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Formula I(1) and I(2): Race Tracks for Likelihood Maximization Algorithms of I(1) and I(2) Cointegrated VAR Models

Author

Listed:
  • Jurgen A. Doornik

    (Department of Economics and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford OX1 3UQ, UK)

  • Rocco Mosconi

    (Politecnico di Milano, 20133 Milano, Italy)

  • Paolo Paruolo

    (Joint Research Centre, European Commission, 21027 Ispra (VA), Italy)

Abstract

This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness , defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research.

Suggested Citation

  • Jurgen A. Doornik & Rocco Mosconi & Paolo Paruolo, 2017. "Formula I(1) and I(2): Race Tracks for Likelihood Maximization Algorithms of I(1) and I(2) Cointegrated VAR Models," Econometrics, MDPI, vol. 5(4), pages 1-30, November.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:4:p:49-:d:119536
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    References listed on IDEAS

    as
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