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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.
IIARD Publication Co.
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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