Actually in a paper I reported normalized co-integrating vector coefficient instead of OLS. The reviewer commented that the power of it is very small. Can any one explain this to me? Actually I followed it from a research paper
Normalization is arbitrary. You normalize on the coeffcient of the variable that is assumed to be endogeneous. The Engle Granger (OLS) method provides a normalization a priori on the coefficient of the left hand side variable. The Johansen (ML) approach does not provide a normalization. Instead, you can normalize the coefficients after the estimation is done. So, for example, if you are interested in a money demand function, you should divide all the long run parameters by the coefficient of real money balances. Hence, normallization could lead to a better interpretation of the results.
Normallization does not lead to a change in power. Probably, the point is that the Engle Granger method is less reliable than the Johansen approach: the former distinguishes between endogenous and exogeneous variables a priori (i.e. without testing), cannot account for the fact that you may have multiple cointegrating vectors and estimates the long run relationship by ignoring short run dynamics.
you may find the answer
http://faculty.washington.edu/ezivot/econ584/notes/cointegration.pdf.
or
http://scholar.harvard.edu/files/stock/files/asymptotic_properties_of_least_squares_estimators_of_cointegrating_vectors.pdf.
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