Hello Rebecca,

Set up your your model as a bivariate VECM (use ca.jo() and create a matrix of 
your x and y variables) and invoke alrtest() on the returned object as already 
mentioned by you. See the example section of alrtest for how accomplishing this.

Best,
Bernhard
Dr. Bernhard Pfaff
Director
Global Asset Allocation

Invesco Asset Management Deutschland GmbH
An der Welle 5
D-60322 Frankfurt am Main

Tel: +49 (0)69 29807 230
Fax: +49 (0)69 29807 178
www.institutional.invesco.com
Email: bernhard_pf...@fra.invesco.com

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----- Originalnachricht -----
Von: Yuan, Rebecca [mailto:rebecca.y...@bankofamerica.com]
Gesendet: Tuesday, May 28, 2013 05:16 PM
An: R help <r-help@r-project.org>
Betreff: [R] The weak exogeneity test in R for the Error Correction Model?

Hello all,

I would like to carry out a single-equation approach of the Error Correction 
Model such as

Delta_y(t) = a + b*y(t-1) + c*x1(t-1) + d*x2(t-1) + e*delta_x1(t) + 
f*delta_x2(t) + epsilon(t)

Where, a, b, c, d, e, f are coefficients to be estimated, y is the dependent 
variable, and x1, x2 are independent variables.

For the single equation approach of ECM, there is a requirement of the weak 
exogeneity. How could I carry out the test to see if there is weak exogeneity 
in the above system?

I read the book "Bernhard-Analysis of Integrated and Cointegrated Time Series" 
where in section 8.1.3 it uses alrtest() for the weak exogeneity test. But that 
is for the vector ECM, where y is of five components, where in my example, y is 
a scalar, only one component. What would be the best way for me to test the 
weak exogeneity for the above approach ECM?

http://books.google.com/books?id=ca5MkRbF3fYC

Thanks very much!

Cheers,

Rebecca

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