scholarly journals An Analysis of Potential Structural Change in the Demand for South Atlantic Fresh Peaches

HortScience ◽  
1992 ◽  
Vol 27 (10) ◽  
pp. 1129-1131 ◽  
Author(s):  
J.E. Epperson ◽  
M.C. Chien ◽  
W.O. Mizelle
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Structural Change ◽  
Time Series Data ◽  
South Atlantic ◽  
Capita Consumption ◽  
Series Data ◽  
Cultivar Development ◽  
Per Capita ◽  
Per Capita Consumption

An analysis was conducted using time-series data to identify possible structural change in the farm-gate demand for South Atlantic fresh peaches [Prunus persica (L.) Batsch.]. Structural change was not found in the price-quantity relationship. However, a failing per capita consumption of South Atlantic fresh peaches was found to be associated with an increase in the per capita consumption of fresh fruits in general. Thus, measures such as promotion and advertising, uniform quality control, and cultivar development may increase the demand for South Atlantic fresh peaches.

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.

scholarly journals DETERMINASI EKSPOR NON MIGAS INDONESIA KE JEPANG

Media Ekonomi ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 147
Author(s):  
Soeharjoto Soeharjoto
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Exchange Rates ◽  
Capita Income ◽  
Oil And Gas ◽  
Time Series Data ◽  
Series Data ◽  
Per Capita Income ◽  
Oil Exports ◽  
Per Capita

<em></em><em><em>This study aims to determine the factors that affect Indonesia's non-oil exports to Japan. The variables used are imports, exchange rates, per capita income, inflation and non-oil exports of Indonesia to Japan</em>. <em>The analytical method used is regression analysis with data used for quarterly time series data from 2005-2016.</em> <em>The results are variable imports of raw and auxiliary materials, cycles, inflation, real Japanese GDP, and the population is able to explain Indonesia's non-oil exports to Japan by 31.3 percent. Imports, exchange rates, per capita income and inflation have a positive and significant effect on non-oil and gas exports to Japan.</em></em><em> </em>

scholarly journals On the run-length of the structural change in time series data

2019 ◽  
Author(s):  
Wiwik Prihartanti ◽  
Dwilaksana Abdullah Rasyid ◽  
Nur Iriawan
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Time Series Data ◽  
Series Data ◽  
Run Length

scholarly journals A non-equilibrium formulation of food security resilience

2017 ◽  
Vol 4 (1) ◽  
pp. 160874 ◽  
Author(s):  
Matteo Smerlak ◽  
Bapu Vaitla
Keyword(s):  
Time Series ◽  
Food Security ◽  
High Frequency ◽  
Time Series Data ◽  
Security Policies ◽  
Series Data ◽  
Poor Countries ◽  
Long Run ◽  
Per Capita ◽  
Non Equilibrium

Resilience, the ability to recover from adverse events, is of fundamental importance to food security. This is especially true in poor countries, where basic needs are frequently threatened by economic, environmental and health shocks. An empirically sound formalization of the concept of food security resilience, however, is lacking. Here, we introduce a general non-equilibrium framework for quantifying resilience based on the statistical notion of persistence. Our approach can be applied to any food security variable for which high-frequency time-series data are available. We illustrate our method with per capita kilocalorie availability for 161 countries between 1961 and 2011. We find that resilient countries are not necessarily those that are characterized by high levels or less volatile fluctuations of kilocalorie intake. Accordingly, food security policies and programmes will need to be tailored not only to welfare levels at any one time, but also to long-run welfare dynamics.

scholarly journals Perkembangan dan Faktor yang Mempengaruhi Permintaan Volume Impor Garam Industri di Indonesia

2021 ◽  
Vol 5 (3) ◽  
pp. 748-761
Author(s):  
Okvianti Putri ◽  
◽  
Teti Sugiarti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Government Policies ◽  
Series Data ◽  
Price Stability ◽  
Gdp Growth ◽  
Variable Amount ◽  
Industry Growth ◽  
Per Capita ◽  
Import Quotas

