The paper proposes a new bootstrap approach to Pesaran, Shin, and Smith’s bound tests in a conditional equilibrium correction model to overcome some typical drawbacks of the latter, such as inconclusive inference and distortion in size. The bootstrap tests are worked out under several data-generating processes, including degenerate cases. Monte Carlo simulations confirm the better performance of the bootstrap tests relative to bound ones and the asymptotic F test on the independent variables of the autoregressive distributed lag, or ARDL, model. Empirical applications highlight the importance of employing the appropriate specification and provide definitive answers to the inconclusive inference of the bound tests when exploring the long-term equilibrium relationship between economic variables.

Bertelli, S., Vacca, G., Zoia, M., Bootstrap cointegration tests in ARDL models, <<ECONOMIC MODELLING>>, 2022; 116 (1): 105987-N/A. [doi:10.1016/j.econmod.2022.105987] [https://hdl.handle.net/10807/224827]

Bootstrap cointegration tests in ARDL models

Bertelli, Stefano
Conceptualization
;
Vacca, Gianmarco
Validation
;
Zoia, Maria
Methodology
2022

Abstract

The paper proposes a new bootstrap approach to Pesaran, Shin, and Smith’s bound tests in a conditional equilibrium correction model to overcome some typical drawbacks of the latter, such as inconclusive inference and distortion in size. The bootstrap tests are worked out under several data-generating processes, including degenerate cases. Monte Carlo simulations confirm the better performance of the bootstrap tests relative to bound ones and the asymptotic F test on the independent variables of the autoregressive distributed lag, or ARDL, model. Empirical applications highlight the importance of employing the appropriate specification and provide definitive answers to the inconclusive inference of the bound tests when exploring the long-term equilibrium relationship between economic variables.
2022
Inglese
Bertelli, S., Vacca, G., Zoia, M., Bootstrap cointegration tests in ARDL models, <<ECONOMIC MODELLING>>, 2022; 116 (1): 105987-N/A. [doi:10.1016/j.econmod.2022.105987] [https://hdl.handle.net/10807/224827]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/224827
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