Taofeek Olusola AYINDE , Muritala Olayemi OGUNSIJI , Kaosarat Olawunmi IBIKUNLE (2021). Twin Deficit Hypothesis and Macroeconomic Fundamentals: New Evidence from Nigeria. Quarterly Journal of Econometrics Research, 7(1): 1-12. DOI: 10.18488/journal.88.2021.71.1.12
This study tests for the validity of the twin-deficit hypothesis in Nigeria for the period 1981 – 2018 and further seeks to ascertain the role of macroeconomic fundamentals in driving this hypothesis using the non-linear autoregressive distributed lag (NARDL) model and structural vector autoregressive (SVAR) model. With evidence from granger causality test, the results obtained for the NARDL model support the validation of the twin-deficit hypothesis for the Nigerian economy. As long-run equilibrium exists, it was further established that the twin deficits were majorly driven by the degrees of financial and trade openness in Nigeria as no substantial shock effects of the twin deficits were traceable to any of the macroeconomic fundamentals. It is therefore recommended that policy makers in Nigeria should properly sequence the degree of economic openness to ensure the overall health of the economy.
The study employs the novel technique of non-linear autoregressive distributed lag (NARDL) to investigate the asymmetric relationship between fiscal and current account deficits. More so, the study distinguishes between budget deficit and fiscal deficit in estimations through the inclusion of borrowing, as a significant component of fiscal deficit in Nigeria.
Econometric Analysis of Dutch Disease Implication of China-Africa Trade
Tirimisiyu F. Oloko , Muritala O. Ogunsiji , Musefiu A. Adeleke (2021). Econometric Analysis of Dutch Disease Implication of China-Africa Trade. Quarterly Journal of Econometrics Research, 7(1): 13-30. DOI: 10.18488/journal.88.2021.71.13.30
This study revisits the analysis of the Dutch disease implication of China-Africa trade for Africa’s non-mineral resources sectors; specifically, manufacturing and agricultural sectors, while focusing on the trade relationship between China and 27 African countries for the period of 19years, 2001 to 2019. This prompted an econometric analysis with the use of two-step dynamic (difference and system) panel Generalized Method of Moment (GMM) models, which was also complemented with dynamic least squares panel econometric regression. The preliminary analysis revealed that Ethiopia is the largest African trading partner with China, with an average of about 21percent China-Ethiopia trade ratio, while Botswana has the least trade relation with China, with 1.5percent Botswana-China trade ratio. The result of our econometric analyses suggests that higher China-Africa trade has the potential to reduce Africa’s manufacturing value-added. In other words, China-Africa trade is not causing Dutch disease in Africa but has the potential to cause Dutch disease in the future. Furthermore, the result suggests that higher China-Africa trade has the potential to increase Africa’s agricultural sector productivity. This implies that China-Africa trade has no tendency of causing Dutch disease in the agricultural sector. Our results are robust to different data structures for the dynamic GMM model.
This study is one of the very few studies which have investigated the Dutch disease implication of China-Africa trade for African countries. It focused on sectoral growth rather than the aggregate growth of the economy and also employed two-step dynamic (difference and system) panel Generalized Method of Moment (GMM) models.
The Optimal Machine Learning Modeling of Brent Crude Oil Price
Chukwudi Paul Obite , Desmond Chekwube Bartholomew , Ugochinyere Ihuoma Nwosu , Gladys Ezenwanyi Esiaba , Lawrence Chizoba Kiwu (2021). The Optimal Machine Learning Modeling of Brent Crude Oil Price. Quarterly Journal of Econometrics Research, 7(1): 31-43. DOI: 10.18488/journal.88.2021.71.31.43
The price of Brent crude oil is very important to the global economy as it has a huge influence and serves as one of the benchmarks in how other countries and organizations value their crude oil. Few original studies on modeling the Brent crude oil price used predominantly different classical models but the application of machine learning methods in modeling the Brent crude oil price has been grossly understudied. In this study, we identified the optimal MLMD (MLMD) amongst the Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN) in modeling the Brent crude oil price and also showed that the optimal MLMD is a better fit to the Brent crude oil price than the classical Autoregressive Integrated Moving Average (ARIMA) model that has been used in original studies. Daily secondary data from the U.S. Energy Information Administration were used in this study. The results showed that the ANN and DNN models behaved alike and both outperformed the SVR and RF models and are chosen as the optimal MLMDs in modeling the Brent crude oil price. The ANN was also better than the classical ARIMA model that performed very poorly. The ANN and DNN models are therefore suggested for a close monitoring of the Brent crude oil price and also for a pre-knowledge of future Brent crude oil price changes.
The paper’s primary contribution is to model the Brent crude oil price using different MLMDs and to show that the optimal MLMD performs better than the classical ARIMA model used in most original studies to model the Brent crude oil price.
Inflationary Rate in Nigeria: Impact of Foreign Capital Inflows
Olabode Eric Olabisi (2021). Inflationary Rate in Nigeria: Impact of Foreign Capital Inflows. Quarterly Journal of Econometrics Research, 7(1): 44-54. DOI: 10.18488/journal.88.2021.71.44.54
As the prices of daily needs are aggravating in Nigeria, the value of the country’s currency (naira) is less appreciated on a daily basis, and this pose a threat to a good standard of living in Nigeria. Therefore, this study investigated the impact of foreign capital inflows on the persistent increase in inflation in Nigeria over the period of 1985 to 2019. The Autoregressive Distributed Lags was used to obtain the parameter estimates of the long run relationship between foreign capital inflows and inflation. By using the Forecast Error Variance Decomposition techniques, the cause-effect analysis of foreign capital inflows and inflation was determined. Results provide evidence of a long run relationship between the series. Results further indicate that inflation is sensitive to foreign capital inflows variables such as net official development assistance received and remittance inflows in Nigeria. Policies that reduce the negative impact on inflation are recommended in the body of the paper.
In the previous literature, the influence of external capital inflows on inflation across the globe had been neglected. This may be an oversight on the part of the researchers. Hence, this paper contributes to the existing literature by investigating the influence of foreign capital inflows on inflation in Nigeria.