Publication Type

Journal Article

Version

Preprint

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1080/07474938.2013.825181

Included in

Econometrics Commons

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