Estimating equation-based causality analysis with application to microarray time series data

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.

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HUBUNGAN KAUSALITAS HARGA JUAL DAN HARGA SEWA APARTEMEN DI JABODETABEK

Jurnal Orientasi Bisnis dan Entrepreneurship (JOBS) ◽

10.33476/jobs.v1i1.1312 ◽

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

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

10.1101/2021.09.26.461882 ◽

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.

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Development and Terrorism in Nigeria: Co-Integration and Causality Analysis of Macroeconomic Factors

Indian Journal of Finance and Banking ◽

10.46281/ijfb.v2i1.90 ◽

2018 ◽

Vol 2 (1) ◽

pp. 1-33

Author(s):

Emmanuel O. Okon

Keyword(s):

Time Series ◽

Time Series Data ◽

Trade Openness ◽

Private Investment ◽

Series Data ◽

Causality Analysis ◽

Macroeconomic Policies ◽

Macroeconomic Factors ◽

The Government ◽

Adf Test

This paper is a cointegration and causality analysis of macroeconomic factors and terrorism in Nigeria using time series data spanning between 1970 and 2016. The stochastic characteristics of each time series was examined using Augmented Dickey Fuller (ADF) test. The result reveals that LOG(GOVX), LOG(INTR), POLX, DLOG(GDPC) and DLOG(OPEN) were in line with the apriori expectation. With this development, some recommendations were made amongst which are that trade openness rate should be all time kept at peak benchmark by adopting tight trade openness while strategic macroeconomic policies should be instituted in order to encourage domestic private investment to enhance the growth of the economy. Nigerian political system has to be stabilized and the government should step up its intelligence gathering capacity as well as training security agents to forcefully combat terrorist group.

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Dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series Data

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.09.379 ◽

2018 ◽

Vol 51 (18) ◽

pp. 476-481 ◽

Cited By ~ 4

Author(s):

Yining Dong ◽

S. Joe Qin

Keyword(s):

Time Series ◽

Canonical Correlation ◽

Time Series Data ◽

Series Data ◽

High Dimensional ◽

Causality Analysis

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Investigating neural primacy in Major Depressive Disorder: multivariate Granger causality analysis of resting-state fMRI time-series data

Molecular Psychiatry ◽

10.1038/mp.2010.46 ◽

2010 ◽

Vol 16 (7) ◽

pp. 763-772 ◽

Cited By ~ 213

Author(s):

J P Hamilton ◽

G Chen ◽

M E Thomason ◽

M E Schwartz ◽

I H Gotlib

Keyword(s):

Time Series ◽

Major Depressive Disorder ◽

Resting State ◽

Time Series Data ◽

Resting State Fmri ◽

Series Data ◽

Causality Analysis ◽

Major Depressive ◽

Granger Causality Analysis ◽

Fmri Time Series

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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