Bootstrapping the autoregressive distributed lag test for cointegration
By R. Mcnown, Chung Yan Sam, S. Goh. 2018
533 citations recorded by the source corpus.
ABSTRACT We propose a bootstrap autoregressive-distributed lag (ARDL) test. By applying the appropriate bootstrap method, some weaknesses underlying the Pesaran, Shin and Smith ARDL bounds test are addressed including size and power properties and the elimination of inconclusive inferences. In addition, inferences based solely on the significance of the F-test and single t-test from the ARDL bounds test are not sufficient to avoid degenerate cases. The bootstrap ARDL test provides an additional test on the significance of coefficients on lagged levels of the regressors, which provides a better insight into the cointegration status of the model.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/066885d1e16e32069eafa343cd7c214bc80ce6e2
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In Economics and Econometrics
- Zhilun Jiao
- Chandrashekar Raghutla
- Hashim Zameer
- M. Song
- Bibhuti Sarker
- F. Khan
- Muhammad Shahbaz
- Avik Sinha
Citations in the corpus
- Environmental degradation in France: The effects of FDI, financial development, and energy innovations (2018, unknown): “However, employing the bootstrapping ARDL approach proposed by McNown et al. (2018) can provide the critical values for all three tests. Concurrently, in our endeavor to provide the empirically robust results, in this study we employed the critical values tabulated by McNown et al. (2018).”
- Nexus between foreign direct investment and economic growth in Bangladesh: an augmented autoregressive distributed lag bounds testing approach (2020, methodology): “To avoid the degenerate case 1 identified by Pesaran et al. (2001), McNown et al. (2018) proposed an additional t-test or F-test on the lagged independent variables to complement the ARDL test by Pesaran et al. (2001).”
- Public-private partnerships investment in energy as new determinant of CO2 emissions: The role of technological innovations in China (2020, methodology): “We consider the bootstrapping ARDL (BARDL) cointegration technique lately developed by McNown et al. (2018) to evaluate the cointegration relationship between the variables. This methodology has main advantage is that it addresses the problem of weak size as well as power properties encountered in t”