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During the bound testing of these 3 models, we ran each model by considering each variable as the dependent variable to investigate the long-run cointegration.
Once the long-run cointegration was confirmed by the bound testing when economic growth was the dependent variable (Table 6), we proceeded to estimate the long elasticities by using the ADRL model's long equation.
The empirical evidence obtained from a bound testing approach in the study of Peker and Mercan (2011) shows that the inflationary effect of oil products price increases is positive and statistically significant in the long term.
Finally, we used the newly developed ARDL bound testing to investigate the long-run association.
Finally, we are using newly developed ARDL bound testing for investigating long-run association.
To capture the influence of financial innovation on economic growth, we estimated the long-run cointegration by applying Autoregressive Distributed Lag (ARDL) bound testing and Granger causality-based Error Correction Model (ECM) to capture the directional association.
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The bound test for examining the long run association among variables can be conducted using F tests.
The basic testing procedure requires four steps: ADF and PP unit root tests, Bound tests for Co-integration, ARDL approach and VECM method.
This paper thus confirms the existence of a positive and long-term impact of all the indicators of financial development on economic growth through bound test.
The table above confirms the bound test results with a positive and significant long run relationship between the financial development indicators and economic growth.
With information on the lag length, it is now possible to verify if the variables are co-integrated using the bound test technique.
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CEO of Professional Science Editing for Scientists @ prosciediting.com