Rational Expectations for Large Models: A Practical Algorithm and a Policy Application
AbstractThis 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.
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Bibliographic InfoPaper provided by Victoria University, Centre of Policy Studies/IMPACT Centre in its series Centre of Policy Studies/IMPACT Centre Working Papers with number ip-81.
Date of creation: May 2002
Date of revision:
classifications: rational expectations algorithm; dynamic general equilibrium; tariffs; investment modelling;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
- F14 - International Economics - - Trade - - - Empirical Studies of Trade
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