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Article

Does Remittance in Nepal Cause Gross Domestic Product? An Empirical Evidence Using Vector Error Correction Model

1Central Department of Economics, Tribhuvan Univercity, Kathmandu, Nepal


International Journal of Econometrics and Financial Management. 2014, 2(5), 168-174
DOI: 10.12691/ijefm-2-5-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Kamal Raj Dhungel. Does Remittance in Nepal Cause Gross Domestic Product? An Empirical Evidence Using Vector Error Correction Model. International Journal of Econometrics and Financial Management. 2014; 2(5):168-174. doi: 10.12691/ijefm-2-5-1.

Correspondence to: Kamal  Raj Dhungel, Central Department of Economics, Tribhuvan Univercity, Kathmandu, Nepal. Email: kamal.raj.dhungel@gmail.com

Abstract

This study aims to investigate short and long run causality between the variable gross domestic product and remittance. The study is based on the estimation of vector error correction model. Testing the unit root and the co-integration is the basic requirement for the estimation of vector error correction model. Further, it also has estimated remittance elasticity using ordinary least square method. The finding reveals that the contribution of remittance in gross domestic product is only 0.07%. It means a 1% change in remittance will change the gross domestic product by only 0.07%. It indicates that the remittance what Nepal received from its migrants is being consumed, not saved and invested in the productive sector that can create gainful employment to the generation to come. Evidence has not support the hypothesis of remittance causes gross domestic product in the long run but there is strong evidence about the short run causality running from remittance to gross domestic product. But opposite is true in reverse order. Gross domestic product causes remittance in both short and long run.

Keywords

References

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[9]  Dhungel K.R.(2012), “ On the relationship between remittance and economic growth: Evidence from Nepal”, South Asia Journal, NJ, USA, October 2012, PP. 52-67.
 
[10]  Dhungel, K. R. (2008), “Remittance: A significant Income Source”`, The Kathmandu Post, March 16, 2008.
 
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[12]  Fayissa B, Nsiah (2008). “The impact of remittances on economic growth and development in Africa”, Department of Economics and Finance, Working paper Series, February, 2008. Online available at:http://ideas.repec.org/p/mts/wpaper/200802.html
 
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Article

Parametric Bootstrap Methods for Parameter Estimation in SLR Models

1Department of Statistics, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria


International Journal of Econometrics and Financial Management. 2014, 2(5), 175-179
DOI: 10.12691/ijefm-2-5-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Chigozie Kelechi Acha. Parametric Bootstrap Methods for Parameter Estimation in SLR Models. International Journal of Econometrics and Financial Management. 2014; 2(5):175-179. doi: 10.12691/ijefm-2-5-2.

Correspondence to: Chigozie  Kelechi Acha, Department of Statistics, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria. Email: specialgozie@yahoo.com

Abstract

The purpose of this study is to investigate the performance of the bootstrap method on external sector statistics (ESS) in the Nigerian economy. It was carried out using the parametric methods and comparing them with a parametric bootstrap method in regression analysis. To achieve this, three general methods of parameter estimation: least-squares estimation (LSE) maximum likelihood estimation (MLE) and method of moments (MOM) were used in terms of their betas and standard errors. Secondary quarterly data collected from Central Bank of Nigeria statistical bulletin 2012 from 1983-2012 was analyzed using by S-PLUS softwares. Datasets on external sector statistics were used as the basis to define the population and the true standard errors. The sampling distribution of the ESS was found to be a Chi-square distribution and was confirmed using a bootstrap method. The stability of the test statistic θ was also ascertained. In addition, other parameter estimation methods like R2, R2adj, Akaike Information criterion (AIC), Schwart Bayesian Information criterion (SBIC), Hannan-Quinn Information criterion (HQIC) were used and they confirmed that when the ESS was bootstrapped it turned out to be the best model with 98.9%, 99.9%, 84.9%, 85.4% and 86.7% respectively.

Keywords

References

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Article

Financial Contagion Crisis Effect of Subprime on G7: Evidence through the Adjusted Correlation Test and Non-linear Error Correction Models (ECM)

1Higher Institute of Business Administration Gafsa Tunisia


International Journal of Econometrics and Financial Management. 2014, 2(5), 180-187
DOI: 10.12691/ijefm-2-5-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
Mourad Hmida. Financial Contagion Crisis Effect of Subprime on G7: Evidence through the Adjusted Correlation Test and Non-linear Error Correction Models (ECM). International Journal of Econometrics and Financial Management. 2014; 2(5):180-187. doi: 10.12691/ijefm-2-5-3.

