Causality Analysis of Climate and Ecosystem Time Series

2020 ◽  
pp. 139-162
Author(s):  
Mohammad Gorji Sefidmazgi ◽  
Ali Gorji Sefidmazgi
Keyword(s):  
Time Series ◽  
Causality Analysis

Examining the Long Run Effects of Export, Import and FDI Inflows on the FDI Outflows from India: A Causality Analysis

2009 ◽  
Vol 10 (1) ◽  
pp. 65-88
Author(s):  
Nandita Dasgupta
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Driving Force ◽  
Time Series Data ◽  
Series Data ◽  
Causality Analysis ◽  
Fdi Inflows ◽  
Long Run ◽  
Time Series Data Analysis ◽  
Trade And Investment

The objective of this paper is to examine the effects of international trade and investment related macro economic variables, namely, exports, imports and FDI inflows on the outflows of FDI from India over 1970 through 2005. Using time series data analysis, the empirical part of the paper finds unidirectional Granger Causality from export and import to FDI outflows but no such causality exists from FDI inflows to the corresponding outflows from India. Results confirm the assumption that lagged imports and exports are a driving force of ing front.

HUBUNGAN KAUSALITAS HARGA JUAL DAN HARGA SEWA APARTEMEN DI JABODETABEK

2021 ◽  
Vol 1 (1) ◽  
pp. 93-105
Author(s):  
Zainal Zawir Simon ◽  
Effendy Zain ◽  
Zulihar Zulihar
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Investment Analysis ◽  
Series Data ◽  
Analysis Tool ◽  
Selling Price ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Causality Relationship

Abstrak Penelitian ini bertujuan untuk mengetahui hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Data yang dipergunakan adalah data  time series dalam bentuk kuartalan untuk periode 2007:1-2018:3 dan alat analisis yang dipergunakan adalah analisa kausalitas Granger. Hasil penelitian menunjukkan bahwa tidak terdapat hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Dengan kata lain perubahan harga jual  tidak mempengaruhi harga sewa. Sebaliknya harga sewa juga tidak mempengaruhi harga jual apartemen. Dengan demikian Investor diharapkan dalam melakukan analisis investasinya memasukkan faktor-faktor lain yang dapat mempengaruhi harga jual dan harga sewa untuk apartemen, agar terlepas dari pandangan bahwa harga jual mempengaruhi harga sewa dan sebaliknya.Kata Kunci : Harga Jual apartemen, Harga Sewa Apartemen, Data Runtut Waktu, Analisa Kausalitas GrangerABSTRACTThis study aims to determine the causality relationship between the selling price of apartments and apartment rental prices in the Greater Jakarta area. The data used are time series data in quarterly form for the period 2007: 1-2018: 3 and the analysis tool used is the Granger causality analysis. The results showed that there was no causality relationship between apartment selling prices and apartment rental prices in the Greater Jakarta area. In other words, changes in selling prices do not affect rental prices. Conversely the rental price also does not affect the selling price of the apartment. Thus Investors are expected to carry out investment analysis to include other factors that can affect the selling price and rental price for an apartment, so that regardless of the view that the selling price affects the rental price and vice versa.Keywords : Selling Price of apartments, rental prices apartments, time series data, Granger Causality Analysis

Determinants of international tourism demand: Evidence from Australian states and territories

2018 ◽  
Vol 25 (2) ◽  
pp. 274-296 ◽  
Author(s):  
Muhammad Shafiullah ◽  
Luke Emeka Okafor ◽  
Usman Khalid
Keyword(s):  
Time Series ◽  
State Level ◽  
International Tourism ◽  
Tourism Demand ◽  
Causality Analysis ◽  
Foreign Born ◽  
Demand Model ◽  
Explanatory Variables ◽  
Cointegration Tests ◽  
World Income

This article explores whether the determinants of international tourism demand differ by states and territories in Australia. This is the first attempt at econometric modelling of international tourism demand in the states and territories of Australia. A demand model is specified where international visits to states and territories is a function of world income, state-level transportation costs, stock of foreign-born residents, the Australian real exchange rate and the price levels of international and domestic substitutes. Panel and time series econometric techniques are employed to test the model variables for stationarity, cointegration and direction of causality. Panel and time series cointegration tests show that the model is cointegrated. The causality analysis indicates that all explanatory variables Granger cause international visits to the Australian states and territories. Further, we show that the impacts of the determinants of international tourism vary by states and territories. The results underscore the importance of targeted policymaking that takes into account the economic and social structure of each state and territory instead of designing tourism policies on the basis of one-size-fits-all approach.

scholarly journals Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Organizacija ◽  
2008 ◽  
Vol 41 (3) ◽  
pp. 116-124 ◽  
Author(s):  
Danijel Bratina ◽  
Armand Faganel
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Discrete Event ◽  
Marketing Research ◽  
Causality Analysis ◽  
Sales Promotions ◽  
Current Application ◽  
Series Analysis ◽  
Daily Data ◽  
The Impact

Forecasting the Primary Demand for a Beer Brand Using Time Series AnalysisMarket research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.

scholarly journals Ability of Granger Causality Analysis to Detect Indirect Links: A Simulation Study

