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Causal Inference by Independent Component Analysis with Applications to Micro- and Macroeconomic Data

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Author Info

  • Alessio Moneta

    ()
    (Max Planck Institute of Economics)

  • Doris Entner

    ()
    (Helsinki Institute for Information Technology)

  • Patrik Hoyer

    ()
    (Helsinki Institute for Information Technology and Massachusetts Institute of Technology)

  • Alex Coad

    ()
    (Max Planck Institute of Economics)

Abstract

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure underlying the data, and show how the method can be applied to both microeconomic data (processes of firm growth and firm performance) as well as macroeconomic data (effects of monetary policy).

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Bibliographic Info

Paper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2010-031.

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Date of creation: 11 May 2010
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Handle: RePEc:jrp:jrpwrp:2010-031

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Related research

Keywords: Causality; Structural VAR; Independent Components Analysis; Non-Gaussianity; Firm Growth; Monetary Policy;

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Cited by:
  1. repec:hal:cesptp:halshs-00144415 is not listed on IDEAS
  2. Tommaso Ferraresi & Andrea Roventini & Giorgio Fagiolo, 2013. "Fiscal Policies and Credit Regimes: A TVAR Approach," Working Papers 03/2013, University of Verona, Department of Economics.
  3. Tommaso Ciarli, 2012. "Structural Interactions and Long Run Growth. An Application of Experimental Design to Agent Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 295-345.
  4. Alex Coad, 2007. "Neoclassical vs evolutionary theories of financial constraints : critique and prospectus," Documents de travail du Centre d'Economie de la Sorbonne r07008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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