Maximum Likelihood Estimation of Singular Equation Systems with Autoregressive Disturbances
Maximum likelihood estimation of equation systems with first-order autocorrelation should, in principle, take into account the first observation and associated stationarity condition. In the general case, this leads to computational difficulties compared with conventional procedures, which perhaps explains the failure of the latter to incorporate the initial observation. However, in a special case where the autoregressive process has only one parameter, which is widely used for single equation systems such as demand systems, taking the first observation into account is no more difficult than ignoring it. The paper presents empirical results of estimating a demand system with Canadian data which suggest that maximizing the full likelihood function can yield very different and more reasonable estimates than maximizing the conventional one.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1977|
|Publication status:||Published in International Economic Review, 20, 1979|
|Contact details of provider:|| Postal: Kingston, Ontario, K7L 3N6|
Phone: (613) 533-2250
Fax: (613) 533-6668
Web page: http://qed.econ.queensu.ca/
More information through EDIRC