Goodness C. Aye

The focus of this investigation was to examine the effects of energy consumption, agricultural commerce, and productivity on CO₂ emissions in Nigeria using quantile regression. Time series data from 1960 to 2021 were used. The findings revealed that the impact of agricultural raw materials imports (AGRIMs) and exports on carbon footprints is positive. There is a prevalence of a set of notable percentile differences in the conditional distribution of the variables on CO₂ emissions. Initially, the coefficient of energy consumption (EnCons) was high, but constantly nosedived from the 25th quantile until it reached the 90th quantile when it picked up again, and the same was true in the case of AGRIM. Thus, a 1% increase in agricultural imports will bring about 0.0047—a significant unit increase in CO₂ emissions in Nigeria from the 0.382946 coefficient in the 10th quantile to the 0.264392 coefficient in the 50th quantile, and thereafter, the effects become insignificant. Profound significant variance across disparate percentiles in the conditional spread of AGRIM, food production index (FPI), CPI, and FDI was found. It further showed that the effects of the regressors on carbon emissions differ over the quantiles. Overall, AGRIM and EnCons have positive and significant effects on carbon emission. However, the agricultural raw material export has significant negative effects on CO₂ emissions as the movement (transportation) of goods within a country prior to export involves a huge level of carbon release. This study provides recommendations and policy implications.

This study investigates the asymmetric effects of economic policy uncertainty (EPU) on food security in Nigeria, utilizing annual time series data from 1970 to 2021. The study used descriptive statistics, unit root tests, the nonlinear autoregressive distributed lag (NARDL) model and its associated Bounds tests to analyze the data. The analysis reveals that adult population, environmental degradation, exchange rate uncertainty (EXRU), financial deepening, food security (FS), government expenditure in agriculture uncertainty (GEAU), inflation, and interest rate uncertainty (INRU) exhibit positive mean values over the period, with varying degrees of volatility. Cointegration tests indicate a long-term relationship between EPU variables (GEAU, INRU, and EXRU) and food security. The study finds that cumulative positive and negative EPU variables have significant effects on food security in the short run. Specifically, negative GEAU, positive INRU, positive and negative EXRU have significant effects in the short run. In the long run, negative GEAU, positive and negative EXRU have significant effects on food security. Additionally, the research highlights asymmetric effects, showing that the influence of GEAU and EXRU on food security differs in the short- and long-run. The study underscores the importance of increased government expenditure on agriculture, control of exchange rate and interest rate uncertainty, and the reduction in economic policy uncertainty to mitigate risks in the agricultural sector and enhance food security. Recommendations include strategies to stabilize exchange rates to safeguard food supply and overall food security.

Given the food supply chain disruption from COVID-19 lockdowns around the world, we examine the predictive power of daily infectious diseases-related uncertainty (EMVID) on commodity traded futures within the agricultural bracket, sometimes known as the softs, using the heterogeneous autoregressive realised variance (HAR-RV) model. Considering the short-, medium-, and long-run recursive out-of-sample estimation approach, we estimate daily realised volatility by using intraday data within the 5 min interval for 15 agricultural commodity futures. During the COVID-19 episode, our results indicated that EMVID plays an important role in predicting the future path of agricultural commodity traded futures in the short, medium, and long run, i.e., h = 1, 5, and 22, respectively. According to the MSE-F test, these results are statistically significant. These results contain important implications for investors, portfolio managers, and speculators when faced with investment risk management and strategic asset allocation during infectious disease-related uncertainty.

The study examined the dynamic effect of fiscal policy on wealth inequality in middle-income countries using panel data from 2010 to 2018 and the system Generalized Method of Moments (GMM) method. Two measures of fiscal policy were considered, namely government expenses and taxes on income, profits and capital gains. GDP per capita and adult population were used as control variables. The findings of this study show that while taxes on income, profits and capital gains have a significant negative effect on wealth inequality, government expenses have no effect whatsoever on wealth inequality.

This study examined contagion involving the aggregate and regional housing markets of the United States (US) with other asset markets using multichannel tests during the 2007–2008 global financial crisis based on a unique high-frequency, i.e., daily data set. To arrive at bias free results several contagion tests: the Forbes and Rigobon (FR) correlation test for contagion, the Fry, Martin and Tang coskewness (CS) test for contagion, the Hsiao cokurtosis (CK) test for contagion and the Hsiao covolatility (CV) test for contagion were employed. At the country level, the linear (correlation) channel indicates that contagion is present from (to) average housing returns to (from) the S&P500, with the correlation contagion also running from average housing returns to REITs. Moreover, the coskewness, cokurtosis and covolatility channels are strongly active with contagion running only from average housing returns to the S&P500, bond returns and REITs. At the Metropolitan Statistical Area (MSA) level, our results indicate that the linear (correlation) channel of contagion is relatively inactive, but the coskewness, cokurtosis and covolatility channels are strongly active with contagion running mostly from housing returns to the S&P500. Our results have important implications for investor and policymakers, given the possibility of differential results based on tests and whether we rely on regional or aggregate data.

This paper decomposes the term spread into the expectation and the term premium components using a fractional integration approach and subsequently uses same with the economic policy uncertainty index to forecast the probability of recession in South Africa. We use different specifications of the probit model and quarterly data from 1990:1 to 2012:1. Our out‐of‐sample results show that the model that incorporates the expectation component and economic policy uncertainty provides the best forecast of recession. All three recession periods in our sample were accurately dictated by the prediction models and the best forecast occurred at the four quarters ahead horizon. A robustness check with a longer sample from 1946q1 to 2017q4 but excluding the factors and economic policy uncertainty due to data limitation provided justification for decomposing the term spread as the model with the expected spread turned out to be the best. We draw the implications of these findings. © 2018 John Wiley & Sons, Ltd.