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The panel conditionally homogenous vectorautoregressive model


  • Georgiadis, Georgios


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.

Suggested Citation

  • Georgiadis, Georgios, 2012. "The panel conditionally homogenous vectorautoregressive model," MPRA Paper 37755, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:37755

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    5. Charles Goodhart & Boris Hofmann, 2008. "House prices, money, credit, and the macroeconomy," Oxford Review of Economic Policy, Oxford University Press, vol. 24(1), pages 180-205, spring.
    6. Matteo Ciccarelli & Angela Maddaloni & Jose Luis Peydro, 2015. "Trusting the Bankers: A New Look at the Credit Channel of Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 979-1002, October.
    7. Abbritti, Mirko & Weber, Sebastian, 2010. "Labor market institutions and the business cycle Unemployment rigidities vs. real wage rigidities," Working Paper Series 1183, European Central Bank.
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    9. Georgiadis, Georgios, 2014. "Towards an explanation of cross-country asymmetries in monetary transmission," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 66-84.
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    Cited by:

    1. repec:ipf:psejou:v:41:y:2017:i:1:p:39-69 is not listed on IDEAS
    2. Marie-Pierre Hory, 2016. "Fiscal multipliers in Emerging Market Economies: Can we learn something from Advanced Economies?," International Economics, CEPII research center, issue 146, pages 59-84.
    3. Georgiadis, Georgios, 2014. "Towards an explanation of cross-country asymmetries in monetary transmission," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 66-84.
    4. Barrot Araya,Luis Diego & Calderon,Cesar & Serven,Luis, 2016. "Openness, specialization, and the external vulnerability of developing countries," Policy Research Working Paper Series 7711, The World Bank.
    5. 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.
    6. Mohapatra, Sandeep & Adamowicz, Wiktor & Boxall, Peter, 2016. "Dynamic technique and scale effects of economic growth on the environment," Energy Economics, Elsevier, vol. 57(C), pages 256-264.
    7. Ana Mitreska & Sultanija Bojcheva – Terzijan, 2017. "Panel Estimation of the Impact of Foreign Banks Presence on Selected Banking Indicators in Macedonia," Working Papers 2017-04, National Bank of the Republic of Macedonia.
    8. Rilind Kabashi, 2017. "Macroeconomic effects of fiscal policy in the European Union, with particular reference to transition countries," Public Sector Economics, Institute of Public Finance, vol. 41(1), pages 39-69.

    More about this item


    Panel VAR; Heterogeneity; Conditional Pooling;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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