Rational Expectations for Large Models: A Practical Algorithm and a Policy Application

This paper describes a practical and conceptually simple iterative method for solving large dynamic CGE models under rational expectations. Details are given for the MONASH model of Australia but the general approach could be applied to a wide range of dynamic models. The method has been automated in the RunMONASH Windows software. This software provided a natural starting point for developing an automated procedure for conducting policy analysis under rational expectations because it already performed this function for static expectations. RunMONASH was also convenient because it incorporates comprehensive user-friendly data- and solution-interrogation facilities. We provide an illustrative application in which MONASH results obtained under rational expectations for the effects of motor vehicle tariff cuts are compared with results obtained under static expectations.