Regression: Insignificant Intercept [duplicate]












1












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This question already has an answer here:




  • When is it ok to remove the intercept in a linear regression model?

    8 answers




I ran a regression and the intercept is statistically insignificant (the p-value is greater than 0.05). I tried to look in some textbooks as to how to handle this scenario but I am still unsure. One textbook I looked at 'Basic Econometrics'by Gujarati and Porter says that if the intercept is insignificant then we have a regression through the origin and that if we remove the intercept, in this case, the model will be more precise. On the other hand, 'Introductory Econometrics'by Chris Brooks says that even if the intercept is insignificant, we should not remove it from the model.



Which one of these textbooks is correct? Should I leave the insignificant intercept in the model or run a regression through the origin?






regression linear-model intercept







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marked as duplicate by kjetil b halvorsen, Peter Flom♦ regression
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 Nov 26 '18 at 10:48


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.














  • 1




    $begingroup$
    It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
    $endgroup$
    – heropup
    Nov 26 '18 at 7:32






  • 1




    $begingroup$
    Why do you think this is a problem?
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
    $endgroup$
    – user198848
    Nov 27 '18 at 0:19


















1












$begingroup$



This question already has an answer here:




  • When is it ok to remove the intercept in a linear regression model?

    8 answers




I ran a regression and the intercept is statistically insignificant (the p-value is greater than 0.05). I tried to look in some textbooks as to how to handle this scenario but I am still unsure. One textbook I looked at 'Basic Econometrics'by Gujarati and Porter says that if the intercept is insignificant then we have a regression through the origin and that if we remove the intercept, in this case, the model will be more precise. On the other hand, 'Introductory Econometrics'by Chris Brooks says that even if the intercept is insignificant, we should not remove it from the model.



Which one of these textbooks is correct? Should I leave the insignificant intercept in the model or run a regression through the origin?






regression linear-model intercept







share|cite|improve this question











$endgroup$



marked as duplicate by kjetil b halvorsen, Peter Flom♦ regression
Users with the  regression badge can single-handedly close regression questions as duplicates and reopen them as needed.

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 Nov 26 '18 at 10:48


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.














  • 1




    $begingroup$
    It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
    $endgroup$
    – heropup
    Nov 26 '18 at 7:32






  • 1




    $begingroup$
    Why do you think this is a problem?
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
    $endgroup$
    – user198848
    Nov 27 '18 at 0:19
















1












1








1





$begingroup$



This question already has an answer here:




  • When is it ok to remove the intercept in a linear regression model?

    8 answers




I ran a regression and the intercept is statistically insignificant (the p-value is greater than 0.05). I tried to look in some textbooks as to how to handle this scenario but I am still unsure. One textbook I looked at 'Basic Econometrics'by Gujarati and Porter says that if the intercept is insignificant then we have a regression through the origin and that if we remove the intercept, in this case, the model will be more precise. On the other hand, 'Introductory Econometrics'by Chris Brooks says that even if the intercept is insignificant, we should not remove it from the model.



Which one of these textbooks is correct? Should I leave the insignificant intercept in the model or run a regression through the origin?






regression linear-model intercept







share|cite|improve this question











$endgroup$





This question already has an answer here:




  • When is it ok to remove the intercept in a linear regression model?

    8 answers




I ran a regression and the intercept is statistically insignificant (the p-value is greater than 0.05). I tried to look in some textbooks as to how to handle this scenario but I am still unsure. One textbook I looked at 'Basic Econometrics'by Gujarati and Porter says that if the intercept is insignificant then we have a regression through the origin and that if we remove the intercept, in this case, the model will be more precise. On the other hand, 'Introductory Econometrics'by Chris Brooks says that even if the intercept is insignificant, we should not remove it from the model.



Which one of these textbooks is correct? Should I leave the insignificant intercept in the model or run a regression through the origin?





This question already has an answer here:




  • When is it ok to remove the intercept in a linear regression model?

    8 answers







regression linear-model intercept




regression linear-model intercept






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Nov 27 '18 at 0:18







user198848

















asked Nov 26 '18 at 2:17









user198848user198848

83




83




marked as duplicate by kjetil b halvorsen, Peter Flom♦ regression
Users with the  regression badge can single-handedly close regression questions as duplicates and reopen them as needed.

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 Nov 26 '18 at 10:48


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.









marked as duplicate by kjetil b halvorsen, Peter Flom♦ regression
Users with the  regression badge can single-handedly close regression questions as duplicates and reopen them as needed.

