scholarly journals Testing the Relationship between Financial Inclusion, Institutional Quality and Inclusive Growth for Nigeria

2021 ◽  
Vol 1 (1) ◽  
pp. 18-28
Author(s):  
Rahman Olanrewaju Raji
Keyword(s):  
Forecast Error ◽  
Institutional Quality ◽  
Error Variance ◽  
Financial Inclusion ◽  
Inclusive Growth ◽  
Causal Relationships ◽  
Government Effectiveness ◽  
Regulatory Quality ◽  
Long Run ◽  
Forecast Error Variance

This paper examines the causal relationship between financial inclusion, institutional quality and inclusive growth within a four-variate ARDL-EC framework and forecast error variance decomposition technique for the period of 2003-2018 using quarterly data in Nigeria. The paper incorporates two variables to capture institutional quality (government effectiveness and regulatory quality) in order to eliminate variable omission bias in which most existing studies are characterised. Those adopted techniques confirm the long-run and bi-causal relationships mainly between financial inclusion and inclusive growth in Nigeria. In addition, bi-directional causal relationships of the outcome of the study are also established between financial inclusion and government effectiveness, likewise between inclusive growth and regulatory quality mainly in the short-run. The results based on the model and empirical outputs suggest that for the authorities of this economy to achieve and sustain equitable growth, fully disciplined policies that can promote and enhance financial inclusion and inclusive growth of the greater proportion of the population should not be managed and handled by loosed hands This paper examines the causal relationship between financial inclusion, institutional quality and inclusive growth within a four-variate ARDL-EC framework and forecast error variance decomposition technique for the period of 2003-2018 using quarterly data in Nigeria. The paper incorporates two variables to capture institutional quality (government effectiveness and regulatory quality) in order to eliminate variable omission bias in which most existing studies are characterised. Those adopted techniques confirm the long-run and bi-causal relationships mainly between financial inclusion and inclusive growth in Nigeria. In addition, bi-directional causal relationships of the outcome of the study are also established between financial inclusion and government effectiveness, likewise between inclusive growth and regulatory quality mainly in the short-run. The results based on the model and empirical outputs suggest that for the authorities of this economy to achieve and sustain equitable growth, fully disciplined policies that can promote and enhance financial inclusion and inclusive growth of the greater proportion of the population should not be managed and handled by loosed hands

scholarly journals Contribution of conventional bank lending for agricultural sector in Indonesia

2021 ◽  
Vol 9 (4) ◽  
pp. 331-342
Author(s):  
Hari Setia Putra ◽  
Yunnise Putri ◽  
Ali Anis ◽  
Zul Azhar
Keyword(s):  
Interest Rates ◽  
Agricultural Sector ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Bank Lending ◽  
Long Run ◽  
Negative Effect ◽  
Forecast Error Variance ◽  
Conventional Bank

This study examines the determinant contribution of conventional bank lending for the agricultural sector in Indonesia. The analysis method used in this research is the Vector Correction Model (VECM). The results showed that in the short term, there was no significant effect of the Non-Performing Loan (LogNPL), GDP of Agricultural Sector (LogPDB), and Agricultural Sector Credit Interest Rates (SBK). However, there is an effect of the LogNPL and LogPDB on the conventional bank lending for the agricultural sector in the long term. The LogNPL has a significant positive effect on the contribution of conventional bank lending to the agricultural sector. While the LogPDB has a significant negative effect on the contribution of conventional bank lending for the agricultural sector. The Impulse Response Function (IRF) analysis results show that shocks to the LogNPL respond negatively in the long run, shocks to the LogPDB respond positively in the long run, and shocks to the SBK respond negatively in the long run by conventional bank lending for the agricultural sector. Through the analysis of FEVD (Forecast Error Variance Decomposition), it is known that the biggest contribution to conventional bank lending for the agricultural sector is agricultural credit and GDP.

scholarly journals Public Expenditure and Economic Growth in India: An Empirical Analysis Using Vector Autoregression (VAR) Model

2020 ◽  
Vol 5 (2) ◽  
pp. 45-58
Author(s):  
Rashmi Gupta ◽  
Swati Shastri
Keyword(s):  
Economic Growth ◽  
Public Expenditure ◽  
Forecast Error ◽  
Granger Causality Test ◽  
Error Variance ◽  
Var Model ◽  
Impulse Responses ◽  
Causality Test ◽  
Long Run ◽  
Forecast Error Variance

