Publication Type
Working Paper
Version
publishedVersion
Publication Date
3-2016
Abstract
This paper examines the limit properties of information criteria for distinguishing between the unit root model and the various kinds of explosive models. The information criteria include AIC, BIC, HQIC. The explosive models include the local-to-unit-root model, the mildly explosive model and the regular explosive model. Initial conditions with different order of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the unit root model when data come from the unit root model. When data come from the local-to-unit-root model, both BIC and HQIC select the wrong model with probability approaching 1 while AIC has a positive probability of selecting the right model in the limit. When data come from the regular explosive model or from the mildly explosive model in the form of 1+nα/n with α ϵ (0, 1), all three information criteria consistently select the true model. Indirect inference estimation can increase or decrease the probability for information criteria to select the right model asymptotically relative to OLS, depending on the information criteria and the true model. Simulation results confirm our asymptotic results in finite sample.
Keywords
Model Selection, Information Criteria, Local-to-unit-root Model, Mildly Explosive Model, Unit Root Model, Indirect Inference.
Discipline
Econometrics | Economic Theory
Research Areas
Econometrics
First Page
1
Last Page
31
Publisher
SMU Economics and Statistics Working Paper Series, No. 06-2016
City or Country
Singapore
Citation
TAO, Yubo and Jun YU.
Model selection for explosive models. (2016). 1-31.
Available at: https://ink.library.smu.edu.sg/soe_research/1801
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.