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Allowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression

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  • Kevin D. Hoover
  • Soren Johansen
  • Katarina Juselius

Abstract

An explication of the key ideas behind the Cointegrated Vector Autoregression Approach. The CVAR approach is related to Haavelmo’s famous “Probability Approach in Econometrics” (1944). It insists on careful stochastic specification as a necessary groundwork for econometric inference and the testing of economic theories. In time-series data, the probability approach requires careful specification of the integration and cointegration properties of variables in systems of equations. The relationship between the CVAR approach and wider methodological issues and between it and related approaches (e.g., the LSE approach) are explored. The specific-to-general strategy of widening the scope of econometric models to identify stochastic trends and cointegrating relations and to nest theoretical economic models is illustrated with the example of purchasing-power parity
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Suggested Citation

  • Kevin D. Hoover & Soren Johansen & Katarina Juselius, 2008. "Allowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression," American Economic Review, American Economic Association, vol. 98(2), pages 251-255, May.
    • Handle: RePEc:aea:aecrev:v:98:y:2008:i:2:p:251-55
    Note: DOI: 10.1257/aer.98.2.251
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    References listed on IDEAS

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    1. Søren Johansen & Katarina Juselius & Roman Frydman & Michael Goldberg, 2007. "Testing Hypotheses in an I(2) Model with Applications to the Persistent Long Swings in the Dmk/$ Rate," Discussion Papers 07-34, University of Copenhagen. Department of Economics.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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