Objective – The objective of this study is to test direction of causality between components of public expenditure and economic growth in India. Methodology/Technique – The paper uses annual data for the period 1980-2015. To measure public expenditure, plan expenditure and non-plan expenditure are used. The econometric methodology employed is Vector Auto regression (VAR) model. Findings – First, the stationary properties of the data were tested using Augmented Dickey-Fuller (ADF) test, Dickey-Fuller (DF) test, and the Phillip-Perron (PP) test and found that variables were non-stationary in level, but stationary in first differences. Then, Johansen- Jueslius cointegration test was employed to test the long-run association among the variables and results suggest an absence of any long-run association between plan expenditure and non-plan expenditure and economic growth in India. The Granger Causality test suggests there is unidirectional causality running from economic growth and non-plan expenditure and plan expenditure and non-plan expenditure and absence of causality public expenditure and economic growth. Novelty – The results of the Forecast Error Variance Decompositions test indicated that innovations in the variables are mostly explained by their own shocks. The impulse responses of the economic growth, plan expenditure and non-plan expenditure with respect to identified shocks are consistent with the results of Variance Decomposition Analysis. Type of Paper: Empirical. JEL Classification: O4, O49, O53. Keywords: Plan Expenditure; Non-plan Expenditure; Economic Growth; Unit Root; Cointegration Test; Granger Causality Test; Forecast Error Variance Decomposition; Impulse Responses. Reference to this paper should be made as follows: Gupta, R; Shastri, S. 2020. Public Expenditure and Economic Growth in India: An Empirical Analysis Using Vector Autoregression (VAR) Model, J. Bus. Econ. Review 5(2) 45– 58 https://doi.org/10.35609/jber.2020.5.2(1)

scholarly journals An Empirical Study of Dividend Payout and Future Earnings in Singapore

2010 ◽  
Vol 13 (02) ◽  
pp. 267-286 ◽  
Author(s):  
King Fuei Lee
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Response Analysis ◽  
Causality Test ◽  
Dividend Payout ◽  
Vector Error Correction ◽  
Future Earnings ◽  
Vector Error ◽  
Long Run ◽  
Forecast Error Variance

The main purpose of this paper is to apply Johansen's vector error-correction model (VECM) to investigate the existence of the dividend signalling effect in the Singapore aggregate market through impulse response analysis, forecast error variance decomposition and Granger-causality test. Our findings show that a unit shock increase in dividend payout leads to a permanent increase in future earnings over time. These results imply that there exists informational/signalling content in dividend payout in the Singapore market over the long run. We further find that at least half of the forecast error variance in earnings can be accounted for by innovations in the dividend payout. In addition, the payout ratio is also shown to Granger-cause earnings in the Singapore market.

scholarly journals Trade Openness Effect on Income Inequality: Empirical Evidence from Indonesia

2018 ◽  
Vol 7 (1) ◽  
pp. 1-14
Author(s):  
Lestari Agusalim ◽  
Fanny Suzuda Pohan
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Negative Impact ◽  
Forecast Error ◽  
Trade Openness ◽  
Impulse Response Function ◽  
Secondary Data ◽  
Error Variance ◽  
Long Run ◽  
Forecast Error Variance

This research analyzed the effect of international trade openness to income inequality in Indonesia using Vector Error Correction Model (VECM). The data used is the secondary data, which are the export-import value, gross domestic product (GDP), GDP per capita, open unemployment rate, and Gini index. The results of this study indicate that in the short term the trade openness has negative impact significantly on the income inequality. However, in the long-run, it does not show any significant effect in decreasing the income inequality rate. The impulse response function (IRF) concluded that income inequality gives a positive response, except on the third year. Based on the forecast error variance decomposition (FEDV), the trade openness does not provide any significant contribution in effecting the income inequality in Indonesia, but economic growth does. Nevertheless, in long-term, the economic growth makes the income inequality getting worse than in the short-term.DOI: 10.15408/sjie.v7i1.5527

scholarly journals Relationship between Technological Advancement and Agricultural Production: Evidence from India

2020 ◽  
Vol 8 (6) ◽  
pp. 2088-2094
Keyword(s):  
Agricultural Production ◽  
Forecast Error ◽  
Error Variance ◽  
Technological Advancement ◽  
Food Grains ◽  
Long Run ◽  
Forecast Error Variance ◽  
Short Run ◽  
Impact Of Technology ◽  
The Impact

Indian economy has more than 60% of the work force engaged in it and with the sectoral contribution of 17- 18% to country's GDP in 2018-19. Despite such heavy dependence and high significance of the agricultural sector, per capita productivity in agriculture over the past few decades is less in comparison to the productivity in other sectors. Available statistics shows that agricultural production has rose marginally during the period of green revolution (starting in 1960s) which was driven by the technology revolution. Technology revolution here means- ‘seed-fertiliser-water technology’ or modern technology. In the present study, a detailed time series analysis for a time period of 36 years (1981-2017) is made to study the impact of technology in production in both the short run and long run. Firstly, the present status of technology use is studied and secondly a crop-output model is considered depicting the role of technology in production of India. Here, the impact of technology is measured using variables such as gross irrigated area, Pesticide use, Synthetic nitrogen fertilizer (NPK) uses, use of improved seed varieties (HYVs) etc. and their impact upon agricultural production (food grains as well as non-food grains) is tested using various econometric tests such as Johansen Co-integration test, regression estimates etc. A composite index has been constructed using PCA method as a proxy to technology. To examine the linkage between technological advancement and agricultural production in India, we employed the Vector auto regression (VAR) model proposed by Sims. To draw inferences on the results of VAR, we also used forecast error variance decomposition (FEVD) which gives both short-run impact and long-run impact of each variable in explaining the forecast error variance of the dependent variable.