Salt demand is increasing along with population and industry growth. The imbalance between the amount of industrial salt production with the amount of industrial salt demand is a problem at this time, so to meet the demand for industrial salt, imports are needed. The objectives of this study are: (1) to know trends in the volume of Indonesian salt imports in 2019-2023, (2) to know the factors that influence the demand for industrial salt import volumes in Indonesia. The study uses time series data (2007-2018) which is processed and analyzed using trend analysis and multiple regression analysis. The results showed that the trend of industrial salt import volume in Indonesia during 2019-2023 was predicted to increase by 107,101 tons / year. The factors that significantly and positively affect the volume of industrial salt imports in Indonesia are the variable price of imported salt and the number of industries that use raw salt, while the variable amount of production, exchange rate and GDP growth in Indonesia per capita have no significant effect. Suggestions for reducing the rate of imports can be done by improving production technology, government policies related to international salt price stability and industrial salt import quotas.

scholarly journals Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

2021 ◽  
Author(s):  
B. Faybishenko ◽  
R. Versteeg ◽  
G. Pastorello ◽  
D. Dwivedi ◽  
C. Varadharajan ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Quality Assurance ◽  
Time Series Data ◽  
Meteorological Station ◽  
Barro Colorado Island ◽  
Phase Iii ◽  
Series Data ◽  
Statistical Framework ◽  
East River

AbstractRepresentativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

Time Series Analysis and Structural Change Detection

2010 ◽  
pp. 377-395
Author(s):  
Kwok Pan Pang
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Time Series Analysis ◽  
Change Detection ◽  
Structural Breaks ◽  
Time Series Data ◽  
Series Data ◽  
Series Analysis ◽  
Us Economy ◽  
Over Time

Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time. However, when structural change occurs in the data, the time series analysis methods based on the assumption of no structural change will no longer be appropriate; and thus there emerges another approach to solving the problem of structural change. Almost all time series analysis or forecasting methods always assume that the structure is consistent and stable over time, and all available data will be used for the time series prediction and analysis. When any structural change occurs in the middle of time series data, any analysis result and forecasting drawn from full data set will be misleading. Structural change is quite common in the real world. In the study of a very large set of macroeconomic time series that represent the ‘fundamentals’ of the US economy, Stock and Watson (1996) has found evidence of structural instability in the majority of the series. Besides, ignoring structural change reduces the prediction accuracy. Persaran and Timmermann (2003), Hansen (2001) and Clement and Hendry (1998, 1999) showed that structural change is pervasive in time series data, ignoring structural breaks which often occur in time series significantly reduces the accuracy of the forecast, and results in misleading or wrong conclusions. This chapter mainly focuses on introducing the most common time series methods. The author highlights the problems when applying to most real situations with structural changes, briefly introduce some existing structural change methods, and demonstrate how to apply structural change detection in time series decomposition.

scholarly journals The Correlation between Energy Cost Share, Human, and Economic Development: Using Time Series Data from Australasia, Europe, North America, and the BRICS Nations

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2405 ◽  
Author(s):  
Ryan Roberts ◽  
Josephine Musango ◽  
Alan Brent ◽  
Matthew Heun
Keyword(s):  
Time Series ◽  
Economic Development ◽  
North America ◽  
Strong Correlation ◽  
Energy Cost ◽  
Time Series Data ◽  
Series Data ◽  
Cost Share ◽  
Social And Economic Development ◽  
Per Capita

This paper investigates how a change in a region’s energy cost share (ECS), a ratio of a region’s energy expenditure as a fraction of its gross domestic product (GDP), affects the region’s social and economic development. Nations from four regions of the world, namely Australasia, Europe, North America, and the BRICS nations (Brazil, Russia, India, China, and South Africa) were chosen for this study. Using time series data from the period of 1978 to 2010, the annual ECS of each country was compared to the year-on-year GDP change, as well as the components of the human development index (HDI). High ECS values were seen to correlate with low economic development. The existence of an ECS threshold was found in 14 of the 15 countries, for all the regions, and for the worldwide analysis, with very strong correlation coefficients obtained for periods of high ECS. New to this field of research, this study also investigated the effects of ECS on gross national income (GNI) per capita change, as well as the effects of 0, 1, 2, and 3 year lags. This investigation found that ECS has a very strong correlation to GNI per capita change, which was much stronger than the correlation between ECS and GDP change. The effects of ECS on social and economic development occurred after varying time lags, and it is unique to each country and region. Regions with similar ECS dynamics were identified, with possible reasons for the similarities being provided.

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