We describe methods for solving general linear rational expectations models in continuous or discrete timing with or without exogenous variables. The methods are based on matrix eigenvalue decompositions.
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Anderson, G. (1997). Continuous-time application of the Anderson–Moore (AIM) algorithm for imposing the saddle point property in dynamic models. Unpublished manuscript, Board of Governors of the Federal Reserve System, http://www.bog.frb.fed.us/pubs/oss/oss4/papers.html
Blanchard, O. and Kahn, C.M. (1980). The solution of linear difference odels under rational expectations. Econometrica, 48, 1305–1313.
King, R.G. and Watson, M. (1997). System reduction and solution algorithms for singular linear difference systems under rational expectations. Unpublished manuscript, University of Virginia and Princeton University, http://www.people.Virginia.EDU/rgk4m/abstracts/algor.htm.
King, R.G. and Watson, M. (1998). The solution of singular linear difference systems under rational expectations. International Economic Review.
Klein, P. (1997). Using the generalized Schur form to solve a system of linear expectational difference equations. Discussion paper, IIES, Stockholm University, email@example.com.
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Sims, C.A. Solving Linear Rational Expectations Models. Computational Economics 20, 1–20 (2002). https://doi.org/10.1023/A:1020517101123
- rational expectations
- QZ decomposition
- generalized Schur decomposition