Testing subspace Granger causality

Al-Sadoon, Majid M.

doi:10.1016/j.ecosta.2017.08.003

Cited by 6 publications

(4 citation statements)

References 53 publications

(60 reference statements)

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“…2. The estimation model of service growth with e-money Before estimating VEC Granger causality model, several steps must be undergone such as testing stationary data with the unit root test, the im-pesaran-shin test (Pesaran et al, 2000) and Johansen co-integration test (Al-Sadoon, 2019).…”

Section: Methodsmentioning

confidence: 99%

The causality among e-money, manufacturing, services and money supply: an empirical evidence of ASEAN countries

Heryadi

Azwardi

Sukanto

2020

JES

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The economic transition happened in ASEAN countries caused the increased growth of e-money use. This study was established to discover the causal relation between the growth in manufacturing and service sector and the e-money usage through causality panel approach. Furthermore, this study also focused on finding the causality among the economic growth in manufacturing and service sector, e-money and money supply during 2008-2018 period in the region. The result of the study showed that: (1) there was a one-way causality between the growth in service sector and e-money use, (2) there was no causality between e-money use and the growth in manufacturing sector (3) the growth of money supply provided causal effect on the increase of e-money use.

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Section: Methodsmentioning

confidence: 99%

The causality among e-money, manufacturing, services and money supply: an empirical evidence of ASEAN countries

Heryadi

Azwardi

Sukanto

2020

JES

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“…Hill (2007) develops a long‐run Granger causality test for a trivariate VAR process

\left({X}_t,{Y}_t,{Z}_t\right)

, where the causality of interest is basically investigated with full model information and the proposed test strategy is restricted on the case that

{Z}_t

includes merely one variable (i.e., a univariate set). Al‐Sadoon (2014, 2019) discusses the estimation and testing of subspace Granger causality from

{X}_t

{Y}_t

, where

{X}_t

and

{Y}_t

are both multivariate time series in vector form. The idea is that Granger causality can be limited to subspaces of

{X}_t

and

{Y}_t

, say,

{X}_t

may not help on predicting co‐movements of

{Y}_t

in all directions, and, not all directions for the co‐movements of

{X}_t

can help predict

{Y}_t

.…”

Section: Introductionmentioning

confidence: 99%

Granger causality tests based on reduced variable information

Tseng,

Hung,

Nakano

2023

Journal Time Series Analysis

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Granger causality is a classical and important technique for measuring predictability from one group of time series to another by incorporating information of the variables described by a full vector autoregressive (VAR) process. However, in some applications economic forecasts need to be made based on information provided merely by a portion of variates (e.g., removal of a listed stock due to halting, suspension or delisting). This requires a new formulation of forecast based on an embedded subprocess of VAR, whose theoretical properties are often difficult to obtain. To avoid the issue of identifying the VAR subprocess, we propose a computation‐based approach so that sophisticated predictions can be made by utilizing a reduced variable information set estimated from sampled data. Such estimated information set allows us to develop a suitable statistical hypothesis testing procedure for characterizing all designated Granger causal relationships, as well as a useful graphical tool for presenting the causal structure over the prediction horizon. Finally, simulated data and a real example from the stock markets are used to illustrate the proposed method.

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“…A recent study on the role of individual nodes in a complex network [ 22 ] may be viewed as another effort. (Causality analyses between subspaces with the classical approaches are rare; a few examples are [ 23 , 24 ], etc.) However, a rigorous formalism for more generic problems (e.g., with mutual causality involved) is yet to be implemented.…”

Section: Introductionmentioning

confidence: 99%

The Causal Interaction between Complex Subsystems

Liang

2021

Entropy

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Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.

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Testing subspace Granger causality

Cited by 6 publications

References 53 publications

The causality among e-money, manufacturing, services and money supply: an empirical evidence of ASEAN countries

The causality among e-money, manufacturing, services and money supply: an empirical evidence of ASEAN countries

Granger causality tests based on reduced variable information

The Causal Interaction between Complex Subsystems

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