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Instrumental variables estimation of stationary and non-stationary cointegrating regressions

Author

Listed:
  • P. M. Robinson
  • M. Gerolimetto

Abstract

Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least-squares estimation of cointegrating regressions between non-stationary and/or long memory stationary variables where the integration orders of regressor and disturbance sum to less than 1, as happens always for stationary regressors, and sometimes for mean-reverting non-stationary ones. Unlike in the classical situation, instruments can be correlated with disturbances and/or uncorrelated with regressors. The approach can also be used in traditional non-fractional cointegrating relations. Various choices of instrument are proposed. Finite sample performance is examined. Copyright Royal Economic Society 2006

Suggested Citation

  • P. M. Robinson & M. Gerolimetto, 2006. "Instrumental variables estimation of stationary and non-stationary cointegrating regressions," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 291-306, July.
    • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:2:p:291-306
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    Cited by:

    1. Hualde, Javier, 2014. "Estimation of long-run parameters in unbalanced cointegration," Journal of Econometrics, Elsevier, vol. 178(2), pages 761-778.
    2. Moshi, Goodiel & Mitomo, Hitoshi, 2014. "Political stability, regulation and investment in the African mobile markets," 25th European Regional ITS Conference, Brussels 2014 101430, International Telecommunications Society (ITS).
    3. Hualde, J. & Robinson, P.M., 2010. "Semiparametric inference in multivariate fractionally cointegrated systems," Journal of Econometrics, Elsevier, vol. 157(2), pages 492-511, August.
    4. Hualde Javier & Iacone Fabrizio, 2012. "First Stage Estimation of Fractional Cointegration," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-32, May.
    5. Garcia-Swartz, Daniel D. & Muhamedagić, Mensur & Saenz, Diana, 2019. "The role of prices and network effects in the growth of the iPhone platform," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 110-122.
    6. Phillips, Peter C.B., 2014. "Optimal estimation of cointegrated systems with irrelevant instruments," Journal of Econometrics, Elsevier, vol. 178(P2), pages 210-224.
    7. Moshi, Goodiel Charles & Mwakatumbula, Hilda Jacob, 2017. "Effects of political stability and sector regulations on investments in African mobile markets," Telecommunications Policy, Elsevier, vol. 41(7), pages 651-661.

    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

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