scholarly journals Meninjau Peran Sektor Manufaktur dan Komunikasi Sebagai Mesin Pertumbuhan Ekonomi Indonesia

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Yunarwanto Yunar
Keyword(s):  
Economies Of Scale ◽  
Time Series Data ◽  
Manufacturing Sector ◽  
Spillover Effects ◽  
Added Value ◽  
Series Data ◽  
Economic Activities ◽  
Alternative Agent ◽  
Communication Sector ◽  
Manufacturing Productivity

The study carry out to examines whether the manufacturing sector is still an engine of growth, and examines the communication sector whether it could be the other growth engine through the linkages and spillover effects in several sectoral activities, using time series data on employment levels in Indonesia. The results showed that, the growth of the nine tested sectors was strongly influenced by the manufacturing. The communication sector with growing productivity, shows it can be a future alternative agent as  it’s also has strong links to growth. While the infrastructure sector has a smaller thrust influence since its charcteristic having very large economies of scale, non linear impact, and its effect on productivity and growth will only after the investment threshold reached. Research also captures manufacturing productivity tend to weakened, showing that Indonesia is trapped in product specialization with low labor productivity. To be more developed, Indonesia needs to diversify its economy focus on manufacturing and economic activities with high added value and more productive production processes.

scholarly journals The Spillover Effects on Strategic Interdependence Neighboring Countries: A Game Theory Approach

2021 ◽  
Author(s):  
Oyundelger Sharkhuu ◽  
Pu Yongjian ◽  
Batdelger Tsogt-Ochir ◽  
Tugs Sanjdorj
Keyword(s):  
Game Theory ◽  
Time Series Data ◽  
Spillover Effects ◽  
Three Dimensions ◽  
Series Data ◽  
Theory Approach ◽  
Vector Error Correction ◽  
Long Run ◽  
Inbound Tourism ◽  
Short Run

The main aim of sustainable development is to ensure an intelligible and long-lasting balance between the economy, society, and the environment. Sustainable tourism could only be successful if the inter-relationships between all three dimensions are accepted. In the limited number of research analyses, the focus of the research is on competition between tourist countries and destinations. This study has used Game theory to analyze the competition applies time-series data in selected neighboring countries measure of a VAR-based spillover index, developed by [1] to investigate the time-varying relationship between tourism and Gross Domestic Product. Each country analyzed Vector Error Correction (VEC) and Granger analysis to explore the causal short and long-term tourism and use a sample that spans from 1997 to 2019. From the main results of Cholesky, the total spillover index is 59.0% between Russia and Mongolia which suggests a moderate interdependence among the four variables. Findings indicate that neither China nor Mongolia have a short-run influence on tourism development. China's inbound tourism is affected in the long run by Mongolia's inbound tourism but not vice versa can be explained by the fact that the number of tourists visiting Mongolia would include China in their travels.

scholarly journals Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shanglei Chai ◽  
Zhen Zhang ◽  
Mo Du ◽  
Lei Jiang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Spillover Effects ◽  
Series Data ◽  
Dominant Source ◽  
Financial Internationalization

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

scholarly journals Impact of Food Contamination on Brands: A Demand Systems Estimation of Peanut Butter

2012 ◽  
Vol 41 (3) ◽  
pp. 327-339 ◽  
Author(s):  
Rafael Bakhtavoryan ◽  
Oral Capps ◽  
Victoria Salin
Keyword(s):  
Time Series Data ◽  
Peanut Butter ◽  
Food Contamination ◽  
Spillover Effects ◽  
Series Data ◽  
Peter Pan ◽  
Price Elasticities ◽  
Food Borne ◽  
Before And After ◽  
Food Borne Illness

A 2007 food-borne illness incident involving peanut butter is linked with structural change in consumer demand. Compensated and uncompensated own- and cross-price elasticities and expenditure elasticities were calculated for leading brands before and after the product recall using the Barten synthetic model and weekly time-series data from 2006 through 2008. Statistically significant differences in price elasticities for the affected brand, Peter Pan, were absent. After a period of 27 weeks, this brand essentially recovered from the food safety crisis. Significant differences in price elasticities were evident among non-affected brands. Hence, spillover effects and heightened competition are associated with the recall.

