (i) to account for heterogeneity in dynamic panel data sets, (ii) to nevertheless exploit the panel nature of the data, and (iii) to analyze the relationship between the units' observed heterogeneities and structural characteristics. I show how standard least squares estimation can be applied, how impulse responses can be computed, how multivariate conditioning is implemented, and how polynomial order restrictions can be incorporated. Finally, I present an easy-to-use Matlab routine which can be used to estimate the panel conditionally homogenous vectorautoregressive model and produce impulse responses as well as forecast error variance decompositions.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 37755.

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Date of creation: 2012
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Handle: RePEc:pra:mprapa:37755

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Keywords: Panel VAR; Heterogeneity; Conditional Pooling;

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  1. Georgiadis, Georgios, 2014. "Towards an explanation of cross-country asymmetries in monetary transmission," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 66-84.
  2. Pascal Towbin & Sebastian Weber, 2011. "Limits of Floating Exchange Rates," IMF Working Papers 11/42, International Monetary Fund.
  3. Canova, Fabio & Ciccarelli, Matteo, 2006. "Estimating multi-country VAR models," Working Paper Series 0603, European Central Bank.
  4. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  5. Ciccarelli, Matteo & Peydró, José-Luis & Maddaloni, Angela, 2010. "Trusting the bankers: a new look at the credit channel of monetary policy," Working Paper Series 1228, European Central Bank.
  6. Filipa Sá & Pascal Towbin & Tomasz Wieladek, 2011. "Low interest rates and housing booms: the role of capital inflows, monetary policy and financial innovation," Globalization and Monetary Policy Institute Working Paper 79, Federal Reserve Bank of Dallas.
  7. Goodhart, Charles & Hofmann, Boris, 2008. "House Prices, Money, Credit and the Macroeconomy," Working Paper Series 0888, European Central Bank.
  8. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, 07.
  9. Norman V. Loayza & Claudio Raddatz, 2007. "The Structural Determinants of External Vulnerability," World Bank Economic Review, World Bank Group, vol. 21(3), pages 359-387, October.
  10. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
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Cited by:
  1. Kraft, Holger & Schmidt, Alexander, 2013. "Systemic risk in the financial sector: What can se learn from option markets?," SAFE Working Paper Series 25, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
  2. Georgiadis, Georgios, 2012. "Towards an explanation of cross-country asymmetries in monetary transmission," Discussion Papers 07/2012, Deutsche Bundesbank, Research Centre.

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