Instrumental variables estimation of stationary and non-stationary cointegrating regressions
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
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Volume (Year): 9 (2006)
Issue (Month): 2 (07)
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