Archive for November, 2019

Payam MOHAMMAD ALIHA

Ph.D candidate, Universiti Kebangsaan Malaysia (UKM), Malaysia

payammaliha@gmail.com

Tamat SARMIDI

Associate Professor Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

tamat@ukm.edu.my

Fathin FAIZAH SAID

Dr. at Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Malaysia

fatin@ukm.edu.my

Abstract

In this paper, we compare the forecasting performance of multivariate models (ARDL/VECM/DOLS/FMOLS) versus univariate models (ARIMA/ETS) for the purpose of forecasting the real demand for money in Malaysia using monthly data during 2010Q1-2018Q4. This study overcomes the issue of misspecification by incorporating financial innovation in the money demand function using separate measures of payment instruments (credit card, charge card, debit card, e-money), payment channels (Real Time Electronics Transfer of Funds and Securities or RENTAS, Interbank GIRO, Financial Process Exchange or FPX and direct debit) and payment channels (Automated Teller Machines or ATM, mobile banking) to capture the effect of financial innovations. The multivariate models which are categorized into structural models (relying on a structural relationship between money demand and other variables) are also cointegration based models meaning that variables have long-run associationship and move together in the long-run while non-structural (non-cointegration) based techniques (ARIMA and ETS model) do not rely on such a structural relationship. We conclude that structural models are better for longer term forecasting. Non-structural models (notably ARIMA) have better forecasting performance for short term horizons such as one year than they do for long term horizons. However, our findings indicate that even for short term horizons, structural models do better than non-structural models but the gap between forecasting accuracy for these two kinds of models is much narrower in the short term horizon compared to long term horizon. The results also indicate that FMOLS has the most predictive power among cointegration/structural/multivariate based models for both short (12-months) and long-time (60-months) horizons. In the context of this model (FMOLS), financial innovation have positive yet small impact on money demand in Malaysia. Finally, we do out-of-sample forecast using FMOLS.

Keywords: Malaysia, Money Demand, Financial Innovations, Multivariate, Univariate, Cointegration

JEL classification: E41, E42, E52
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NGATINDRIATUN

Departement of Management Science, Bina Nusantara University, Jakarta, Indonesia

ngatindriatun@yahoo.com

Didik Sofian HARYADI

STEKOM PAT, Semarang, Indonesia

didikshse@rocketmail.com

Abstract

This study aims to test and analyze the effects of intellectual capital to competitive advantage and company’s performance at Troso traditional weaving business. The variables in this research are human capital as exogenous variable and structural capital, customer capital, competitive advantage, and company performance as the endogenous ones. The subject of the study was 200 sample consisting 572 craftmen. This research applies structural equation modelling. The result of SEM analysis fulfills Goodness of Fit Index criteria, i.e. chi-square value = 432.543, significance probability = 0.000, RMSEA = 0.070, CMIN/DF = 1.966, TLI = 0.885, CFI = 0.900, GFI = 0.849 and AGFI = 0.810. Based on the research result, it can be concluded that human capital, structural capital, and customer capital influence on competitive advantage and company performance.

Keywords: Intellectual Capital, Competitive Advantage, Company Performance

JEL classification: A, M12, J24
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Igor DUNAYEV

Doctor of science in public administration, associate professor of Department of economic policy and management; Kharkiv regional institute of public administration of the National academy of public administration by the President of Ukraine

Igor.dunayev@gmail.com

(Corresponding author)

Mykola LATYNIN

Doctor of science in public administration, professor, head of Department of economic policy and management; Kharkiv regional institute of public administration of the National academy of public administration by the President of Ukraine

m.a.latynin@gmail.com

Yuriy ULYANCHENKO

Doctor of science in public administration, professor at Department of economic policy and management; Kharkiv regional institute of public administration of the National academy of public administration by the President of Ukraine

y.ulyanchenko@gmail.com

Alisa KOSENKO

PhD in public administration, associate professor of Department of economic policy and management; Kharkiv regional institute of public administration of the National academy of public administration by the President of Ukraine

Abstract

The article develops a methodology for applying the competence-based approach to use on a sub-national level in order to determine points of growth of a regional economy. A proprietary meta-model is proposed that links ‘to-do tasks’ for regional public authorities with essential competences. It also contains some methodological conclusions with the modernization strategy of a regional economic policy on the example of modern Ukraine, which is designed due to comparing an existing approach and approach modernized. Departed from the modernization meta-model and strongly relying on discreteness (discontinuity) as the natural property of any modernization, the authors substantiate the mechanism for overcoming undesirable discrete shifts during the modernization. Then, the relevant methods and forms of international centers of competence for the responsible development of a limited number of necessary competencies are discussed. A national network of competence centers is proposed as an optimal and sustainable way to run those centers in the future. Generally all findings were originated according a methodology on desk research, data collection and fresh expert survey of Ukrainian stakeholders.

Keywords: smart growth, regional economic policy, public policy, competencies, competence-based approach, modernization, Ukraine, mechanism

JEL classification: O38, R58, R50
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