Abstract
In the last two decades, Nigeria’s economy has been affected by several shocks such as the 1985-86 oil price crash; 1997 Asian financial crisis; 2008-2009 global financial crisis, oil price crash that started in 2014 as well as political uncertainties in the country. Parameters of econometric models are dependent on prevailing policy and will react to policy changes. Yet, previous researches on trade-growth modelling in Nigeria had assumed parameter constancy over time. Thus, this paper constructed a time varying parameter model for trade-growth nexus and demonstrated how the model can be useful in the detection of structural breaks and outliers. The paper first demonstrated via the rolling regression method that the parameters have been time dependent and proceeded with the Kalman filter to estimate the transition of the changing parameters of the trade-growth nexus. Thereafter, it presented applications to show how the auxiliary residuals of the model can be used to detect the time of structural breaks and outliers.
I thank Tom Doan of ESTIMA for his comments when writing the RATS codes used in the analysis.
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Notes
- 1.
This section draws largely from Asemota and Saeki (2012).
- 2.
The RATS (version 8.3) ecoometric software is used for all our estimations.
- 3.
Auxiliary residuals are the residuals associated with the state or transition equation. These auxiliary residuals are estimators of the disturbances associated with the unobserved components.
- 4.
The Impact of the Global Financial Crisis on Nigerian Economy. November 28, 213. By KA Project Research. Available at www.proshareng.com.
- 5.
The plot is a graphical display of ordered residuals against their theoretical quantiles. The 45 degree line is taken as a reference line (Durbin and Koopman, 2001).
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Asemota, O.J. (2019). Kalman Filter and Structural Change Revisited: An Application to Foreign Trade-Economic Growth Nexus. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_4
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