Transformation Models with Cointegrated and Deterministically Trending Regressors
Essays in Honor of Joon Y. Park: Econometric Theory
ISBN: 978-1-83753-209-4, eISBN: 978-1-83753-208-7
Publication date: 24 April 2023
Abstract
This chapter develops an asymptotic theory for a general transformation model with a time trend, stationary regressors, and unit root nonstationary regressors. This model extends that of Han (1987) to incorporate time trend and nonstationary regressors. When the transformation is specified as an identity function, the model reduces to the conventional cointegrating regression, possibly with a time trend and other stationary regressors, which has been studied in Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). The limiting distributions of the extremum estimator of the transformation parameter and the plug-in estimators of other model parameters are found to critically depend upon the transformation function and the order of the time trend. Simulations demonstrate that the estimators perform well in finite samples.
Keywords
Acknowledgements
Acknowledgments
Lin acknowledges the support of the Fundamental Research Funds for the Central Universities (China) (2021110462), the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (Grant 21CGA43), the National Natural Science Foundation of China (Grant 72203141). Tu (corresponding author) would like to thank support from National Natural Science Foundation of China (Grant 72073002, 12026607, 92046021), the Center for Statistical Science at Peking University, and Key Laboratory of Mathematical Economics and Quantitative Finance (Peking University), Ministry of Education.
Citation
Lin, Y. and Tu, Y. (2023), "Transformation Models with Cointegrated and Deterministically Trending Regressors", Chang, Y., Lee, S. and Miller, J.I. (Ed.) Essays in Honor of Joon Y. Park: Econometric Theory (Advances in Econometrics, Vol. 45A), Emerald Publishing Limited, Leeds, pp. 207-232. https://doi.org/10.1108/S0731-90532023000045A007
Publisher
:Emerald Publishing Limited
Copyright © 2023 Yingqian Lin and Yundong Tu