Determining factors in the influx of foreign direct investment to Sudan: Implementing Autoregressive Distributed Lag (ARDL)
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The purpose of this study was to examine foreign direct investment (FDI) in the context of Sudan between the years 1990 and 2020 by using the bounds testing method of cointegration and the error-correction model.As per the results, it can be understood that the variables considered for the study are combined on a long-term basis if FDI remains a dependent variable.Further, a significant equilibrium correction was found, which supports the existence of a longterm relationship.A number of internal and external factors (especially gross domestic product (GDP), trade openness, inflation, exchange rate, and growth rate, as well as additional macroeconomic indicators such as investment/GDP) were found to impact the state of FDI in Sudan.The variables showed no causalities as per the outcomes of the Granger causality test.Moreover, as per the variance decomposition results, the forecast error variance of FDI, in addition to that of GDP, investment/GDP, and inflation, was found to be self-explanatory.Additionally, as per the impulse response functions, the results demonstrated a short-term negative association between GDP and FDI, suggesting that Sudan possesses inadequate absorptive capacity for promoting its economy using FDI.The results of this research emphasized how selective public policies can be utilized to promote FDI and consequently facilitate short-and long-term economic growth.Therefore, it is useful to recommend some policy implications to promote the application of FDI in Sudan.Keywords:
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Universal health coverage is key to the Kenyan government's ‘big four' development agenda which is meant to be to be achieved by 2022. Over the years, health has always been given a higher priority and has been at the epicenter of political campaign manifesto. As a result, the government has continuously pumped resources into the health sector, and established public insurance scheme, as well as providing an enabling environment for private insurance companies in the spirit of achieving the objectives of better health care. However, insurance penetration is low with a 4 percent uptake of the private insurance and 16 percent uptake of public insurance. This low uptake has contributed to the huge out-of-pocket budgets and expenditures in Kenya, which sums up to approximately 26.1% of the percent of the overall healthcare expenditure in Kenya. This has contributed to an increase in the level of poverty as well as dependency ratios. This research aimed to look into the determinants of health insurance demand in Kenya using the Auto-regressive Distributed Lag (ARDL) Model. The research used secondary data spanning from 1980 to 2018. The study established that, income levels positively affects health insurance demand in the long-run, the effect is however, negative in the short-run. The study established that financial development has no effect on health insurance demand. Inflation rate negatively affects health insurance demand in the long run but a positive one in the short-run. Unemployment has a negative effect on health insurance demand both in the short-run and in the long-run. Finally, education level has a positively affects health insurance demand in the long-run but a negative relationship in the short-run.
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This study examines the causes of bank distress in Nigeria using annual data from 1986 to 2015.The study employed Autoregressive Distributed Lag Model developed by Pesaran et al (2001) as the technique of data analysis.The study reveals that exchange rate and non-performing loans have positive and statistically significant impact on bank distress, while inflation and interest rate have negative and statistically insignificant effect on bank distress.The study further found that liquidity ratio exerts positive and statistically insignificant influence on bank distress.In consistent with the findings, the study recommends the followings: firstly, there should be proactive measures by the banks such as loan surveillance and monitoring.The government should strengthen the mechanisms that will create favorable and sustainable macroeconomic stability in the economy; secondly, to guarantee effective control of non-performing loans, banks should ensure that loans to be given must satisfy the requirements of banking policy and finally, the Central Bank of Nigeria must ensure that deposit money banks are operating in line with the banking policy guide lines and any bank that violate or bridge the policy should be call to order or penalized.
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This study examined the relationship between government spending on economic infrastructure and economic growth in Nigeria from 1989 to 2018.Real gross domestic product was used to proxy economic growth and was specified as a function of government spending on transport and communication, government spending on power and employment rate (as a proxy for the classical theory of labour force).The Autoregressive Distributed Lag Bounds method to cointegration was chosen to ascertain the impact and the long-run relationship between the dependent and independent variables.The short-run and long-run results showed that government spending on power exerted a positive but insignificant effect on Nigeria's RGDP.However, government spending on transport and communication had a positive relationship in the short-run but negative relationship in the long-run.Furthermore, the Causality results showed a uni-directional causality running from RGDP to GEXP and EMP to GEXTC but there was no evidence to support the existence of causality between the remaining pairs of variable.It is recommended that in order for Nigeria to achieve infrastructure development success, it is important that the government redirect excessive revenue in the maintenance of government official to these pivotal sectors of the economy with a view to monitoring the implementation after disbursing funds to the affected ones to subsequently trigger economic growth.
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Abstract We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non‐stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non‐linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio‐temporal ARDL model.
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Abstract This article studies quantile regression in an autoregressive dynamic framework with exogenous stationary covariates. We demonstrate the potential of the quantile autoregressive distributed lag model with an application to house price returns in the United Kingdom. The results show that house price returns present a heterogeneous autoregressive behaviour across the quantiles. Real GDP growth and interest rates also have an asymmetric impact on house prices variations.
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Combined with the consideration of the impact of time,GDP,population and to water consumption,it predicts planned water supply by the establishment and application of autoregressive distributed lag model through the analysis of the annual water consumption of Guangzhou city.The case study shows that it's applicable for autoregressive distributed lag model which is with high accuracy.
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