scholarly journals Granger causality between cardiovascular diseases and some macroeconomic indicators: Azerbaijan case

2021 ◽  
Vol 12 (33) ◽  
pp. 247-263
Author(s):  
Madina Yuzbashova
Keyword(s):  
Time Series ◽  
Causal Relationship ◽  
Steady Increase ◽  
Time Lags ◽  
Annual Income ◽  
Macroeconomic Indicators ◽  
Per Capita ◽  
Adf Test ◽  
Average Annual Income ◽  
Granger Test

Objective: Statistical assessment of the interdependence of CVD indicators on macroeconomic indicators on the example of Azerbaijan. Design: Research design is to test statistical hypotheses about the presence of direct and inverse causal relationships between CDV-indicators and macroeconomic indicators. Baseline and estimated data cover the period from 1991 to 2018 and are based on data from the SSCRA (2019) report. We use paired linear regression in which macroeconomic indicators are independent and CDV indicators are dependent variables. The stationarity of the time series was checked using the ADF test. To investigate the causal relationship between time series, the Granger test was used. Main Outcome Measures: p-level < 0.05; time lags are 1, 2 and 3 years. Results: Absence of direct and inverse causal relationship between CVD indicators and macroeconomic indicators GDP per capita, average annual income households per capita and average annual income households per capita. Conclusions: In the period from 1991 to 2018, the number of CDV deaths in Azerbaijan increased by 1.54. There is a steady increase in CDV diseases by 2.23 times. Despite GDP growth, there is no direct and inverse causal relationship between CVD indicators and macroeconomic indicators in the sense of the Granger test.

scholarly journals Trade Openness and Economic Growth Nexus: A Case Study of BRICS

2018 ◽  
Vol 54 (1) ◽  
pp. 1-15 ◽  
Author(s):  
L. G. Burange ◽  
Rucha R. Ranadive ◽  
Neha N. Karnik
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Causal Relationship ◽  
Trade Openness ◽  
Gdp Per Capita ◽  
Jel Codes ◽  
Per Capita ◽  
The Impact ◽  
Econometric Technique

The article analyses a causal relationship between trade openness and economic growth for the member countries of BRICS by using an econometric technique of time series analysis. Member countries of BRICS adopted a series of liberalization reforms, almost simultaneously, from the late 1980s. The article attempts to study the impact of trade openness on their growth in GDP per capita. It captures structural composition of GDP and openness of trade in four aspects, that is, merchandise exports, merchandise imports, services export and services import. In India, the study found growth-led trade in services hypothesis. The article supports the growth-led export and growth-led import hypothesis for China and export- and import-led growth for South Africa. However, no causal relationship was evident for Brazil and Russia. JEL Codes: F43, C22

The relationship between energy consumption and gross domestic product in Hong Kong (1992–2015): Evidence from sectoral analysis and implications on future energy policy

2019 ◽  
Vol 31 (2) ◽  
pp. 215-236
Author(s):  
Ruixiaoxiao Zhang ◽  
Geoffrey QP Shen ◽  
Meng Ni ◽  
Johnny Wong
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Gross Domestic Product ◽  
Causal Relationship ◽  
Granger Causality ◽  
Value Added ◽  
Granger Causality Test ◽  
Domestic Product ◽  
Causality Test ◽  
Per Capita

The causal relationship between energy consumption and gross domestic product in Hong Kong from 1992 to 2015 is investigated in this study. Different from the previous studies focusing on the causal relationship between total energy consumption and total gross domestic product per capita, this study further investigates the causal relationship from sectoral perspective, including residential, commercial, industrial and transportation sectors. For each sector, the time series data of sectoral energy consumption and sectoral per capita value added are collected. To conduct the Granger causality test, the unit root test is first applied to analyse the stationarity of time series. The cointegration test is then employed to examine whether causal relationship exists in long-term. Finally, based on the aforementioned tests, both vector error correction model and vector autoregression model can be selected to determine the Granger causality between time series. It is interesting to find that the sectoral energy consumption and corresponding sectoral per capita value-added exhibit quite different causal relationships. For both residential sector and commercial sectors, a unidirectional causal relationship is found running from the sectoral per capita value added to sectoral energy consumption. Oppositely, for industrial sector and transportation sector, a unidirectional causal relationship is found running from sectoral energy consumption to sectoral per capita value added. Regarding the Granger causality test results, the indicative suggestions on energy conservation policies, energy efficiency policies and greenhouse gas emission reduction policies are discussed based on the background of Hong Kong’s economic structure and fuel types.

scholarly journals Ecological Footprint Accounting for Countries: Updates and Results of the National Footprint Accounts, 2012–2018

Resources ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 58 ◽  
Author(s):  
David Lin ◽  
Laurel Hanscom ◽  
Adeline Murthy ◽  
Alessandro Galli ◽  
Mikel Evans ◽  
...  
Keyword(s):  
Time Series ◽  
Ecological Footprint ◽  
Management Practices ◽  
Supply And Demand ◽  
Steady Increase ◽  
Global Trends ◽  
The World ◽  
Per Capita ◽  
Agricultural Yields