2020 ◽  
Vol 23 (2) ◽  
pp. 121-124
Author(s):  
N. W. Falasca ◽  
R. Franciotti
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Domain ◽  
Simulated Data ◽  
Biological Data ◽  
Connection Strength ◽  
Connectivity Analysis ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Virtual Time

Granger causality (G-causality) has emerged as a useful tool to investigate the influence that one system can exert over another system, but challenges remain when applying it to biological data. Specifically, it is not clear if G-causality can distinguish between direct and indirect influences. In this study time domain G-causality connectivity analysis was performed on simulated electroencephalographic cerebral signals. Conditional multivariate autoregressive model was applied to 19 virtual time series (nodes) to identify the effects of direct and indirect links while varying one of the following variables: the length of the time series, the lags between interacting nodes, the connection strength of the links, and the noise. Simulated data revealed that weak indirect influences are not identified by Gcausality analysis when applied on covariance stationary, non-correlated electrophysiological time series.

scholarly journals PERAMALAN PRODUKSI PADI DI KABUPATEN BADUNG, GIANYAR, DAN TABANAN DENGAN METODE VECTOR AUTOREGRESSION (VAR)

2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
I GUSTI AYU MEIGAYONI LESTARI ◽  
I WAYAN SUMARJAYA ◽  
I NYOMAN WIDANA
Keyword(s):  
Time Series ◽  
Vector Autoregression ◽  
Rice Production ◽  
Percentage Error ◽  
Causality Analysis ◽  
Third Order ◽  
Public Consumption ◽  
Time Series Method ◽  
The Mean ◽  
The Relationship

Rice is one of the staple foodstuffs whose availability is very important for public consumption in Indonesia, especially Bali Province. The three regencies that produce the most rice in Bali they are Badung, Gianyar and Tabanan. This study aims to model, predict, and analyze the relationship between rice production in Badung, Gianyar, and Tabanan Regency from January 2018 to December 2019 using vector autoregression (VAR) method. VAR method is a time series method that can be used to model and predict time series with more than one variable simultaneously. The results of this study, namely the VAR model obtained to predict the amount of rice production in Badung, Gianyar, and Tabanan Regencies is third order VAR (VAR (3)). Based on the forecasting criteria for the mean absolute percentage error (MAPE) in this model, a reasonable forecast is obtained for the rice production variables in Badung and Gianyar regencies, and good forecasting for the rice production variables in Tabanan Regency is obtained. Then, based on the granger causality analysis, it is found that the amount of rice production in Gianyar Regency affects the amount of rice production in Badung and Tabanan Regencies, and the amount of rice production in Badung Regency affects the amount of rice production in Gianyar Regency.

scholarly journals Using causality and correlation analysis to decipher microbial interactions in activated sludge

2021 ◽  
Author(s):  
Weiwei Cai ◽  
Xiangyu Han ◽  
Hong Yao
Keyword(s):  
Time Series ◽  
Activated Sludge ◽  
Network Theory ◽  
Time Series Data ◽  
Microbial Interactions ◽  
Series Data ◽  
Natural Environments ◽  
Cell Aggregate ◽  
Causality Analysis ◽  
Mutualistic Relationship

Network theory is widely used to understand microbial interactions in activated sludge and numerous other artificial and natural environments. However, when using correlation-based methods, it is not possible to identify the directionality of interactions within microbiota. Based on the classic Granger test of sequencing-based time-series data, a new Microbial Causal Correlation Network (MCCN) was constructed with distributed ecological interaction on the directed, associated links. As a result of applying MCCN to a time series of activated sludge data, we found that the hub species OTU56, classified as belonging the genus Nitrospira, was responsible for completing nitrification in activated sludge, and mainly interacted with Proteobacteria and Bacteroidetes in the form of amensal and commensal relationships, respectively. Phylogenetic tree suggested a mutualistic relationship between Nitrospira and denitrifiers. Zoogloea displayed the highest ncf value within the classified OTUs of the MCCN, indicating that it could be a foundation for activated sludge through forming the characteristic cell aggregate matrices into which other organisms embed during floc formation. Overall, the introduction of causality analysis greatly expands the ability of a network to shed a light on understanding the interactions between members of a microbial community.

Análisis de las interrelaciones entre la evolución de la flota atunera española y el sector conservero

2019 ◽  
Vol 37 (3) ◽  
pp. 81
Author(s):  
Juan José García del Hoyo ◽  
Ramón Jiménez Toribio ◽  
Félix García Ordaz
Keyword(s):  
Time Series ◽  
Joint Ventures ◽  
European Countries ◽  
Causality Analysis ◽  
Fish Processing ◽  
Analysis Techniques ◽  
Canned Tuna ◽  
The World ◽  
Canning Industry ◽  
A Current

The objective of this paper is to analyse the fish processing sector in general, and, specifically looking at canned tuna. In this regard, the tuna canning industry and its market in Spain from 1900 to nowadays are described, being one of the most important in the world. This industry has proved to be competitive enough to survive the globalisation process as opposed to all other European countries. Its main characteristics, its strengths and its weaknesses are shown.The evolution and the current situation of production, imports and exports of canned tuna are studied. Additionally, the evolution of the tuna fleet, ship- owning companies and joint ventures in third countries is presented.To sum up, this study provides a current overview of the fish processing sector, and its evolution throughout history. Interesting conclusions are drawn about the situation of the sector using causality analysis techniques for time series.

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