Publication Type
Journal Article
Version
submittedVersion
Publication Date
8-2014
Abstract
In regressions involving integrable functions we examine the limit properties of instrumental variable (IV) estimators that utilise integrable transformations of lagged regressors as instruments. The regressors can be either I(0) or nearly integrated (NI) processes. We show that this kind of nonlinearity in the regression function can significantly affect the relevance of the instruments. In particular, such instruments become weak when the signal of the regressor is strong, as it is in the NI case. Instruments based on integrable functions of lagged NI regressors display long range dependence and so remain relevant even at long lags, continuing to contribute to variance reduction in IV estimation. However, simulations show that ordinary least square (OLS) is generally superior to IV estimation in terms of mean squared error (MSE), even in the presence of endogeneity. Estimation precision is also reduced when the regressor is nonstationary.
Keywords
Instrumental variables, Integrable function, Integrated process, Invariance principle, Local time, Mixed normality, Nonlinear cointegration, Stationarity, Unit roots, Weak instruments
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Reviews
Volume
33
Issue
5-6
First Page
676
Last Page
712
ISSN
0747-4938
Identifier
10.1080/07474938.2013.825181
Publisher
Taylor & Francis: STM, Behavioural Science and Public Health Titles
Citation
Kasparis, Ioannis; Peter C. B. PHILLIPS; and Magdalinos, Tassos.
Nonlinearity Induced Weak Instrumentation. (2014). Econometric Reviews. 33, (5-6), 676-712.
Available at: https://ink.library.smu.edu.sg/soe_research/1837
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.
Additional URL
https://doi.org/10.1080/07474938.2013.825181