Ecological Footprint accounting quantifies the supply and demand of Earth’s biocapacity. The National Footprint Accounts (NFA) are the most widely used Ecological Footprint (EF) dataset, and provide results for most countries and the world from 1961 to 2014, based primarily on publicly available UN datasets. Here, we review the evolution of the NFA, describe and quantify the effects of improvements that have been implemented into the accounts since the 2012 edition, and review the latest global trends. Comparing results over six editions of NFAs, we find that time-series trends in world results remain stable, and that the world Ecological Footprint for the latest common year (2008) has increased six percent after four major accounting improvements and more than thirty minor improvements. The latest results from the NFA 2018 Edition for the year 2014 indicate that humanity’s Ecological Footprint is 1.7 Earths, and that global ecological overshoot continues to grow. While improved management practices and increased agricultural yields have assisted in a steady increase of Earth’s biocapacity since 1961, humanity’s Ecological Footprint continues to increase at a faster pace than global biocapacity, particularly in Asia, where the total and per capita Ecological Footprint are increasing faster than all other regions.

scholarly journals LAND FRAGMENTATION AND IT DETERMINANTS IN NIGERIA: A CASE STUDY OF SMALLHOLDER FARMERS IN IKENNE AGRICULTURAL ZONE, OGUN STATE, NIGERIA

2019 ◽  
Vol 52 (2) ◽  
Author(s):  
Abiodun Elijah Obayelu ◽  
Omotoso Oluseye Ogunmola ◽  
Kola Jeremiah Oyewole
Keyword(s):  
Agricultural Land ◽  
Smallholder Farmers ◽  
Labour Force ◽  
Arable Land ◽  
Annual Income ◽  
Land Fragmentation ◽  
Ogun State ◽  
Smallholder Farm ◽  
Per Capita ◽  
Average Annual Income

Land fragmentation is a major obstacle to agricultural development in Nigeria. The objective of this study is to examine land fragmentation and its determinants by smallholder farmers. In Nigeria through the use of Ikenne Agricultural zone of Ogun State. A multi-stage sampling procedure was used to select 120 smallholder farmers in the study area. Descriptive statistics, Simpson index and Stepwise Multiple Linear Regression were used. The results of the Simpson’s Index showed that the average land fragmentation index was 0.38 which implies that smallholder farm lands are highly fragmented. Household average annual income (p<0.01), per capita arable land (p<0.01), size of land rented in and out by household (p<0.01), labour force of household (p<0.05), total available land area (p<0.05) and farming experience (P<0.01) were the significant factors affecting average plot size owned by farmers. Also, household average annual income (p<0.01), per capita arable land (p<0.05), size of land rented in and out by household (p<0.01), labour force of household (p<0.05) were the ones contributing to the number of plots cultivated by the farmers. Land consolidation, application of specific land protection policies to prevent agricultural land from being developed for non-agricultural purposes are recommended.  

Measuring Effectiveness Of Economic Growth On Reducing Infant Mortality Rate In Tamilnadu

2019 ◽  
Vol 118 (4) ◽  
pp. 129-141
Author(s):  
Mr. Y. EBENEZER
Keyword(s):  
Economic Growth ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Negative Relationship ◽  
Infant Mortality Rate ◽  
Domestic Product ◽  
Per Capita ◽  
The Impact ◽  
Adf Test ◽  
Bound Test

                   This paper deals with economic growth and infant mortality rate in Tamilnadu. The objects of this paper are to test the relationship between Per capita Net State Domestic Product and infant mortality rate and also to measure the impact of Per capita Net State Domestic Product on infant mortality rate in Tamil Nadu. This analysis has employed the ADF test and ARDL approach. The result of the study shows that IMR got reduced and Per capita Net State Domestic Product increased during the study period. This analysis also revealed that there is a negative relationship between IMR and the economic growth of Tamilnadu. In addition, ARDL bound test result has concluded that per capita Net State Domestic Product of Tamilnadu has long run association with IMR.

scholarly journals Examining GDP Growth and Its Volatility: An Episodic Approach

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 890
Author(s):  
Jakub Bartak ◽  
Łukasz Jabłoński ◽  
Agnieszka Jastrzębska
Keyword(s):  
Economic Growth ◽  
Genetic Algorithm ◽  
Time Series ◽  
Human Capital ◽  
Growth Rates ◽  
Structural Breaks ◽  
Negative Relationship ◽  
Gdp Per Capita ◽  
Gdp Growth ◽  
Per Capita

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

Comparative Study on BRIC Carbon Dioxide Emissions - Econometric Empirical Test Based on Cross-Century Time Series Data

2012 ◽  
Vol 616-618 ◽  
pp. 1512-1515
Author(s):  
Wei Hua Du
Keyword(s):  
Carbon Dioxide ◽  
Time Series ◽  
Carbon Dioxide Emissions ◽  
Time Series Data ◽  
Comparative Statics ◽  
Total Carbon ◽  
Series Data ◽  
Per Capita Gdp ◽  
Bric Countries ◽  
Per Capita

Take for example the BRIC economies: Brazil, Russia, India and China. We investigated the time series data on the relationship between carbon dioxide emission and economic growth in these fast-growing developing countries by both comparative statics and comparative dynamics. The results show that there is the monotonic relationship between total carbon dioxide emissions, carbon dioxide emissions per capita and per capita GDP in any one of the BRIC countries. And there is decreasing relationship between the carbon dioxide emissions per unit GDP and per capita GDP.

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 Distinguishing Trends and Shifts from Memory in Climate Data

2018 ◽  
Vol 31 (23) ◽  
pp. 9519-9543 ◽  
Author(s):  
Claudie Beaulieu ◽  
Rebecca Killick
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Information Criterion ◽  
Steady Increase ◽  
Climate Data ◽  
Surface Warming ◽  
Abrupt Changes ◽  
Short Memory Process ◽  
The 1960S ◽  
And Shifts

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change.

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