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Haavelmo's Probability Approach and the Cointegrated VAR

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  • Katarina Juselius

    (Department of Economics)

Abstract

Some key econometric concepts and problems addressed by Trygve Haavelmo and Ragnar Frisch are discussed within the general framework of a cointegrated VAR. The focus is on problems typical of time series data such as multicollinearity, spurious correlation and regression results, time dependent residuals, normalization, reduced rank, model selection, missing variables, simultaneity, autonomy and identification. Specifically the paper discusses (1) the conditions under which the VAR model represents a full probability formulation of a sample of time-series observations, (2) the plausibility of the multivariate normality assumption underlying the VAR, (3) cointegration as a solution to the problem of spurious correlation and multicollinearity when data contain deterministic and stochastic trends, (4) the existence of a universe, (5) the association between Frisch's confluence analysis and cointegrated VAR analysis, (6) simultaneity and identification when data are nonstationary, (7) conditions under which identified cointegration relations can be considered structural or autonomous, and finally (8) a formulation of a design of experiment for passive observations based on theory consistent CVAR scenarios illustrated with a monetary model for inflation.

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Bibliographic Info

Paper provided by University of Copenhagen. Department of Economics in its series Discussion Papers with number 12-01.

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Length: 8 pages
Date of creation: 01 Mar 2012
Date of revision:
Handle: RePEc:kud:kuiedp:1201

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Keywords: Haavelmo; CVAR; autonomy; identification; passive observations;

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  1. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199285679, October.
  2. Davidson, James, 1998. "Structural relations, cointegration and identification: some simple results and their application," Journal of Econometrics, Elsevier, Elsevier, vol. 87(1), pages 87-113, August.
  3. Kongsted, Hans Christian, 2005. "Testing the nominal-to-real transformation," Journal of Econometrics, Elsevier, Elsevier, vol. 124(2), pages 205-225, February.
  4. Garrone Giovanna & Marchionatti Roberto & Bellofiore Riccardo, 2004. "Keynes on econometric method. A reassessment of his debate with Tinbergen and econometricians, 1938-1943," CESMEP Working Papers, University of Turin 200401, University of Turin.
  5. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, Elsevier, vol. 33(3), pages 311-340, December.
  6. Johansen, Soren & Juselius, Katarina, 1994. "Identification of the long-run and the short-run structure an application to the ISLM model," Journal of Econometrics, Elsevier, Elsevier, vol. 63(1), pages 7-36, July.
  7. Franchi, Massimo & Jusélius, Katarina, 2007. "Taking a DSGE Model to the Data Meaningfully," Economics Discussion Papers 2007-6, Kiel Institute for the World Economy.
  8. Johansen, Soren, 1995. "Identifying restrictions of linear equations with applications to simultaneous equations and cointegration," Journal of Econometrics, Elsevier, Elsevier, vol. 69(1), pages 111-132, September.
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