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Open AccessArticle

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

1
Department of Economics and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford OX1 3UQ, UK
2
Politecnico di Milano, 20133 Milano, Italy
3
Joint Research Centre, European Commission, 21027 Ispra (VA), Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Katarina Juselius
Econometrics 2017, 5(4), 49; https://doi.org/10.3390/econometrics5040049
Received: 1 July 2017 / Revised: 4 October 2017 / Accepted: 15 October 2017 / Published: 20 November 2017
(This article belongs to the Special Issue Recent Developments in Cointegration)
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. View Full-Text
Keywords: maximum likelihood; Monte Carlo; VAR; cointegration; I(1); I(2) maximum likelihood; Monte Carlo; VAR; cointegration; I(1); I(2)
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Doornik, J.A.; Mosconi, R.; Paruolo, P. Formula I(1) and I(2): Race Tracks for Likelihood Maximization Algorithms of I(1) and I(2) Cointegrated VAR Models. Econometrics 2017, 5, 49.

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