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 Nov 26 '18 at 10:48


This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.










  • 1




    $begingroup$
    It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
    $endgroup$
    – heropup
    Nov 26 '18 at 7:32






  • 1




    $begingroup$
    Why do you think this is a problem?
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
    $endgroup$
    – user198848
    Nov 27 '18 at 0:19
















  • 1




    $begingroup$
    It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
    $endgroup$
    – heropup
    Nov 26 '18 at 7:32






  • 1




    $begingroup$
    Why do you think this is a problem?
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
    $endgroup$
    – user198848
    Nov 27 '18 at 0:19










1




1




$begingroup$
It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
$endgroup$
– heropup
Nov 26 '18 at 7:32




$begingroup$
It bears mentioning that failure to detect a nonzero intercept is not the same thing as inferring that the intercept is actually zero.
$endgroup$
– heropup
Nov 26 '18 at 7:32




1




1




$begingroup$
Why do you think this is a problem?
$endgroup$
– kjetil b halvorsen
Nov 26 '18 at 9:16




$begingroup$
Why do you think this is a problem?
$endgroup$
– kjetil b halvorsen
Nov 26 '18 at 9:16












$begingroup$
I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
$endgroup$
– user198848
Nov 27 '18 at 0:19






$begingroup$
I don't think its a problem sorry. I re edited the question. Rather its a scenario I haven't encountered before.
$endgroup$
– user198848
Nov 27 '18 at 0:19












1 Answer
1






active

oldest

votes


















2












$begingroup$

If the true data generating process implies that Y=0 whenever X=0, then exclude the intercept, otherwise keep the intercept, even it is not significant. One example of this: response variable (Y) is distance the can run, and the covariate (X) is volume of gasoline consumed by the car. When X = 0, Y = 0.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
    $endgroup$
    – user198848
    Nov 27 '18 at 0:26


















1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









2












$begingroup$

If the true data generating process implies that Y=0 whenever X=0, then exclude the intercept, otherwise keep the intercept, even it is not significant. One example of this: response variable (Y) is distance the can run, and the covariate (X) is volume of gasoline consumed by the car. When X = 0, Y = 0.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
    $endgroup$
    – user198848
    Nov 27 '18 at 0:26
















2












$begingroup$

If the true data generating process implies that Y=0 whenever X=0, then exclude the intercept, otherwise keep the intercept, even it is not significant. One example of this: response variable (Y) is distance the can run, and the covariate (X) is volume of gasoline consumed by the car. When X = 0, Y = 0.






share|cite|improve this answer











$endgroup$













  • $begingroup$
    There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
    $endgroup$
    – user198848
    Nov 27 '18 at 0:26














2












2








2





$begingroup$

If the true data generating process implies that Y=0 whenever X=0, then exclude the intercept, otherwise keep the intercept, even it is not significant. One example of this: response variable (Y) is distance the can run, and the covariate (X) is volume of gasoline consumed by the car. When X = 0, Y = 0.






share|cite|improve this answer











$endgroup$



If the true data generating process implies that Y=0 whenever X=0, then exclude the intercept, otherwise keep the intercept, even it is not significant. One example of this: response variable (Y) is distance the can run, and the covariate (X) is volume of gasoline consumed by the car. When X = 0, Y = 0.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited Dec 30 '18 at 22:50









braaterAfrikaaner

1031




1031










answered Nov 26 '18 at 3:40









user158565user158565

5,3921518




5,3921518












  • $begingroup$
    There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
    $endgroup$
    – user198848
    Nov 27 '18 at 0:26


















  • $begingroup$
    There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
    $endgroup$
    – kjetil b halvorsen
    Nov 26 '18 at 9:16










  • $begingroup$
    Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
    $endgroup$
    – user198848
    Nov 27 '18 at 0:26
















$begingroup$
There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
$endgroup$
– kjetil b halvorsen
Nov 26 '18 at 9:16




$begingroup$
There are many reasons not to follow this advice. See stats.stackexchange.com/questions/102709/…
$endgroup$
– kjetil b halvorsen
Nov 26 '18 at 9:16












$begingroup$
Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
$endgroup$
– user198848
Nov 27 '18 at 0:26




$begingroup$
Thanks for the link. I read and saw that it was pointed out that the insignificance of the intercept is not enough to remove it from the model. So is it that we remove the intercept only when we have strong theoretical reason to believe that when x=0, y must also equal 0?
$endgroup$
– user198848
Nov 27 '18 at 0:26



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