Correspondence to: Mourad  Hmida, Higher Institute of Business Administration Gafsa Tunisia. Email: hmida.mourad@yahoo.fr

Abstract

The objective of this study is to test the presence of the contagion phenomenon during the US subprime crisis. We adopt the test of adjusted correlation coefficients between markets and propose a new procedure which involves testing the non-linearity of the propagation mechanisms shocks, estimated with a model of long-term interdependence. We apply this methodology to the financial markets which measure the risk perception. Our results prove the existence of some cases of the contagion phenomenon between the financial markets of G7 countries.

Keywords

References

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Article

Risk Measurement in Commodities Markets Using Conditional Extreme Value Theory

1Business, Economics Statistics Modelling Laboratory (BESTMOD), Faculty of Economics and Management, Sfax-Tunisia


International Journal of Econometrics and Financial Management. 2014, 2(5), 188-205
DOI: 10.12691/ijefm-2-5-4
Copyright © 2014 Science and Education Publishing

Cite this paper:
Ahmed GHORBEL, Sameh SOUILMI. Risk Measurement in Commodities Markets Using Conditional Extreme Value Theory. International Journal of Econometrics and Financial Management. 2014; 2(5):188-205. doi: 10.12691/ijefm-2-5-4.

Correspondence to: Ahmed  GHORBEL, Business, Economics Statistics Modelling Laboratory (BESTMOD), Faculty of Economics and Management, Sfax-Tunisia. Email: ahmed_isg@yahoo.fr

Abstract

The aim of this paper is to quantify risk in oil, gas natural and phosphates markets by the Value at Risk and Expected Shortfull using McNeil and Frey (2000) two-steps approach based on the combination of the theory of extreme values and the GARCH model. A comparison is made between this method and various conventional methods such as GARCH models, Filtered hsitoriacal simulation, unconditional EVT-POT and unconditional EVT Bloc. Particular attention is given to study the quality of VaR forecasts obtained from conditional EVT method. The results we report show that this method is the best one for quantile superior to 99%. In all other cases, it offer acceptable VaR’s forecasts but not statistically better than GARCH methods.

Keywords

References

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Article

Determinants of Port Performance – Evidence from Major Ports in India – A Panel Approach

1Department of Commerce, Pondicherry University, Pondicherry, India


International Journal of Econometrics and Financial Management. 2014, 2(5), 206-213
DOI: 10.12691/ijefm-2-5-5
Copyright © 2014 Science and Education Publishing

Cite this paper:
T. Rajasekar, Malabika Deo. Determinants of Port Performance – Evidence from Major Ports in India – A Panel Approach. International Journal of Econometrics and Financial Management. 2014; 2(5):206-213. doi: 10.12691/ijefm-2-5-5.

Correspondence to: T.  Rajasekar, Department of Commerce, Pondicherry University, Pondicherry, India. Email: rajakudal@gmail.com

Abstract

The present study’s motivation is to identify the determinant factors for port efficiency of major ports in India during 1993 – 2011. For identifying the factors panel data models like pooled ordinary least square method, fixed effect model and random effect model are used. The hypothesis tested here is outside factors also influence port performance. The results of the study indicated that berth throughput, operating expenses and number of employees affect port efficiency in a significant positive manner, whereas cargo equipment’s and idle time shows negative but significant effect on port efficiency. From the study the inference drawn that only inside factors of the ports are the major determining factors of the performance of ports compare with outside factors.

Keywords

References

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Article

Estimation of Short and Long Run Equilibrium Coefficients in Error Correction Model: An Empirical Evidence from Nepal

1Central Department of Economics, Tribhuvan Univercity, Kathmandu, Nepal


International Journal of Econometrics and Financial Management. 2014, 2(6), 214-219
DOI: 10.12691/ijefm-2-6-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Kamal Raj Dhungel. Estimation of Short and Long Run Equilibrium Coefficients in Error Correction Model: An Empirical Evidence from Nepal. International Journal of Econometrics and Financial Management. 2014; 2(6):214-219. doi: 10.12691/ijefm-2-6-1.

