Abstract
This paper conduct a quantitative experiment to assess the effectiveness of a hybrid perturbation-projection (HPP) method to solve dynamic stochastic general equilibrium (DSGE) asset pricing models. We employ a macro-based asset pricing model to compare HPP method and value function iteration (VFI) method based on the same reasonable calibrations. This DSGE asset pricing model in the paper incorporates nonlinearities in both household preferences and firm production technologies. Additionally, the market for debt introduces its own type of nonlinearity; leverage can force a nonlinear wedge between asset rates. In the paper, we first show how to apply the HPP method and then assess its accuracy and speed compared with the old VFI method. Accuracy of solutions is demonstrated by comparing standard deviations of the logged values of first order conditions errors of important financial assets. Speed of the solution methods is shown by comparing the computational time. By comparing the results from applying the same DSGE asset pricing model to the competing methods, we find that the HPP method is not only feasible but also robust to the extreme nonlinearities in the asset pricing model.
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Notes
In a dynamic model, f(x) is typically the solution for the next period x (i.e., \(x_{t+1}=f(x_{t})\)).
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We are grateful to Duane Graddy and Kevin Zhao for helpful comments and suggestions.
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Chen, Y., Fowler, S. Hybrid Perturbation-Projection Method for Solving DSGE Asset Pricing Models. Comput Econ 48, 649–667 (2016). https://doi.org/10.1007/s10614-015-9529-0
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DOI: https://doi.org/10.1007/s10614-015-9529-0