Comments on: How to regress a stationary variable on a non stationary variable? Answer I http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/ The Power of Numeracy Wed, 16 May 2012 18:37:00 +0000 hourly 1 http://wordpress.org/?v=3.3.2 By: pkdvzx http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/#comment-5931 pkdvzx Mon, 20 Jul 2009 05:36:26 +0000 http://landshape.org/enm/?p=170#comment-5931 BTtwNA <a href="http://dinalehdxhjv.com/" rel="nofollow">dinalehdxhjv</a>, [url=http://yksfrzsonpsa.com/]yksfrzsonpsa[/url], [link=http://mjfufcckxulk.com/]mjfufcckxulk[/link], http://dybjtlxnocce.com/ BTtwNA dinalehdxhjv, [url=http://yksfrzsonpsa.com/]yksfrzsonpsa[/url], [link=http://mjfufcckxulk.com/]mjfufcckxulk[/link], http://dybjtlxnocce.com/

]]> By: Martin Ringo http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/#comment-5930 Martin Ringo Thu, 17 Aug 2006 22:35:07 +0000 http://landshape.org/enm/?p=170#comment-5930 Uli, With regard to your (rhetorical?) question, I don't know. Indeed, like you I presume that if the dependent variable is highly autocorrelated, let alone integrated, the residuals of the OLS regression will be serially correlated, unless the variables of the regression are co-integrated. But I thought the question in this thread was about a stationary (and presumably not highly serially correlated) dependent being regressed on a non-stationary, e.g. an integrated, independent variable(s). In which case one can proceed as normal with the caveat that one still has to look at the residuals. But back to the first topic, when is a variable integrated? A failure to reject on a Dickey-Fuller or the like? One gets as lot of failures to reject with, say, 0.8 first order autocorrelation - 50% or so? (It's been awhile since played with that stuff.) Thus, when does one move from correcting for serial correlation to modeling everything in first differences? After (too many) decades of applied work on stuff from plant growth rates to spot to futures ratios, I have seen a lot of non-stationary variables -- presumably more than I recognized -- but not a whole lot of integrated ones. I kind of look at unit roots as the macroeconomic terrorism scare of the late 1970s: they are there; they are real, but they aren't all that common. Uli,
With regard to your (rhetorical?) question, I don’t know. Indeed, like you I presume that if the dependent variable is highly autocorrelated, let alone integrated, the residuals of the OLS regression will be serially correlated, unless the variables of the regression are co-integrated.

But I thought the question in this thread was about a stationary (and presumably not highly serially correlated) dependent being regressed on a non-stationary, e.g. an integrated, independent variable(s). In which case one can proceed as normal with the caveat that one still has to look at the residuals.

But back to the first topic, when is a variable integrated? A failure to reject on a Dickey-Fuller or the like? One gets as lot of failures to reject with, say, 0.8 first order autocorrelation – 50% or so? (It’s been awhile since played with that stuff.) Thus, when does one move from correcting for serial correlation to modeling everything in first differences? After (too many) decades of applied work on stuff from plant growth rates to spot to futures ratios, I have seen a lot of non-stationary variables — presumably more than I recognized — but not a whole lot of integrated ones. I kind of look at unit roots as the macroeconomic terrorism scare of the late 1970s: they are there; they are real, but they aren’t all that common.

]]> By: Martin Ringo http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/#comment-6229 Martin Ringo Thu, 17 Aug 2006 22:35:00 +0000 http://landshape.org/enm/?p=170#comment-6229 Uli, With regard to your (rhetorical?) question, I don't know. Indeed, like you I presume that if the dependent variable is highly autocorrelated, let alone integrated, the residuals of the OLS regression will be serially correlated, unless the variables of the regression are co-integrated. But I thought the question in this thread was about a stationary (and presumably not highly serially correlated) dependent being regressed on a non-stationary, e.g. an integrated, independent variable(s). In which case one can proceed as normal with the caveat that one still has to look at the residuals. But back to the first topic, when is a variable integrated? A failure to reject on a Dickey-Fuller or the like? One gets as lot of failures to reject with, say, 0.8 first order autocorrelation - 50% or so? (It's been awhile since played with that stuff.) Thus, when does one move from correcting for serial correlation to modeling everything in first differences? After (too many) decades of applied work on stuff from plant growth rates to spot to futures ratios, I have seen a lot of non-stationary variables -- presumably more than I recognized -- but not a whole lot of integrated ones. I kind of look at unit roots as the macroeconomic terrorism scare of the late 1970s: they are there; they are real, but they aren't all that common. Uli,
With regard to your (rhetorical?) question, I don’t know. Indeed, like you I presume that if the dependent variable is highly autocorrelated, let alone integrated, the residuals of the OLS regression will be serially correlated, unless the variables of the regression are co-integrated.

But I thought the question in this thread was about a stationary (and presumably not highly serially correlated) dependent being regressed on a non-stationary, e.g. an integrated, independent variable(s). In which case one can proceed as normal with the caveat that one still has to look at the residuals.

But back to the first topic, when is a variable integrated? A failure to reject on a Dickey-Fuller or the like? One gets as lot of failures to reject with, say, 0.8 first order autocorrelation – 50% or so? (It’s been awhile since played with that stuff.) Thus, when does one move from correcting for serial correlation to modeling everything in first differences? After (too many) decades of applied work on stuff from plant growth rates to spot to futures ratios, I have seen a lot of non-stationary variables — presumably more than I recognized — but not a whole lot of integrated ones. I kind of look at unit roots as the macroeconomic terrorism scare of the late 1970s: they are there; they are real, but they aren’t all that common.

]]> By: Uli Leuchtmann http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/#comment-5929 Uli Leuchtmann Wed, 16 Aug 2006 20:58:03 +0000 http://landshape.org/enm/?p=170#comment-5929 Martin. How in the world can the residuals of a (linear) regression of an I(1) variable on an I(0) variable be white noise? The (stochastic) trend of the I(1) variable will (with T -> infty) dominate all other variation. Therefore, the residual will be I(1), too. The situation might change if you have two I(1) variables on the RHS, given that they are co-integrated. But that's a different story... Uli Martin.
How in the world can the residuals of a (linear) regression of an I(1) variable on an I(0) variable be white noise? The (stochastic) trend of the I(1) variable will (with T -> infty) dominate all other variation. Therefore, the residual will be I(1), too. The situation might change if you have two I(1) variables on the RHS, given that they are co-integrated. But that’s a different story…
Uli

]]> By: Uli Leuchtmann http://landshape.org/enm/how-to-regress-a-stationary-variable-on-a-non-stationary-variable/#comment-6228 Uli Leuchtmann Wed, 16 Aug 2006 20:58:00 +0000 http://landshape.org/enm/?p=170#comment-6228 Martin. How in the world can the residuals of a (linear) regression of an I(1) variable on an I(0) variable be white noise? The (stochastic) trend of the I(1) variable will (with T -> infty) dominate all other variation. Therefore, the residual will be I(1), too. The situation might change if you have two I(1) variables on the RHS, given that they are co-integrated. But that's a different story... Uli Martin.
How in the world can the residuals of a (linear) regression of an I(1) variable on an I(0) variable be white noise? The (stochastic) trend of the I(1) variable will (with T -> infty) dominate all other variation. Therefore, the residual will be I(1), too. The situation might change if you have two I(1) variables on the RHS, given that they are co-integrated. But that’s a different story…
Uli

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