scholarly journals Exchange Rate Volatility and Monetary Policy Shocks

2022 ◽  
Author(s):  
Gbalam Peter Eze ◽  
Tonprebofa Waikumo Okotori
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Forecast Error ◽  
Exchange Rate Volatility ◽  
Error Variance ◽  
Variance Decomposition ◽  
Long Run ◽  
Forecast Error Variance ◽  
Short Run ◽  
The Exchange Rate

The study investigated the influence of innovations in monetary policy on the rate of exchange volatility in Nigeria. The research adopted vector error correction model as well as impulse response function and forecast error variance decomposition function in the estimation using two models derived in the study. Monthly data between the periods 2009 and 2019 were adopted for the research. Our findings show that in the long run; all the monetary policy variables have a significant long run correlation with volatility in the exchange rate; but that money supply and the rate of exchange seem to have significant short run impact on volatility in the exchange rate, the other variables such as liquidity ratio or monetary policy rate did not show a significant short run relationship with the volatility in the exchange rate. Further findings on the volatility impulse response and the forecast error variance decomposition suggest a significant link between volatility in the exchange rate and money supply though the link was much more pronounced. The use of monthly data shows that the managed exchange rate regime by the CBN seems to have the desired effect in exchange rate volatility and thus having a critical impact on inflationary spikes.

scholarly journals Monte Carlo Analysis of Forecast Error Variance Decompositions under Alternative Model Identification Schemes

2018 ◽  
Vol 5 (338) ◽  
pp. 115-131
Author(s):  
Anna Staszewska-Bystrova
Keyword(s):  
Monte Carlo ◽  
Forecast Error ◽  
Model Identification ◽  
Error Variance ◽  
Estimation Errors ◽  
Identification Schemes ◽  
Long Run ◽  
Forecast Error Variance ◽  
Short Run ◽  
The Impact

The goal of the paper is to investigate the estimation precision of forecast error variance decomposition (FEVD) based on stable structural vector autoregressive models identified using short‑run and long‑run restrictions. The analysis is performed by means of Monte Carlo experiments. It is demonstrated that for processes with roots close to one, selected FEVD parameters can be esti­mated more accurately using recursive restrictions on the long‑run multipliers than under recursive restrictions on the impact effects of shocks. This finding contributes to the discussion of pros and cons of using alternative identification schemes by providing counterexamples for the notion that short‑run identifying restrictions lead to smaller estimation errors than long‑run restrictions.

scholarly journals Assessing the progress of exports diversification in Saudi Arabia: growth-share matrix approach

2020 ◽  
Vol 18 (3) ◽  
pp. 118-128
Author(s):  
Mohammad Imdadul Haque
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Run ◽  
Market Shares ◽  
Forecast Error Variance ◽  
Revenue Volatility

High dependence on a particular category of exports results in fluctuations in income as the price of the export item fluctuates. In Saudi Arabia, a single category of mineral exports forms over 78% of the total exports, exposing the country to revenue volatility. The study aims to assess the magnitude of diversification of the export basket for the country. It uses data from 1984 to 2018 to study the importance of non-mineral exports in total exports. It applies Granger causality, variance decomposition, and impulse response function in the vector autoregressive framework. The study also uses the growth-share matrix to evaluate individual items of non-mineral exports. The results show a long-run relationship with a 1% increase in non-mineral exports, leading to a 0.30% increase in total exports. Non-mineral exports Granger-cause total exports. In the long run, non-mineral exports have a share of 64% of the forecast error variance in total exports. Moreover, a 1% shock in non-mineral exports creates a huge initial impact on total exports. Also, the growth rate of non-mineral products is higher than mineral products. The results indicate the importance of non-mineral exports for a predominantly oil-exporting country. Finally, the study attempts to classify its non-mineral export categories based on growth rates and market shares. Targeted emphasis on export category with a strong growth rate and low market share can be an effective strategy for further export diversification.

The influence of economic policy uncertainty and geopolitical risk on US citizens overseas air passenger travel by regional destination

2020 ◽  
pp. 135481662098119
Author(s):  
James E Payne ◽  
Nicholas Apergis
Keyword(s):  
Economic Policy ◽  
Forecast Error ◽  
Error Variance ◽  
Variance Decomposition ◽  
Air Travel ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Passenger Travel ◽  
Forecast Error Variance

This research note extends the literature on the role of economic policy uncertainty and geopolitical risk on US citizens overseas air travel through the examination of the forecast error variance decomposition of total overseas air travel and by regional destination. Our empirical findings indicate that across regional destinations, US economic policy uncertainty explains more of the forecast error variance of US overseas air travel, followed by geopolitical risk with global economic policy uncertainty explaining a much smaller percentage of the forecast error variance.

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