Publication Type
Working Paper
Version
submittedVersion
Publication Date
11-2017
Abstract
This paper proposes a quasi-Bayesian approach for structural parameters in finite-horizon life-cycle models. This approach circumvents the numerical evaluation of the gradient of the objective function and alleviates the local optimum problem. The asymptotic normality of the estimators with and without approximation errors is derived. The proposed estimators reach the semiparametric eciency bound in the general methods of moment (GMM) framework. Both the estimators and the corresponding asymptotic covariance are readily computable. The estimation procedure is easy to parallel so that the graphic processing unit (GPU) can be used to enhance the computational speed. The estimation procedure is illustrated using a variant of the model in Gourinchas and Parker (2002).
Keywords
Finite-horizon life-cycle model, Structural estimation, Quasi-Bayesian estimator, Method of simulated moment, Numerical solution, GPU computation
Discipline
Econometrics
First Page
1
Last Page
43
Embargo Period
11-12-2019
Citation
LIU, Xiaobin.
Estimating finite-horizon life-cycle models: A quasi-Bayesian approach. (2017). 1-43.
Available at: https://ink.library.smu.edu.sg/soe_research/2307
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.