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.

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 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 Oil Prices and the U.S. Dollar Exchange Rate: Evidence from the Monetary Model

2018 ◽  
Vol 10 (4) ◽  
pp. 17
Author(s):  
Moayad H. Al Rasasi
Keyword(s):  
Exchange Rate ◽  
Forecast Error ◽  
Negative Relationship ◽  
Impulse Response Function ◽  
Decomposition Analysis ◽  
Oil Prices ◽  
Error Variance ◽  
Monetary Model ◽  
Forecast Error Variance ◽  
Positive Shock

This paper analyzes how changes in global oil prices affect the US dollar (USD) exchange rate based on the monetary model of exchange rate. We find evidence indicating a negative relationship between oil prices and the USD exchange rate against 12 currencies. Specifically, the analysis of the impulse response function shows that the depreciation rate of the USD exchange rate ranges between 0.002 and 0.018 percentage points as a result of a one-standard deviation positive shock to the real price of crude oil. In the same vein, the forecast error variance decomposition analysis reveals that variation in the USD exchange rate is largely attributable to changes in the price of oil rather than monetary fundamentals. In last, the out-of-sample forecast exercise indicates that oil prices enhance the predictability power of the monetary model of exchange rate.

scholarly journals KAJIAN AKTIVITAS EKONOMI LUAR NEGERI INDONESIA TERHADAP PERTUMBUHAN EKONOMI INDONESIA PERIODE 1998-2014

2017 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Ryan Hawari ◽  
Fitri Kartiasih
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Direct Investment ◽  
Forecast Error ◽  
Foreign Debt ◽  
Trade Openness ◽  
Impulse Response Function ◽  
Error Variance ◽  
Forecast Error Variance

Indonesia is a developing country which adopts an “open economic”. That caused Indonesia economic is strongly influenced by factors that come from outside of Indonesia. External factors in this research is referred to foreign debt, foreign direct investment, trade openness and exchange rate of rupiah with USD. The analytical method in this research used Vector Error Correction Model (VECM) which will focused on Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). Based on result of IRF, exchange rate had a positive effect to economic growth, while foreign debt, foreign direct investment and trade openness had a negative effect to economic growth. Based on result of FEVD, shock on economic growth in Indonesia affected by economic growth itself (43.21%), followed by foreign debt (26.30%), trade openness (14.16%), foreign direct investment (8.29%) and exchange rate (8.04%) Keywords: economic growth, trade openness, VECM, IRF, FEVD

scholarly journals Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

2009 ◽  
Vol 41 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Andrew M. McKenzie ◽  
Harold L. Goodwin ◽  
Rita I. Carreira
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Autoregressive Models ◽  
Model Specification ◽  
Decomposition Approach ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Variance Decompositions

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

scholarly journals Measuring monetary policy by money supply and interest rate: evidence from emerging economies

2021 ◽  
Vol 21 (3) ◽  
pp. 347-367
Author(s):  
Trung Thanh Bui ◽  
Kiss Dávid Gábor
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Money Supply ◽  
Emerging Economies ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Limited Effect ◽  
Forecast Error Variance

Abstract Although measuring monetary policy is a contentious issue in the literature, much less evidence on this issue is available for emerging economies. This paper aims to investigate the role of interest rate and money supply in measuring monetary policy in twelve emerging economies that target inflation through the analysis of Granger causality, impulse response function, and forecast error variance decomposition. The empirical results show that both money supply and interest rate are useful predictors for changes in inflation. Moreover, both show a comparable power to explain the variation of inflation. However, a rise in interest rate increases rather than decreases inflation, whereas money supply has a positive and expected effect on inflation. These findings suggest that interest rate may not fully capture the overall stance of monetary policy or interest rate has a limited effect on inflation.

Is Money Supply Exogenous? Evidence from India

2017 ◽  
Vol 11 (2) ◽  
pp. 167-195
Author(s):  
Santosh Kumar Dash
Keyword(s):  
Error Correction ◽  
Money Supply ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Jel Classification ◽  
Quarterly Data ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Major Factors

Against the backdrop of the claim that the rising growth rate of money is one of the major factors behind India’s recent bout of elevated and sticky inflation, this article asks: Is money supply exogenous or endogenous, and can it predict future inflation. This question is investigated using the monetarist framework of inflation. In the empirical analysis of data spanning from 1970–71 to 2009–10, the results of both the monetarist and the error-correction models suggest that money supply accounts for inflation in India. There is also the presence of an error-correction mechanism among money, inflation and output. However, a monetarist equation does not tell anything about causality. Thus, the vector autoregression (VAR) method is used to detect the direction of causality between money supply and the inflation rate. Findings from Granger causality tests suggest weak evidence of inflation (Granger) causing money supply. As a robustness check, we estimate VAR models using quarterly data and, further, using core inflation. The results of the causality tests from the quarterly data, the impulse response function and forecast error variance decomposition suggest that money supply is weakly endogenous. JEL Classification: E31, E51, E52

scholarly journals Inflation in Malaysia

2010 ◽  
Author(s):  
Wong Hock Tsen
Keyword(s):  
Forecast Error ◽  
Price Change ◽  
Impulse Response Function ◽  
Import Price ◽  
Error Variance ◽  
Variance Decomposition ◽  
Oil Price ◽  
Forecast Error Variance ◽  
The Impact

This study examines the determination of inflation in Malaysia. The results of the generalised forecast error variance decomposition show that real import price change is the most important factor in the determination of inflation. The impact of real oil price change on inflation is marginal. An increase in real oil price has a more significant impact on inflation than a decrease in real oil price. The results of the generalised impulse response function show the impact of variables examined on inflation is relatively short. There is evidence that real oil price change Granger causes inflation.  

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 DETERMINANT OF GOVERNMENT BOND YIELDS

2020 ◽  
Vol 1 (1) ◽  
pp. 86-99
Author(s):  
Priyo Adiwibowo ◽  
Pardomuan Sihombing
Keyword(s):  
Forecast Error ◽  
Impulse Response Function ◽  
Oil Prices ◽  
Error Variance ◽  
Government Bond ◽  
Bond Yields ◽  
Determinant Factors ◽  
Bank Rate ◽  
Forecast Error Variance

This study aims to analyze the influence of determinant factors: (i) exchange rates, (ii) inflation, (iii) CDS spreads, (iv) bid-ask spreads, (v) overnight rate, (vi) CB’s rate (Central Bank Rate), and (vii) oil prices on Government bond yields. The data used are monthly data in the period 2012 - 2018. The research method used is the Vector Auto Regression (VAR) approach. Our analysis indicated that the determinant factors have impact on government bond yields. Based on the analysis of the impulse response function (IRF), the yield is to respond to any shocks given by the long term. While through forecast error variance decomposition (FEVD) analysis, found that CDS spreads and oil prices contributed significantly to the movement of Government bond yields.

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