Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-27T18:19:59.219Z Has data issue: false hasContentIssue false

IV AND GMM INFERENCE IN ENDOGENOUS STOCHASTIC UNIT ROOT MODELS

Published online by Cambridge University Press:  14 August 2017

Offer Lieberman*
Affiliation:
Bar-Ilan University
Peter C.B. Phillips
Affiliation:
Yale University, University of Auckland, Southampton University, and Singapore Management University
*
*Address correspondence to Offer Lieberman, Department of Economics and Research Institute for Econometrics (RIE), Bar-Ilan University, Ramat Gan 52900, Israel; e-mail: [email protected].

Abstract

Lieberman and Phillips (2017; LP) introduced a multivariate stochastic unit root (STUR) model, which allows for random, time varying local departures from a unit root (UR) model, where nonlinear least squares (NLLS) may be used for estimation and inference on the STUR coefficient. In a structural version of this model where the driver variables of the STUR coefficient are endogenous, the NLLS estimate of the STUR parameter is inconsistent, as are the corresponding estimates of the associated covariance parameters. This paper develops a nonlinear instrumental variable (NLIV) as well as GMM estimators of the STUR parameter which conveniently addresses endogeneity. We derive the asymptotic distributions of the NLIV and GMM estimators and establish consistency under similar orthogonality and relevance conditions to those used in the linear model. An overidentification test and its asymptotic distribution are also developed. The results enable inference about structural STUR models and a mechanism for testing the local STUR model against a simple UR null, which complements usual UR tests. Simulations reveal that the asymptotic distributions of the NLIV and GMM estimators of the STUR parameter as well as the test for overidentifying restrictions perform well in small samples and that the distribution of the NLIV estimator is heavily leptokurtic with a limit theory which has Cauchy-like tails. Comparisons of STUR coefficient and standard UR coefficient tests show that the one-sided UR test performs poorly against the one-sided STUR coefficient test both as the sample size and departures from the null rise. The results are applied to study the relationships between stock returns and bond spread changes.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper is a revised version of an earlier paper entitled “IV and GMM Estimation and Testing of Multivariate Stochastic Unit Root Models.” We thank the CoEditor, Pentti Saikkonen and two referees for helpful comments and Tim Ginker for research assistance. Support from the NSF under Grant No. SES 1258258 is acknowledged by Peter C.B. Phillips. Support from the Israel Science Foundation grant No. 1082-14 and from The Pinhas Sapir Center for Development at Tel Aviv University is gratefully acknowledged by Offer Lieberman.

References

REFERENCES

Chan, N.H. & Wei, C.Z. (1987) Asymptotic Inference for nearly nonstationary AR(1) processes. Annals of Statistics 15, 10501063.CrossRefGoogle Scholar
Chao, J. & Phillips, P.C.B. (2017) Uniform Inference in Panel Autoregression. Cowles Foundation Discussion paper, Yale University.Google Scholar
Giraitis, L. & Phillips, P.C.B. (2006) Uniform limit theory for stationary autoregression. Journal of Time Series Analysis 27, 5160.CrossRefGoogle Scholar
Hayashi, F. (2000) Econometrics. Princeton University Press.Google Scholar
Ibragimov, R. & Phillips, P.C.B. (2008) Regression asymptotics using martingale convergence methods. Econometric Theory 24, 888947.CrossRefGoogle Scholar
Kwan, S.H. (1996) Firm-specific information and the correlation between individual stocks and bonds. Journal of Financial Economics 40, 6380.CrossRefGoogle Scholar
Liang, H., Phillips, P.C.B., Wang, H., & Wang, Q. (2016) Weak convergence to stochastic integrals for econometric applications. Econometric Theory 32, 13491375.CrossRefGoogle Scholar
Lieberman, O. (2012) A similarity-based approach to time-varying coefficient non-stationary autoregression. Journal of Time Series Analysis 33, 484502.CrossRefGoogle Scholar
Lieberman, O. & Phillips, P.C.B. (2014) Norming rates and limit theory for some time-varying coefficient autoregressions. Journal of Time Series Analysis 35, 592623.CrossRefGoogle Scholar
Lieberman, O. & Phillips, P.C.B. (2016) IV and GMM Inference in Estimation and Testing of Multivariate Stochastic Unit Root Models. Cowles Foundation Discussion paper no. 2061, Yale University.Google Scholar
Lieberman, O. & Phillips, P.C.B. (2017) A multivariate stochastic unit root model with an application to derivative pricing. Journal of Econometrics 196, 99110.CrossRefGoogle Scholar
Mikusheva, A. (2007) Uniform inference in autoregressive models. Econometrica 75, 14111452.CrossRefGoogle Scholar
Mikusheva, A. (2012) One-dimensional inference in autoregressive models with the potential presence of a unit root. Econometrica 80, 173212.Google Scholar
Phillips, P.C.B. (1987a) Towards a unified asymptotic theory for autoregression. Biometrika 74, 535547.CrossRefGoogle Scholar
Phillips, P.C.B. (1987b) Time series regression with a unit root. Econometrica 55, 277301.CrossRefGoogle Scholar
Phillips, P.C.B. & Solo, V. (1992) Asymptotics for linear processes. Annals of Statistics 20, 9711001.CrossRefGoogle Scholar