scholarly journals The Impact of Remittance on Trade Balance: The Case of Malaysia

2017 ◽  
Vol 3 (4) ◽  
pp. 531 ◽  
Author(s):  
Nguyen Phuc Hien

<p><em>The paper aims to examines impact of remittance on Malaysia’s trade balance in last over two decades. </em><em>Using Ordinary Least Squares (OLS) regression model based on the annual data for twenty five years period from 1990 to 2015 to test the impact of remittance on the trade balance. The results showed the remittance influence on the trade balance positively. Our findings indicate that Malaysia did not face a symptom of Dutch disease impacted by the remittance. This is not surprised due to a small remittance to Malaysia, however, the interesting is that the remittance’s fluctuation is semilar to trade balance’s one.</em></p>

Accounting ◽  
2021 ◽  
pp. 837-844
Author(s):  
Tekin Birinci ◽  
Dervis Kirikkaleli

Information and Communications Technology (ICT) has played overwhelming roles in the economic and social development of nations and continents in the last two decades. This study aims to explore the impact of mobile telephone and broadband use on economic growth in G7 countries using annual data covering the period of 2000-2017. We performed Pedroni cointegration, Kao cointegration, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and panel Granger causality tests to investigate the causal and long-run effects. The empirical findings reveal that (i) mobile telephone and broadband use contribute to economic growth in the long-run; (ii) changes in mobile telephone and broadband use significantly lead to a change in economic growth.


2018 ◽  
Vol 7 (4.10) ◽  
pp. 518
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni

This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir.  Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.  


Author(s):  
Ferdinand Thies ◽  
Sören Wallbach ◽  
Michael Wessel ◽  
Markus Besler ◽  
Alexander Benlian

AbstractInitial coin offerings (ICOs) have recently emerged as a new financing instrument for entrepreneurial ventures, spurring economic and academic interest. Nevertheless, the impact of exogenous and endogenous signals on the performance of ICOs as well as the effects of the cryptocurrency hype and subsequent downfall of Bitcoin between 2016 and 2019 remain underexplored. We applied ordinary least squares (OLS) regressions based on a dataset containing 1597 ICOs that covers almost 2.5 years. The results show that exogenous and endogenous signals have a significant effect on the funds raised in ICOs. We also find that the Bitcoin price heavily drives the performance of ICOs. However, this hype effect is moderated, as high-quality ICOs are not pegged to these price developments. Revealing the interplay between hypes and signals in the ICO’s asset class should broaden the discussion of this emerging digital phenomenon.


2017 ◽  
Vol 6 (2) ◽  
pp. 114 ◽  
Author(s):  
Tawfiq Ahmad Mousa ◽  
Abudallah. M. LShawareh

In the last two decades, Jordan’s economy has been relied on public debt in order to enhance the economic growth. As such, an understanding  of the dynamics between public debt and economic growth is very important in addressing the obstacles to economic growth. The study investigates the impact of public debt on economic growth using data from 2000 to 2015. The study employs least squares method and regression model to capture the impact of public debt on economic growth. The results of the analysis indicate that there is a negative impact of total public debt, especially the external debt on economic growth. 


2021 ◽  
Vol 10 (2) ◽  
pp. 39-56
Author(s):  
Vesna Karadžić ◽  
Nikola Đalović

Abstract The subject of research in this paper is the profitability of the biggest banks in the European financial market, some of which operate in Montenegro. The profitability of banks is influenced by a large number of factors, including internal banking and external macroeconomic factors. The aim of this paper is to use statistical and econometric methods to examine which factors and with what intensity affect the profitability of large banks in Europe. The empirical analysis used highly balanced panel models with annual data on 47 large banks from 14 European countries over the period 2013-2018. Three static panel models were estimated and evaluated (pooled ordinary least squares, model with fixed effects and model with random effects), as well as dynamic model utilizing general methods of moments. The POLS model was chosen as the best, confirming that all macroeconomic factors have a statistically significant impact on the profitability of big banks, while the impact of internal factors, which are controlled by the bank’s management, is not significant. GDP growth rate, inflation rate and market concentration have a positive effect on profitability, while the membership of the European Union has a negative impact on profit, meaning that banks with headquarters outside the EU are more profitable.


Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 174
Author(s):  
Khalid Eltayeb Elfaki ◽  
Rossanto Dwi Handoyo ◽  
Kabiru Hannafi Ibrahim

This study aimed to scrutinize the impact of financial development, energy consumption, industrialization, and trade openness on economic growth in Indonesia over the period 1984–2018. To do so, the study employed the autoregressive distributed lag (ARDL) model to estimate the long-run and short-run nexus among the variables. Furthermore, fully modified ordinary least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegrating regression (CCR) were used for a more robust examination of the empirical findings. The result of cointegration confirms the presence of cointegration among the variables. Findings from the ARDL indicate that industrialization, energy consumption, and financial development (measured by domestic credit) positively influence economic growth in the long run. However, financial development (measured by money supply) and trade openness demonstrate a negative effect on economic growth. The positive nexus among industrialization, financial development, energy consumption, and economic growth explains that these variables were stimulating growth in Indonesia. The error correction term indicates a 68% annual adjustment from any deviation in the previous period’s long-run equilibrium economic growth. These findings provide a strong testimony that industrialization and financial development are key to sustained long-run economic growth in Indonesia.


Author(s):  
Thomas Appiah ◽  
Frank Bisiw

The economic development of any nation hinges on the health of its financial system. In recent years, the health of the Ghanaian Banking sector has been affected severely as a result of high levels of non-performing loans (NPLs), which has been identified as a major threat to the overall profitability and survival of banks. To minimize the impact of NPLs on the financial sector, key stakeholders such as the government, bank officials and regulators are working hard in that regard. However, any policy response aimed at dealing with the high rate of non-performing loans first requires the understanding of the underlying determinants of NPLs. Against this backdrop, this paper apply panel co-integration techniques to investigate the determinants of credit risk (NPLs) in the banking sector of Ghana.  We use NPL as a proxy to measure credit risk and assess how it is influenced by macroeconomic and bank-specific factors. A balanced panel data of 16 universal banks in Ghana from 2010 to 2016 has been analyzed using Panel co-integration techniques such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS). Our result shows that growth in the economy, measured by Gross Domestic Product (GDP) has significant influence on the NPLs of banks in the long-run. The results further revealed that capital adequacy, profitability and liquidity of banks are significant predictors of NPLs. However, our results suggest that bank size, inflation and interest rate have statistically insignificant influence on the NPLs of Ghanaian banks. The study recommend, among others, that whereas it is important for government and policymakers to work to improve macroeconomic outcomes, banks should also improve their capital adequacy, profitability, and efficiency position as these bank-specific interventions could significantly improve credit quality and minimize NPLs.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ömer Esen ◽  
Gamze Yıldız Seren

PurposeThis study aims to empirically examine the impact of gender-based inequalities in both education and employment on economic performance using the dataset of Turkey for the period 1975–2018.Design/methodology/approachThis study employs Johansen cointegration tests to analyze the existence of a long-term relation among variables. Furthermore, dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) estimation methods are performed to determine the long-run coefficients.FindingsThe findings from the Johansen cointegration analysis confirm that there is a long-term cointegration relation between variables. Moreover, DOLS and FMOLS results reveal that improvements in gender equality in both education and employment have a strong and significant impact on real gross domestic product (GDP) per capita in the long term.Originality/valueThe authors expect that this study will make remarkable contributions to the future academic studies and policy implementation, as it examines the relation among the variables by including the school life expectancy from primary to tertiary based on the gender parity index (GPI), the gross enrollment ratio from primary to tertiary based on GPI and the ratio of female to male labor force participation (FMLFP) rate.


2018 ◽  
Vol 7 (4.10) ◽  
pp. 543
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni

This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.  


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