Higher order approximations of stochastic rational expectations models

Kowal, Pawel (2007): Higher order approximations of stochastic rational expectations models.

Abstract

We describe algorithm to find higher order approximations of stochastic rational expectations models near the deterministic steady state. Using matrix representation of function derivatives instead of tensor representation we obtain simple expressions of matrix equations determining higher order terms.

• Item Type: MPRA Paper
• Language: English
• Keywords: perturbation method; DSGE models
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications

References

[1] H. Jin and K.L. Judd, Perturbation methods for general dynamic stochastic models, working paper, April 2002. [2] P. Kowal, A note on matrix differentiation, working paper, July 2007.