Granger-causality inference in the presence of gaps: An equidistant missing-data problem for non-synchronous recorded time series data

2019 ◽  
Vol 523 ◽  
pp. 839-851
Author(s):  
Heba Elsegai
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Granger Causality ◽  
Time Series Data ◽  
Series Data ◽  
Missing Data Problem ◽  
Data Problem ◽  
Causality Inference

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

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

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

An Empirical Analysis on the Relationship between Transport Infrastructure and Economic Growth in Tianjin

2012 ◽  
Vol 253-255 ◽  
pp. 278-281
Author(s):  
Xiao Zhe Meng
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Granger Causality ◽  
Empirical Analysis ◽  
Time Series Data ◽  
Transport Infrastructure ◽  
Integration Theory ◽  
Series Data ◽  
Positive Correlations ◽  
The Relationship

Transport infrastructure makes important contribution to economic growth. At the same time, the economic growth provides support to the transport infrastructure. Based on the co-integration theory and Granger casualty analysis, using time series data in Tianjin from 1978 to 2010, empirically analyze the co-integration relationship and Granger causality between the index of all kinds of transport infrastructure and the GDP in Tianjin. Research shows that there are positive correlations between the length of road, railway, quay line and GDP. The length of road, railway and quay line is the Granger cause of GDP. However, GDP is not the Granger cause of transport infrastructure.

scholarly journals Causal inference with multiple time series: principles and problems

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110613 ◽  
Author(s):  
Michael Eichler
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Granger Causality ◽  
Latent Variables ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Identification Algorithm ◽  
Multiple Time Series ◽  
Theoretical Justification

I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

scholarly journals Total Imports and Its Components Influence on Inflation in Kenya

2020 ◽  
pp. 54-62
Author(s):  
Evans Ovamba Kiganda ◽  
Margaret Atieno Omondi
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Central Bank ◽  
Granger Causality ◽  
Time Series Data ◽  
Negative Influence ◽  
Variance Decomposition ◽  
Series Data ◽  
Causality Tests ◽  
Granger Causality Tests

Aim: The purpose of this study was to analyze the influence of total imports (TIMP) and its components of commercial imports (CIMP) and government imports (GIMP) on inflation in           Kenya. Study Design: Quantitative approach was employed to analyze the influence of imports on inflation in Kenya. Methodology: Monthly time series data from Central Bank of Kenya for the period 2005 to 2018 was used for analysis involving correlation analysis, variance decomposition, impulse response and Granger causality tests. Results: Results indicated that total imports and commercial imports had negative influence on inflation while government imports did not significantly influence inflation in Kenya. Unidirectional causality from total imports and commercial imports to inflation was noted while there was no causality between government imports and inflation. Conclusion: The study concluded that imports influence inflation in Kenya but commercial imports highly determined total imports influence on inflation in Kenya.

scholarly journals Time-Series Causality with Missing Data

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Bo Yuan Chang ◽  
Mohamed A. Naiel ◽  
Steven Wardell ◽  
Stan Kleinikkink ◽  
John S. Zelek
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Causal Relationships ◽  
Sampled Data ◽  
The Past

Over the past years, researchers have proposed various methods to discover causal relationships among time-series data as well as algorithms to fill in missing entries in time-series data. Little to no work has been done in combining the two strategies for the purpose of learning causal relationships using unevenly sampled multivariate time-series data. In this paper, we examine how the causal parameters learnt from unevenly sampled data (with missing entries) deviates from the parameters learnt using the evenly sampled data (without missing entries). However, to obtain the causal relationship from a given time-series requires evenly sampled data, which suggests filling the missing data values before obtaining the causal parameters. Therefore, the proposed method is based on applying a Gaussian Process Regression (GPR) model for missing data recovery, followed by several pairwise Granger causality equations in Vector Autoregssive form to fit the recovered data and obtain the causal parameters. Experimental results show that the causal parameters generated by using GPR data filling offers much lower RMSE than the dummy model (fill with last seen entry) under all missing values percentage, suggesting that GPR data filling can better preserve the causal relationships when compared with dummy data filling, thus should be considered when dealing with unevenly sampled time-series causality learning.

scholarly journals Tweets, tents, and events: The interplay between street protests and social media

2017 ◽  
Author(s):  
Marco T. Bastos ◽  
Dan Mercea ◽  
Arthur Charpentier
Keyword(s):  
Social Media ◽  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Series Data ◽  
Political Unrest ◽  
Protest Activity

Recent protests have fuelled deliberations about the extent to which social media ignites popular uprisings. In this paper we use time-series data of Twitter, Facebook, and onsite protests to assess the Granger-causality between social media streams and onsite developments at the Indignados, Occupy, and Brazilian Vinegar protests. After applying a Gaussianization procedure to the data, we found that contentious communication on Twitter and Facebook forecasted onsite protest during the Indignados and Occupy protests, with bidirectional Granger-causality between online and onsite protest in the Occupy series. Conversely, the Vinegar demonstrations presented Granger-causality between Facebook and Twitter communication, and separately between protestors and injuries/arrests onsite. We conclude that the effective forecasting of protest activity likely varies across different instances of political unrest.

scholarly journals An Econometric Analysis on Remittance and Economic Growth in Bangladesh

2020 ◽  
pp. 214-218
Author(s):  
Md Shafiul Islam
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Granger Causality Test ◽  
Causal Link ◽  
Series Data ◽  
Causality Test ◽  
External Finance ◽  
The Relationship

In Bangladesh, migrant worker’s remittances constitute one of the most significant sources of external finance. This paper investigates the existence of relation between remittance inflow and GDP and the causal link between them in Bangladesh by employing the Granger causality test under a VECM framework. Using time series data over a 38 year period, we found that growth in remittances does lead to economic growth in Bangladesh. In addition to the relationship, this paper also points out some issues that are working as impediments in getting remittance and give some recommendations to overcome those impediments.

Sign in / Sign up

Export Citation Format

Share Document