Error correction model: Difference between revisions

Revision as of 22:56, 3 November 2022

Revision as of 08:18, 10 September 2023

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The second step is then to estimate the model using [[ordinary least squares]]: <math> y_t = \beta_0 + \beta_1 x_t + \varepsilon_t </math>

If the regression is not spurious as determined by test criteria described above, [[Ordinary least squares]] will not only be valid, but in fact superalso [[consistent estimator|consistent]] (Stock, 1987).

Then the predicted residuals <math>\hat{\varepsilon_t}= y_t -\beta_0 - \beta_1 x_t </math> from this regression are saved and used in a regression of differenced variables plus a lagged error term