Correspondence to: Kamal  Raj Dhungel, Central Department of Economics, Tribhuvan Univercity, Kathmandu, Nepal. Email: kamal.raj.dhungel@gmail.com

Abstract

This study aims to investigate the short and long run equilibrium between the electricity consumption and foreign aid of Nepalese economy during 1974-2012. Unit root test, co-integration test and finally error correction model are the econometric tools to establish the relationship between electricity consumption and foreign aid. In addition to this ordinary least square method is used to find out the foreign aid elasticity and spurious regression. The findings reveal that the variables are non-stationary at their level and they become stationary in their first difference. There are two co-integration equations showing the long run relationship between electricity consumption and foreign aid. There is short and long run equilibrium as indicated by the statistically significant coefficient of foreign aid and error correction term. The long run elasticity coefficient reveals that the 1% change in foreign aid will change the electricity consumption by 0.46%. The results of ECM indicate that there is both short and long run equilibrium in the system. The coefficient of one period lag residual is negative and significant which represent the long run equilibrium. The coefficient is -0.336 meaning that system corrects its previous period disequilibrium at a speed of 33.6% annually to reach at the steady state.

Keywords

References

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Article

Efficacité de la Méthodologie Six Sigma dans la Gestion de la Chaine Logistique

1Department of Quantitative Methods, Higher Institute of Business Administration of Gafsa, Laboratory of Modeling and Optimization for Decisional, Industrial and Logistic Systems, (MODELIS)

2Institut Supérieur de Gestion Industrielle de Sfax (ISGI), Route El – Meharza Km 1,5, B.P. No. 954, Sfax 3018, Tunisia


International Journal of Econometrics and Financial Management. 2014, 2(6), 220-235
DOI: 10.12691/ijefm-2-6-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Tarek Sadraoui, Jallouli Fayza. Efficacité de la Méthodologie Six Sigma dans la Gestion de la Chaine Logistique. International Journal of Econometrics and Financial Management. 2014; 2(6):220-235. doi: 10.12691/ijefm-2-6-2.

Correspondence to: Tarek  Sadraoui, Department of Quantitative Methods, Higher Institute of Business Administration of Gafsa, Laboratory of Modeling and Optimization for Decisional, Industrial and Logistic Systems, (MODELIS). Email: tarek_sadraoui@yahoo.fr

Abstract

Lean Six Sigma Supply Chain Management and have share commonalities in terms of process and focus on solving customer problems to achieve customer satisfaction. They also complement each other and can be integrated together. This work has extended previous work on the proposed approaches and the implementation of the Lean Six Sigma DMAIC to improve Supply Chain Management using tools. Lean Six Sigma Tools efficient movement through the Supply Chain Management, including inventories, schedules, quantities of demand, etc. Supply Chain Management can use Lean Six Sigma principles, such as focusing on added value for customers, reducing waste and deficient, streamlining the value stream, and improved delivery times. The relevance of these tools and methods generally depends on the understanding of the methods and application environment. Improving the implementation, management and performance of a supply chain are not easy tasks. However, Supply Chain Management can use the concepts and tools of Lean Six Sigma to achieve high levels of customer satisfaction in terms of cost, quality and delivery. The case study provides an example implementation of Lean Six Sigma to improve a Supply Chain. It validated the implementation and provided a description of all phases of DMAIC.

Keywords

References

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Article

Predicting Oil Production Level for Specified Cost, Price and Revenue

1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

2Department of Engineering Research Development and Production, Project Development Institute, Enugu, Nigeria

3Department of Metallurgical and Materials Engineering, Enugu State University of Science & Technology, Enugu, Nigeria

4United Bank for Africa, Lagos, Nigeria

5Robert Gordon University, Aberdeen


International Journal of Econometrics and Financial Management. 2014, 2(6), 236-242
DOI: 10.12691/ijefm-2-6-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
C. I. Nwoye, I. D. Adiele, S. E. Ede, I. H. Obiagwu, C. C. Nwoye. Predicting Oil Production Level for Specified Cost, Price and Revenue. International Journal of Econometrics and Financial Management. 2014; 2(6):236-242. doi: 10.12691/ijefm-2-6-3.

Correspondence to: C.  I. Nwoye, Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria. Email: nwoyennike@gmail.com

Abstract

This paper presents an evaluation of the trend of oil level production dependence on its operating cost, current oil price and expected revenue over a production period of 10 years (2003-2012). Results from both experiment and model prediction show that the oil production level is very significantly affected by its operating cost and expected revenue at a particular current oil price. A three-factorial model was derived, validated and used for the response analysis. Results from evaluations indicated that the standard error incurred in predicting oil production level for each value of the expected revenue & operating cost considered, as obtained from derived model and regression model were 0.0401 and 3.5 x 10-4 & 3.7348 and 3.7544% respectively. Further evaluation indicates that oil production per unit operating cost as obtained from experiment; derived model and regression model were 0.0556, 0.0552 and 0.0552 MTSB /M$ respectively. Comparative analysis of the correlations between oil production and operating cost, current oil price and expected revenue as obtained from experiment; derived model and regression model indicated that they were all > 0.99. The maximum deviation of the model-predicted oil production level (from experimental results) was less than 7%. This translated into over 93% operational confidence for the derived model as well as over 0.93 reliability response coefficients of oil production dependence to the operating cost, current oil price and expected revenue.

