Publication Type

Book Chapter

Version

acceptedVersion

Publication Date

1-2020

Abstract

This chapter examines the limit properties of information criteria (such as AIC, BIC, and HQIC) for distinguishing between the unit-root (UR) model and the various kinds of explosive models. The explosive models include the local-to-unit-root model from the explosive side the mildly explosive (ME) model, and the regular explosive model. Initial conditions with different orders 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 UR model when data come from the UR model. When data come from the local-to-unit-root model from the explosive side, 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 ME model in the form of 1 + nα/n with α ∈ (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

Research Areas

Econometrics

Publication

Essays in honor of Cheng Hsiao

Volume

41

Editor

Li, T., Pesaran, M.H. & Terrell, D.

First Page

73

Last Page

103

ISBN

9781789739589

Identifier

10.1108/S0731-905320200000041003

Publisher

Jai Press Inc.

City or Country

Bingley

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1108/S0731-905320200000041003

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