scholarly journals Pervasive false brain connectivity from electrophysiological signals

2021 ◽  
Author(s):  
Roberto D. Pascual-Marqui ◽  
Peter Achermann ◽  
Pascal Faber ◽  
Toshihiko Kinoshita ◽  
Kieko Kochi ◽  
...  
Keyword(s):  
Granger Causality ◽  
Time Series Data ◽  
Brain Connectivity ◽  
Estimation Method ◽  
Brain Regions ◽  
Reproducible Research ◽  
Series Data ◽  
Coupling Coefficients ◽  
Causality Inference ◽  
Observation Noise

1.AbstractSignals of brain electric neuronal activity, either invasively measured or non-invasively estimated, are commonly used for connectivity inference. One popular methodology assumes that the neural dynamics follow a multivariate autoregression, where the autoregressive coefficients represent the couplings among regions. If observation noise is present and ignored, as is common in practice, the estimated couplings are biased, affecting all forms of Granger-causality inference, both in time and in frequency domains. Significant nonsense coupling, i.e., nonsense connectivity, can appear when in reality there is none, since there is always observation noise in two possible forms: measurement noise, and activity from other brain regions due to volume conduction and low spatial resolution. This problem is critical, and is currently not being addressed, calling into question the validity of many Granger-causality reports in the literature. An estimation method that accounts for noise is based on an overdetermined system of high-order multivariate Yule-Walker equations, which give reduced variance estimators for the coupling coefficients of the unobserved signals. Simulation-based comparisons to other published methods are presented, demonstrating its adequate performance. In addition, simulation results are presented for a zero connectivity case with noisy observations, where the new method correctly reports no connectivity while classical analyses (as found in most software packages) report nonsense connectivity. For the sake of reproducible research, the supplementary material includes, in human readable format, all the time series data used here.

scholarly journals Government Expenditure and Economic Growth in South Africa: A Vector Error Correction Modelling and Granger Causality Test

2017 ◽  
Vol 9 (4) ◽  
pp. 164
Author(s):  
Kagiso Molefe ◽  
Ireen Choga
Keyword(s):  
Economic Growth ◽  
South Africa ◽  
Error Correction ◽  
Granger Causality ◽  
Time Series Data ◽  
Government Expenditure ◽  
Series Data ◽  
Vector Error Correction ◽  
Vector Error ◽  
The Relationship

Previous studies generally find mixed empirical evidence on the relationship between government spending and economic growth. This study re-examine the relationship between government expenditure and economic growth in South Africa for the period of 1990 to 2015 using the Vector Error Correction Model and Granger Causality techniques. The time series data included in the model were gross domestic Product (GDP), government expenditure, national savings, government debt and consumer price index or inflation. Results obtained from the analysis showed a negative long-run relationship between government expenditure and economic growth in South Africa. Furthermore, the estimate of the speed of adjustment coefficient found in this study has revealed that 49 per cent of the variation in GDP from its equilibrium level is corrected within of a year. Furthermore, the study discovered that the causality relationship run from economic growth to government expenditure. This implied that the Wagner’s law is applicable to South Africa since government expenditure is an effect rather than a cause of economic growth. The results presented in this study are similar to those in the literature and are also sustained by preceding studies.

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

Democratic peace: Does ethnic inclusiveness reduce interstate conflict?

2019 ◽  
pp. 019251211988473
Author(s):  
Seung-Whan Choi ◽  
Henry Noll
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Estimation Method ◽  
Interstate Conflict ◽  
Model Specification ◽  
Series Data ◽  
Democratic Peace ◽  
Interstate Disputes ◽  
Time Series Data Analysis ◽  
Cross National

In this study, we argue that ethnic inclusiveness is an important democratic norm that fosters interstate peace. When two states are socialized into the notion of ethnic tolerance, they acquire the ability to reach cooperative arrangements in time of crisis. Based on cross-national time-series data analysis covering the period 1950–2001, we illustrate how two states that are inclusive of their politically relevant ethnic groups are less likely to experience interstate disputes than states that remain exclusive. This finding was robust, regardless of sample size, intensity of the dispute, model specification, or estimation method. Therefore, we believe in the existence of ethnic peace: ethnic inclusiveness represents an unambiguous force for democratic peace.

Changepoint Analysis of Binary and Ordinal Probit Models: An Application to Bank Rate Policy Under the Interwar Gold Standard

2011 ◽  
Vol 19 (2) ◽  
pp. 188-204 ◽  
Author(s):  
Jong Hee Park
Keyword(s):  
Time Series ◽  
Gold Standard ◽  
Time Series Data ◽  
Likelihood Function ◽  
Marginal Likelihood ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Method ◽  
Series Data ◽  
Ordinal Probit

In this paper, I introduce changepoint models for binary and ordered time series data based on Chib's hidden Markov model. The extension of the changepoint model to a binary probit model is straightforward in a Bayesian setting. However, detecting parameter breaks from ordered regression models is difficult because ordered time series data often have clustering along the break points. To address this issue, I propose an estimation method that uses the linear regression likelihood function for the sampling of hidden states of the ordinal probit changepoint model. The marginal likelihood method is used to detect the number of hidden regimes. I evaluate the performance of the introduced methods using simulated data and apply the ordinal probit changepoint model to the study of Eichengreen, Watson, and Grossman on violations of the “rules of the game” of the gold standard by the Bank of England during the interwar period.

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 A method for the estimation of functional brain connectivity from time-series data

2010 ◽  
Vol 4 (2) ◽  
pp. 133-149 ◽  
Author(s):  
A. Wilmer ◽  
M. H. E. de Lussanet ◽  
M. Lappe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Brain Connectivity ◽  
Series Data ◽  
Functional Brain

Impact of Fiscal Policy on Agricultural Output in Nigeria

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Adedoyin Isola LAWAL ◽  
Ernest Onyebuchi FIDELIS ◽  
Abiola Ayoopo BABAJIDE ◽  
Barnabas O. OBASAJU ◽  
Oluwatoyese OYETADE ◽  
...  
Keyword(s):  
Fiscal Policy ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Negative Impact ◽  
Capital Expenditure ◽  
Research Work ◽  
Estimation Method ◽  
Series Data ◽  
Agricultural Output ◽  
The Impact

This study examines the impact of fiscal policy on agricultural output in Nigeria using the most recent official data. The metrics for fiscal policy is government capital expenditure and custom duties on fertilizer. The study used annual time series data obtained from CBN annual statistical bulletin, NCS, and FIRS which was found to be stationary at the order of I(1) and I(0). The order of unit root test led to the use of ARDL estimation method employed in the empirical analysis of this research work. The study found evidence of both short and long run relationship between the variables (VAO, GEX, IDMF, and ACGSF) using both Johansen co-integration and ARDL Bounds test. Although government expenditure (GEX) to agricultural sector was found to be statistically insignificant which recommend that government should increase agriculture capital expenditure to ensure that its contribution is significant. Consequently, custom duties on fertilizer (IDMF) was found to be negatively signed and significant indicating a negative impact on agricultural output. This demands that the policy makers should be prudent in the use of fiscal policy instrument in achieving its desired objective.

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