Keywords

References

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Article

Exchange Rate Relationship of India with Its Major Trading Partners: A Joint Testing Approach

1Ontello Consultancy, Indore, India

2International Institute of Professional Studies, DAVV, Indore, India


International Journal of Econometrics and Financial Management. 2014, 2(6), 243-252
DOI: 10.12691/ijefm-2-6-4
Copyright © 2014 Science and Education Publishing

Cite this paper:
Deepika Chandwani, Manminder Singh Saluja. Exchange Rate Relationship of India with Its Major Trading Partners: A Joint Testing Approach. International Journal of Econometrics and Financial Management. 2014; 2(6):243-252. doi: 10.12691/ijefm-2-6-4.

Correspondence to: Manminder  Singh Saluja, International Institute of Professional Studies, DAVV, Indore, India. Email: gursikh11@gmail.com

Abstract

After 1991 economic reforms, India registered tremendous fluctuation in Rupee exchange rate figures owing to its increasing trade and financial relationship with its major trading partners i.e. USA, Europe, and China. This paper examines the International parity conditions viz. Relative Purchasing Power Parity (RPPP), Covered Interest Rate Parity (CIP), Uncovered Interest Rate Parity (UIP), Fisher Effect, and Forward Rate Hypothesis, to reveal the changing financial and economic relations of India with USA, China and Europe. The major objectives of the research are to study the extent to which these International Parity conditions hold for the examined period by employing single cointegration framework and, examining the strong and weak form of parities - allowing for more channels of interaction between variables under joint modeling framework. Using Johansen cointegration test, it is argued that all the parities - except forward rate hypothesis- fail to hold in Rupee/Dollar case, reflecting that the commodity and capital markets of these two countries are not integrated. CIP and Fisher effect hold in weak form in India with respect to China and validity of weak Fisher effect with Europe indicates partial integration and openness of India to these countries. Evidence for the joint validity of strong PPP and weak CIP is reported for India - China suggesting that these parities hold when the actions of importers and exporters, and investors are combined together.

Keywords

References

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Article

Bootstrapping Normal and Binomial Distributions

1Department of Statistics, Micheal Okpara University of Agriculture, Umudike, Abia State, Nigeria


International Journal of Econometrics and Financial Management. 2014, 2(6), 253-256
DOI: 10.12691/ijefm-2-6-5
Copyright © 2014 Science and Education Publishing

Cite this paper:
Acha Chigozie Kelechi. Bootstrapping Normal and Binomial Distributions. International Journal of Econometrics and Financial Management. 2014; 2(6):253-256. doi: 10.12691/ijefm-2-6-5.

Correspondence to: Acha  Chigozie Kelechi, Department of Statistics, Micheal Okpara University of Agriculture, Umudike, Abia State, Nigeria. Email: specialgozie@yahoo.com

Abstract

This paper examines and compares the implications of bootstrapping normal and binomial distributions. Hypothetical data set was used aided by S-plus package. Data analysis, examination, and comparison were based on their correlation coefficients and the bootstrap estimate of the standard error of plug-in correlation coefficient. Evidence shows that both distributions behave very well as seen in the fundamental theory of statistics. Also as the bootstrap level increases, the binomial gives a lower correlation coefficient (-0.07258132), against -0.1355295 in normal distribution. It is pertinent to note that the correlation coefficient is steady as the bootstrap increases.The paper therefore focuses on the performance of the standard error of plug-in correlation coefficient. Result shows that the normal distribution gives lower standard error which suggests more reliability to the plug-in estimate. Thus, this study uses the bootstrap method to demonstrate a scenario where the normality assumption becomes stronger as the bootstrap sample sizes (100, 500, and 1000) gets larger but on approximation at two decimal places, both distributions give the same statistical inference (1000: 0.14). Therefore, binomial distribution is preferred to normal distribution in terms of their correlation coefficient but the normal distribution is preferred to binomial for carrying out further analysis in parameter estimation and other statistical inference in terms of their standard error of plug-in correlation coefficient.

Keywords

References

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