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International Trade Agreements and Financial System Development in Nigeria

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This study examines the short-run and long-run determinants of CAR using the Autoregressive Distributed Lag (ARDL) modelling approach, employing annual data from 2000 to 2024. The mixed order of integration among the variables, confirmed through unit root tests, justified the use of the ARDL framework. The ARDL bounds test produced an F-statistic of 22.078, surpassing the upper bound critical value at the 1% level, thereby confirming the existence of a long-run cointegrating relationship between CAR, TROR, ATR, TVC, and TRB. The short-run results revealed that TROR had a significant negative contemporaneous effect on CAR, which reversed positively in the subsequent period. ATR exhibited a consistently significant negative influence, while TVC had a positive but marginally significant effect. TRB emerged as a strong and highly significant positive determinant in both the short and long run. The error correction term (-0.355) was negative and significant, indicating that approximately 35.5% of any deviation from the long-run equilibrium is corrected annually. The long-run estimates indicated that ATR retained its significant negative impact, TRB maintained its strong positive influence, and TROR’s effect, though positive, was statistically insignificant. Diagnostic tests supported the robustness of the model, with no evidence of heteroskedasticity or functional form misspecification. The results underscore the importance of TRB-enhancing strategies, ATR cost control, and careful management of TROR to maximize long-term gains. This study contributes to the empirical literature by providing a dynamic analysis of CAR determinants within an ARDL framework, capturing both immediate and sustained effects while addressing model robustness through comprehensive diagnostic testing. The findings offer valuable insights for policymakers, industry stakeholders, and researchers seeking to improve sectoral performance and resource allocation efficiency.
Title: International Trade Agreements and Financial System Development in Nigeria
Description:
This study examines the short-run and long-run determinants of CAR using the Autoregressive Distributed Lag (ARDL) modelling approach, employing annual data from 2000 to 2024.
The mixed order of integration among the variables, confirmed through unit root tests, justified the use of the ARDL framework.
The ARDL bounds test produced an F-statistic of 22.
078, surpassing the upper bound critical value at the 1% level, thereby confirming the existence of a long-run cointegrating relationship between CAR, TROR, ATR, TVC, and TRB.
The short-run results revealed that TROR had a significant negative contemporaneous effect on CAR, which reversed positively in the subsequent period.
ATR exhibited a consistently significant negative influence, while TVC had a positive but marginally significant effect.
TRB emerged as a strong and highly significant positive determinant in both the short and long run.
The error correction term (-0.
355) was negative and significant, indicating that approximately 35.
5% of any deviation from the long-run equilibrium is corrected annually.
The long-run estimates indicated that ATR retained its significant negative impact, TRB maintained its strong positive influence, and TROR’s effect, though positive, was statistically insignificant.
Diagnostic tests supported the robustness of the model, with no evidence of heteroskedasticity or functional form misspecification.
The results underscore the importance of TRB-enhancing strategies, ATR cost control, and careful management of TROR to maximize long-term gains.
This study contributes to the empirical literature by providing a dynamic analysis of CAR determinants within an ARDL framework, capturing both immediate and sustained effects while addressing model robustness through comprehensive diagnostic testing.
The findings offer valuable insights for policymakers, industry stakeholders, and researchers seeking to improve sectoral performance and resource allocation efficiency.

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