scholarly journals Status of Sewage Water Generation in Karnataka, India

2019 ◽  
pp. 1-5
Author(s):  
V. S. Radhika ◽  
G. N. Kulkarni
Keyword(s):  
Population Growth ◽  
Time Series Data ◽  
Critical Factor ◽  
High Water ◽  
Sewage Water ◽  
Series Data ◽  
Twin Cities ◽  
Economic Activities ◽  
Living Organisms ◽  
Future Population

Water is vital to the existence of all living organisms, but this valuable resource is increasingly being threatened with increasing population growth and demand for high water quality for both domestic purposes and economic activities. A critical factor in the estimation of waste water generation is the population growth. The population of the Hubli-Dharwad twin cities is the second-largest in Karnataka, after Bangalore. The present study was based on secondary (time series) data. The population and sewage water flow in twin cities was found to have increased almost nearly about twelve times with the growth rate of 1.07 percent per annum. The projected future population and sewage water generation from twin cities for three decadal points of time showed an increasing trend. This poses a challenging task in future with respect to management. The farmers consider the resource as a boon which provides water for irrigation throughout the year and serves as a source of income and employment.

scholarly journals Banking Sector Consolidation: Possible Effects for Sectoral Credit Allocation and Economic Growth in Nigeria

2017 ◽  
Vol 3 (01) ◽  
Author(s):  
Alwell Nteegah
Keyword(s):  
Economic Growth ◽  
Banking Sector ◽  
Oil And Gas ◽  
Time Series Data ◽  
Manufacturing Sector ◽  
Percent Level ◽  
Job Creation ◽  
Series Data ◽  
Credit Allocation ◽  
Banking Credit

This study investigated possible effects of banking sector consolidation- credit allocation to selected sectors on the growth of Nigerian economy. utilizing time series data on growth rate of GDP, banking sector credit distribution to the agriculture, manufacturing, oil and gas/mining, commercial (export financing) sectors and bank size (number of Deposit money bank branches) for the period 1981 - 2015 and employing Vector Error Correction Model (VECM), the results indicate that only banking sector credit allocation to the manufacturing sector is positive and significant at 5 percent level. Banking credit to agriculture, oil & gas/mining, commercial and bank size were all insignificant at 5 percent level. This result revealed that funds allocated to the manufacturing sector spurred economic growth in Nigeria during the duration of this investigation. Other finding of study shows that the manufacturing sector has higher propensity for increasing investment, job creation and value addition hence attracts funds from the banks than other sectors. Based on these findings, the paper suggested creation of enabling environment and enactments of policies that will enhance higher credit allocation to manufacturing sector in particular and the real sector in general in order to spur investment, job creation and stimulate economic growth in Nigeria.

scholarly journals Economic Growth, Inflation and Monetary Policy in Pakistan: Preliminary Empirical Estimates

2021 ◽  
Vol 4 (2) ◽  
pp. 321-333
Author(s):  
Hina Ali ◽  
Malka Liaquat ◽  
Noreen Safdar ◽  
Saeed ur Rahman
Keyword(s):  
Time Series ◽  
Monetary Policy ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Economic Activities ◽  
Data Set ◽  
Explanatory Variables ◽  
Empirical Estimates

In economic policy, construction Inflation is a core variable to be considered that determines the economic activity. To make a suitable monetary policy, it is very essential to check the price level and later on, many other variables are considered to achieve the goal. This study aims to reveal the affiliation of inflation on the growth of economic activities in Pakistan. Time series data set for the period 1989-2020 was used to have the empirical estimates.  Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag techniques are used for empirical estimates. The present research uses Inflation as a dependent variable and Gross Domestic Product, Interest Rate, Money Supply, and Exchange Rate as the explanatory variables of the study. The findings of this analysis reveal that there's an antagonistic relation between Inflation and GDP.

