Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables

Gulasekaran Rajaguru, Tilak Abeysinghe

Research output: Contribution to conferencePresentationResearchpeer-review

Abstract

Concurrent with Mamingi findings using Monte Carlo experiment (Economics
Letters, 1996, 52, 7-14), this paper analyses the existence of Granger causality distortion
in error correction models using the relationship between the theoretical cross
covariances of the basic and aggregated process. The analytical result confirms that the
causal distortion depends on the degree of cointegaration, the data span, the sample size
and the type of aggregation. The more prominent result is that the systematic sampling
preserves the causal direction among the variables while most of the causal distortion
occurs due to temporal aggregation. For the bivariate cointegarated system with no short
run dynamics, this paper develops the testing procedure to test for the long run Granger
causality between the variables in the basic disaggregated form using the temporally
aggregated data.
Original languageEnglish
Publication statusPublished - 2001
Externally publishedYes
Event12th (EC)2 Conference - Causality and Exogeneity in Econometrics: Causality and Exogeneity in Econometrics - Centre for Operation Research and Econometrics (CORE), Louvain-la-Neuve, Belgium
Duration: 12 Dec 200115 Dec 2001
https://sites.google.com/site/ecpower2/conferences

Conference

Conference12th (EC)2 Conference - Causality and Exogeneity in Econometrics
CountryBelgium
CityLouvain-la-Neuve
Period12/12/0115/12/01
Internet address

Fingerprint

Testing
Granger causality
Integrated
Monte Carlo experiment
Error correction model
Temporal aggregation

Cite this

Rajaguru, G., & Abeysinghe, T. (2001). Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables. 12th (EC)2 Conference - Causality and Exogeneity in Econometrics, Louvain-la-Neuve, Belgium.
Rajaguru, Gulasekaran ; Abeysinghe, Tilak. / Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables. 12th (EC)2 Conference - Causality and Exogeneity in Econometrics, Louvain-la-Neuve, Belgium.
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author = "Gulasekaran Rajaguru and Tilak Abeysinghe",
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Rajaguru, G & Abeysinghe, T 2001, 'Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables' 12th (EC)2 Conference - Causality and Exogeneity in Econometrics, Louvain-la-Neuve, Belgium, 12/12/01 - 15/12/01, .

Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables. / Rajaguru, Gulasekaran; Abeysinghe, Tilak.

2001. 12th (EC)2 Conference - Causality and Exogeneity in Econometrics, Louvain-la-Neuve, Belgium.

Research output: Contribution to conferencePresentationResearchpeer-review

TY - CONF

T1 - Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables

AU - Rajaguru, Gulasekaran

AU - Abeysinghe, Tilak

PY - 2001

Y1 - 2001

N2 - Concurrent with Mamingi findings using Monte Carlo experiment (EconomicsLetters, 1996, 52, 7-14), this paper analyses the existence of Granger causality distortionin error correction models using the relationship between the theoretical crosscovariances of the basic and aggregated process. The analytical result confirms that thecausal distortion depends on the degree of cointegaration, the data span, the sample sizeand the type of aggregation. The more prominent result is that the systematic samplingpreserves the causal direction among the variables while most of the causal distortionoccurs due to temporal aggregation. For the bivariate cointegarated system with no shortrun dynamics, this paper develops the testing procedure to test for the long run Grangercausality between the variables in the basic disaggregated form using the temporallyaggregated data.

AB - Concurrent with Mamingi findings using Monte Carlo experiment (EconomicsLetters, 1996, 52, 7-14), this paper analyses the existence of Granger causality distortionin error correction models using the relationship between the theoretical crosscovariances of the basic and aggregated process. The analytical result confirms that thecausal distortion depends on the degree of cointegaration, the data span, the sample sizeand the type of aggregation. The more prominent result is that the systematic samplingpreserves the causal direction among the variables while most of the causal distortionoccurs due to temporal aggregation. For the bivariate cointegarated system with no shortrun dynamics, this paper develops the testing procedure to test for the long run Grangercausality between the variables in the basic disaggregated form using the temporallyaggregated data.

M3 - Presentation

ER -

Rajaguru G, Abeysinghe T. Testing for Granger causality between Temporally Aggregated and Systematically sampled Co-integrated Variables. 2001. 12th (EC)2 Conference - Causality and Exogeneity in Econometrics, Louvain-la-Neuve, Belgium.