scholarly journals KAUSALITAS KONTRIBUSI INDUSTRI PARIWISATA DAN JUMLAH KUNJUNGAN WISATAWAN TERHADAP PERTUMBUHAN EKONOMI

2018 ◽  
Vol 7 (4) ◽  
pp. 330
Author(s):  
COKORDA BAGUS YUDISTIRA ◽  
I WAYAN SUMARJAYA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Causal Relationship ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Var Model ◽  
Vector Autoregressive ◽  
Foreign Tourist ◽  
The World

Bali is known as one of the most popular tourism destination in the world. The number of tourist visit to Bali increases every year. In 2010, there roughly 7 millions tourist visits to Bali and reach up to 14 million people by the end of 2017. This increased in number may affect the growth of tourism industries and economic growth in Bali Province. This study aims to analyze the patterns of causal relationship between tourism industry receipts, tourist visits, and economic growth in Bali based on time series data using vector autoregressive (VAR) model. The results conclude the following: (i) foreign tourist visits is significantly affect economic growth. In addition, economic growth, domestic tourist visits, and foreign tourist visits are significantly impact to tourism industry receipts, (ii) economic growth would affect the tourism industry receipts in the next four consecutive months, (iii) the forecasting result of economic growth with VAR model is highly accurated with MAPE 2%.

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

2013 ◽  
Vol 5 (8) ◽  
pp. 379-384
Author(s):  
Seuk Wai ◽  
Mohd Tahir Ismail . ◽  
Siok Kun Sek .
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Markov Switching ◽  
Stock Index ◽  
Smooth Transition ◽  
Series Data ◽  
Var Model ◽  
Economic Time Series ◽  
Vector Autoregressive ◽  
Economic Time

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

scholarly journals HUBUNGAN KAUSALITAS ANTAR KETIMPANGAN PENDAPATAN, PERTUMBUHAN EKONOMI, DAN KEMISKINAN DI INONESIA

2021 ◽  
Vol 3 (1) ◽  
pp. 79
Author(s):  
Dicky Fernando ◽  
Syamsul Amar
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Income Inequality ◽  
Regression Model ◽  
Causal Relationship ◽  
Annual Report ◽  
Time Series Data ◽  
Series Data ◽  
Panel Regression ◽  
Causality Relationship

This study aims to explain the causality relationship between income inequality, economic growth, and poverty in Indonesia. In this study using a panel regression model. And data used are time series data from 2011-2017, Consisting of 32 provinces. This data is obtained from BPS annual report. The result of this study indicate that (1) There is no causal relationship between economic growth and poverty (2) There is a causal relationship between income inequality and poverty (3) There is a one-way causal relationship between economic growth and income inequality.

scholarly journals ANALISIS KAUSALITAS PERTUMBUHAN EKONOMI, KEMISKINAN, DAN KETIMPANGAN PENDAPATAN DI SUMATERA BARAT

2019 ◽  
Vol 1 (3) ◽  
pp. 781
Author(s):  
Riri Agustina Fratiwi ◽  
Mike Triani
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Income Inequality ◽  
Regression Model ◽  
Causal Relationship ◽  
Annual Report ◽  
Time Series Data ◽  
Series Data ◽  
Panel Regression

This stuudy”explains the analysis causality of economic”growth poverty, and income”inequality in west”sumatera. The method used is to a panel regression model. This data uses a combination of time series data”from 2013-2017, which consists of 19 city districts. Data obtained from BPS annual”report (Statistics Indonesia). The”results of”this study show that (1) there is no”causall relationship”between”economic”growth and poverty (2) there is a causal relationship”between”economic”growth”and inequality (3) there is no causal relationship”between poverty”and income”inequaality.

scholarly journals Empirical Analysis of the Relationship Between the Development of China's Tourism Industry and Economic Growth Based on VAR Model

2021 ◽  
Vol 12 (4) ◽  
pp. 100
Author(s):  
Lianfeng Zhang ◽  
Yuriy Danko ◽  
Jianmin Wang ◽  
Zhuanqing Chen
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Tourism Development ◽  
Tourism Industry ◽  
Series Data ◽  
Var Model ◽  
Long Run ◽  
Equilibrium Relationship ◽  
The Relationship

The relationship between tourism development and economic growth has been a hot topic in the field of tourism economy in recent years, and whether there is a long-term equilibrium relationship between tourism development variables and economic variables (usually GDP) is also a hot topic. By identifying the long-term equilibrium relationship between two variables, we can find the quantitative variation law (generally effect) of one variable with the other. Based on the vector autoregression of the time series data of China's tourism development from 2000 to 2019, it is found that there is a long-term equilibrium relationship between China's tourism foreign exchange income and domestic tourism gross income and their respective GDP, and the long-term effect is 99% respectively. Through the establishment of the VAR model for the development of China's tourism industry and economic growth, in the long run, they have a balanced relationship of mutual promotion, so as to further guide the development of China's tourism.

scholarly journals Dynamic Modelling and Forecasting of Data Export of Agricultural Commodity by Vector Autoregressive Model

