Kernel-based Inference in Time-Varying Coefficient Cointegrating Regression
Degui Li,
Peter Phillips and
Jiti Gao
Journal of Econometrics, 2020, vol. 215, issue 2, 607-632
Abstract:
This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of the data, we allow the regressors to be cointegrated and to embody a mixture of stochastic and deterministic trends, complications which result in asymptotic degeneracy of the kernel-weighted signal matrix. To address these complications new local and global rotation techniques are introduced to transform the covariate space to accommodate multiple scenarios of induced degeneracy. Under regularity conditions we derive asymptotic results that differ substantially from existing kernel regression asymptotics, leading to new limit theory under multiple convergence rates. For the practically important case of endogenous nonstationary regressors we propose a fully-modified kernel estimator whose limit distribution theory corresponds to the prototypical pure cointegration case (i.e., with exogenous covariates), thereby facilitating inference using a generalized Wald-type test statistic. These results substantially generalize econometric estimation and testing techniques in the cointegration literature to accommodate time variation and complications of co-moving regressors. Finally, Monte-Carlo simulation studies as well as an empirical illustration to aggregate US data on consumption, income, and interest rates are provided to illustrate the methodology and evaluate the numerical performance of the proposed methods in finite samples.
Keywords: Cointegration; FM-kernel estimation; Generalized Wald test; Global rotation; Kernel degeneracy; Local rotation; Super-consistency; Time-varying coefficients (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407619302210
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression (2017) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:215:y:2020:i:2:p:607-632
DOI: 10.1016/j.jeconom.2019.10.005
Access Statistics for this article
Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson
More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().