Fiscal Deficit and Economic Growth in Nigeria: Ascertaining a Feasible Threshold

Aero, Oluwafadekemi; Ogundipe, Adeyemi A.

doi:10.2139/ssrn.2861505

Cited by 15 publications

(20 citation statements)

References 35 publications

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“…On the other hand, if the budget deficit is below the threshold level, it has an insignificant impact on GDP per capita. These findings are consistent with the threshold levels estimated for other developing countries, for example, 5 percent of GDP for Nigeria (Aero & Ogundipe, 2016) and 6 percent of GDP for the Gambia (Onwioduokit & Bassey, 2014). 5 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 Trans.…”

Section: Figure 2: Real and Fitted Gdp Per Capitasupporting

confidence: 89%

“…Onwioduokit and Bassey (2014) estimate the threshold level of deficit for the Gambia at 6 percent of GDP. Using a threshold autoregressive model, Aero and Ogundipe (2016) investigate the effect of a fiscal deficit on growth in Nigeria and establish a threshold level of 5 percent of GDP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model uses a continuous transition function to capture the nonlinear relationship between the variables of interest. This is unlike earlier studies, which use either a squared term to estimate the threshold level (see Ali & Ahmad, 2010;Qasim, Kemal & Siddique, in press) or the threshold model developed by Khan and Senhadji (2001) to calculate threshold inflation (see Aero & Ogundipe, 2016;Onwioduokit, 2012Onwioduokit, , 2013Onwioduokit & Bassey, 2014).…”

Section: Introductionmentioning

confidence: 95%

“…Adam and Bevan (2005) find evidence of a threshold effect at a fiscal deficit of about 1.5 percent of GDP for developing countries, indicating that fiscal deficits higher than this would hamper economic growth. Other studies find a threshold effect at significantly higher levels of the fiscal deficit -about 5-7 percent of GDP for selected countries, depending on the structure and openness of the economy, the political system and institutional arrangements (see, for example, Onwioduokit, 2012Onwioduokit, , 2013Aero & Ogundipe, 2016). This study explores the relationship between the fiscal deficit and economic growth in the case of Pakistan.…”

Section: Introductionmentioning

confidence: 98%

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The Fiscal Deficit and Economic Growth in Pakistan: New Evidence

Iqbal¹,

Din

Ghani

2017

LJE

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This study revisits the relationship between the fiscal deficit and economic growth in Pakistan to determine whether there exists a threshold fiscal deficit that might serve as a benchmark for policymakers aiming to promote growth through fiscal expansion. We apply the smooth transition autoregressive model to time-series data for the period 1972–2014. The empirical analysis shows that the threshold level of fiscal deficit is 5.57 percent of GDP, above which the deficit has a negative impact on growth. Overall, the fiscal deficit has a negative impact on economic growth, mainly because it has tended to remain above the threshold level. However, there is room for fiscal policy to promote growth, provided the fiscal deficit is kept below the threshold level and public spending is channeled into productive investments that raise the country’s long-term growth potential.

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Section: Figure 2: Real and Fitted Gdp Per Capitasupporting

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

The Fiscal Deficit and Economic Growth in Pakistan: New Evidence

Iqbal¹,

Din

Ghani

2017

LJE

View full text Add to dashboard Cite

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“…Obviously, a suitable mathematical model should be built to fit the historical curve, and the future trend is predicted according to the model trend curve. Statistical models used include the autoregressive moving average (ARMA) [23][24][25], autoregressive integrated moving average (ARIMA) [26], threshold autoregressive (TAR) model [27], and the hidden Markov model (HMM) [28]. For example, Kumarmahto et al [29] selected the ARIMA (1,1,2) model for the prediction of agriculture commodity prices.…”

Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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