scholarly journals Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 99
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Scale ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Economic Recession ◽  
Series Data ◽  
Economic Activities ◽  
Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

scholarly journals Total Factor Productivity Growth in Indian Agriculture

2010 ◽  
Vol 6 (4) ◽  
pp. 286-298 ◽  
Author(s):  
Amarnath Tripathi
Keyword(s):  
Total Factor Productivity ◽  
Economies Of Scale ◽  
Time Series Data ◽  
Market Access ◽  
Output Growth ◽  
Series Data ◽  
Factor Productivity ◽  
Indian Agriculture ◽  
The People ◽  
Future Growth

n this study, time series data has been related to broad agricultural outputs which included farming, livestock, forestry, and fisheries and 3 conventional inputs: labour, land, and capital, to construct an index of total factor productivity (TFP) between 1969-70 to 2005-06. A TFP index is simply the ratio of an output index to an input index. Therefore, growth in TFP is the residual share of output growth after accounting for changes in land, labor, and other conventional agricultural inputs. Changes in TFP can be interpreted as a measure of the collective contribution of non-conventional inputs in agriculture, such as improvements in input quality, market access, economies of scale, and technology. What emerges from this exercise is a picture that raises concern about future growth in Indian agriculture, and the welfare of the people who depend on agriculture for their livelihood. Agricultural productivity in India appeared to stagnate in the late 1990s after enjoying two decades of rapid growth.

scholarly journals Pendekatan Regresi Data Panel untuk Pemodelan Jumlah Angkatan Kerja dan Penanaman Modal Luar Negeri terhadap PDRB Provinsi di Indonesia

2019 ◽  
Vol 1 (2) ◽  
pp. 100
Author(s):  
Muhammad Syukron ◽  
Hafidz Muhammad Fahri
Keyword(s):  
Panel Data ◽  
Labor Force ◽  
Foreign Investment ◽  
Time Series Data ◽  
Series Data ◽  
Economic Activities ◽  
Cross Sectional ◽  
Independent Variables ◽  
Domestic Capital ◽  
Panel Data Regression

<p>Indonesia is a country with great economic potency. Indonesia has a vast area and abundant natural products, but until now Indonesia is still a developing country. The Indonesian economy is defeated by other countries such as Japan, China and South Korea even by the neighboring country, Singapore. Increasing the national economy can be started from improving the regional economy which can be measured by gross regional domestic product (GRDP). Indonesia will experience a demographic bonus in 2045 so that the population of productive age is expected to contribute a lot to economic growth. The large number of productive age population must be balanced with the availability of jobs so that this momentum can be fully utilized. Foreign investment can be a solution when domestic capital is insufficient in financing economic activities. In addressing this phenomenon, a statistical analysis of panel data regression was conducted to see the relationship between independent variables, namely the number of labor force and realization of foreign investment, and a dependent variable, namely GRDP at constant prices in 2010 for every province in Indonesia. We use time series data in 2015-2017 and cross-sectional data of 34 provinces in Indonesia taken from BPS official website. The estimation result shows that both independent variables partially and fully have a significant effect on the GRDP with an adjusted <em>R<sup>2</sup></em> of 99.86%.</p><p> </p><p><strong>Keywords</strong><strong>: </strong>Labor force; regression; panel data; foreign capital; GRDP.</p>

scholarly journals Pengaruh Infrastruktur terhadap Pertumbuhan Ekonomi di Provinsi Jawa Tengah

2020 ◽  
Vol 5 (02) ◽  
pp. 169-179
Author(s):  
Saadatul Kamilla ◽  
Dinar Melani Hutajulu
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Multiple Linear Regression Model ◽  
Secondary Data ◽  
Series Data ◽  
Economic Activities ◽  
The Road ◽  
Central Java ◽  
Independent Variable ◽  
Basic Infrastructure

ABSTRACT One of the most important aspects in economic development is infrastructure. Adequate and equitable infrastructure in all regions of a country will facilitate economic performer in accessing and carrying out economic activities. A good economic activities will create economic growth. The main purpose of this research is to know the effect of infrastructure on economic growth. The study was conducted in Central Java province using secondary data from 2006-2018. The independent variable of this study is the basic infrastructure including roads, electricity and water. While the dependent variable is the GRDP. The model used is multiple linear regression model using time series data. The results of this study indicate that the road infrastructure variable is significant to  influence on economic growth. The variable infrastructure of electricity shows significant results and have a positive effect on economic growth. Variable infrastructure of water shows the results are insignificant on economic growth. Keywords: economic growth, road, electricity, water

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