Difference between revisions of "Orange: Granger Causality"

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(Created page with "Sumber: https://orange.biolab.si/widget-catalog/time-series/granger_causality/ Test if one time series Granger-causes (i.e. can be an indicator of) another time series. Inp...")
 
 
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Test if one time series Granger-causes (i.e. can be an indicator of) another time series.
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Widget Granger Causality melakukan test untuk mencek apakah sebuah time series Granger-causes (yaitu, menjadi indikator dari) time series yang lain.
  
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Inputs
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==Input==
  
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    Time series: Time series as output by As Timeseries widget.
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Time series: Time series as output by As Timeseries widget.
  
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This widgets performs a series of statistical tests to determine the series that cause other series so we can use the former to forecast the latter.
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Widget Granger Causality melakukan beberapa test statistik untuk mengetahui bahwa series ini menyebabkan series lain sehingga kita dapat menggunakan series sebelumnya untuk mem-forcast series yang kemudian.
  
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    Desired level of confidence.
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[[File:Granger-causality-stamped.png|center|400px|thumb]]
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    Maximum lag to test to.
 
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    Runs the test.
 
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    Denotes the minimum lag at which one series can be said to cause another. In the first line of the example above, if we have the monthly unemployment rate time series for Austria, we can say something about unemployment rate in Hungary 10 months ahead.
 
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    The causing (antecedent) series.
 
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    The effect (consequent) series.
 
  
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The time series that Granger-cause the series you are interested in are good candidates to have in the same VAR model. But careful, even if one series is said to Granger-cause another, this doesn’t mean there really exists a causal relationship. Mind your conclusions.
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* Desired level of confidence.
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* Maximum lag to test to.
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* Runs the test.
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* Denotes the minimum lag at which one series can be said to cause another. In the first line of the example above, if we have the monthly unemployment rate time series for Austria, we can say something about unemployment rate in Hungary 10 months ahead.
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* The causing (antecedent) series.
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* The effect (consequent) series.
  
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Time series yang Granger-cause series adalah kandidat yang baik untuk dimasukan dalam VAR model yang sama. Akan tetapi kita harus berhati-hati, meskipun sebuah series di katakan Granger-cause yang lain, ini tidak berarti pasti ada hubungan antara series tersebut. Oleh karenanya, berhati-hati lah.
  
 
==Referensi==
 
==Referensi==

Latest revision as of 06:34, 7 April 2020

Sumber: https://orange.biolab.si/widget-catalog/time-series/granger_causality/


Widget Granger Causality melakukan test untuk mencek apakah sebuah time series Granger-causes (yaitu, menjadi indikator dari) time series yang lain.

Input

Time series: Time series as output by As Timeseries widget.

Widget Granger Causality melakukan beberapa test statistik untuk mengetahui bahwa series ini menyebabkan series lain sehingga kita dapat menggunakan series sebelumnya untuk mem-forcast series yang kemudian.

Granger-causality-stamped.png
  • Desired level of confidence.
  • Maximum lag to test to.
  • Runs the test.
  • Denotes the minimum lag at which one series can be said to cause another. In the first line of the example above, if we have the monthly unemployment rate time series for Austria, we can say something about unemployment rate in Hungary 10 months ahead.
  • The causing (antecedent) series.
  • The effect (consequent) series.

Time series yang Granger-cause series adalah kandidat yang baik untuk dimasukan dalam VAR model yang sama. Akan tetapi kita harus berhati-hati, meskipun sebuah series di katakan Granger-cause yang lain, ini tidak berarti pasti ada hubungan antara series tersebut. Oleh karenanya, berhati-hati lah.

Referensi

Pranala Menarik

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