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
L.M. Hamzah ◽  
S.U. Nabilah ◽  
E. Russel ◽  
M. Usman ◽  
E. Virginia ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Models ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Var Model ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Coffee Beans ◽  
The Relationship

The Vector Autoregressive Model (VAR) is one of the statistical models that can be used for modeling multivariate time series data. It is commonly used in finance, management, business and economics. The VAR model analyzes the time series data simultaneously to arrive at the right conclusions while dynamically explaining the behavior of the relationship between endogenous variables, as well as endogenous and exogenous variables. From time to time, the VAR model is influenced by its own factors via Granger Causality. In this study, we will discuss and determine the best model to describe the relationship among data export value of Indonesia's agricultural commodities—coffee beans, cacao beans and tobacco—where the monthly data spans the years 2007-2018. Several models are applied to the data, such as VAR (1), VAR (2), VAR (3), VAR (4) and VAR (5) models. As a result, the VAR (2) model was chosen as the best model based on the Akaike’s Information Criterion with Correction, Schwarz Bayesian Criterion, Akaike’s Information Criterion and Hanna-Quinn Information Criterion for selecting statistical models. The dynamic behavior of the three export variables of Indonesian coffee beans, cacao beans and tobacco is explained by Granger Causality. Furthermore, the best model VAR (2) is used to forecast the next 10 months.

The Causal Relationship between Tourist Arrivals and Economic Growth: Evidence from Indonesia

2018 ◽  
Vol 9 (4) ◽  
pp. 721 ◽  
Author(s):  
Muhamad Rifki FADILAH ◽  
Haryo KUNCORO ◽  
K. Dianta A. SEBAYANG
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Causal Relationship ◽  
Granger Causality ◽  
Var Model ◽  
Short Term ◽  
Vector Autoregressive ◽  
Main Motivation ◽  
Foreign Tourist

The main motivation behind this research because nowadays tourism becomes one of the major industries in Indonesia. However, literature on the causal relationship between the cyclical components of tourist arrivals and economic growth revealed inconclusive especially in the developing countries as Indonesia. This paper aims at investigating whether foreign tourist arrivals contribute to economic growth evidence from Indonesia over the period 2004 (1) – 2016 (12). Then, this research was applied the vector autoregressive (VAR) model. Afterwards, we exploited Hodrick-Prescott de-trending procedure to obtain cyclical components of tourist arrivals and economic growth. By using the test of Granger-causality, we found that causality running from tourist arrivals to economic growth. Furthermore, the VAR model shows that tourist arrivals were pro-cyclical to economic growth, implied that the increase of tourist arrivals promote the economic growth so that foreign tourist arrivals could be the key to escalate Indonesia’s economic growth in short term.

Visualizing States of Time-Series Data by Autoregressive Gaussian Process Dynamical Models

2017 ◽  
Vol 21 (5) ◽  
pp. 825-831 ◽  
Author(s):  
Nobuhiko Yamaguchi ◽  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Nonlinear Dimensionality Reduction ◽  
Dynamical Models ◽  
Vector Autoregressive ◽  
Single State ◽  
Over Time

Gaussian process dynamical models (GPDMs) are used for nonlinear dimensionality reduction in time series by means of Gaussian process priors. An extension of GPDMs is proposed for visualizing the states of time series. The conventional GPDM approach associates a state with an observation value. Therefore, observations changing over time cannot be represented by a single state. Consequently, the resulting visualization of state transition is difficult to understand, as states change when the observation values change. To overcome this issue, autoregressive GPDMs, called ARGPDMs, are proposed. They associate a state with a vector autoregressive (VAR) model. Therefore, observations changing over time can be represented by a single state. The resulting visualization is easier to understand, as states change only when the VAR model changes. We demonstrate experimentally that the ARGPDM approach provides better visualization compared with conventional GPDMs.

scholarly journals Hypothesizing directional causality between the governance indicators and economic growth: the case of Afghanistan

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Naim Azimi ◽  
Mohammad Musa Shafiq
Keyword(s):  
Economic Growth ◽  
Rule Of Law ◽  
Time Series Data ◽  
Political Stability ◽  
Wald Test ◽  
Series Data ◽  
Vector Autoregressive ◽  
Government Effectiveness ◽  
Governance Indicators ◽  
The Government

AbstractThis paper examines the causal relationship between governance indicators and economic growth in Afghanistan. We use a set of quarterly time series data from 2003Q1 to 2018Q4 to test our hypothesis. Following Toda and Yamamoto’s (J Econom 66(1–2):225–250, 1995. 10.1016/0304-4076(94)01616-8) vector autoregressive model and the modified Wald test, our empirical results show a unidirectional causality between the government effectiveness, rule of law, and the economic growth. Our findings exhibit significant causal relationships running from economic growth to the eradication of corruption, the establishment of the rule of law, quality of regulatory measures, government effectiveness, and political stability. More interestingly, we support the significant multidimensional causality hypothesis among the governance indicators. Overall, our findings not only reveal causality between economic growth and governance indicators, but they also show interdependencies among the governance indicators.

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