Hi guys.
I'm planning on running CO on my time series data to correct for AR(1) and then perform feasible GLS using the estimated rho from the CO iterative procedure.
I've read that you can just regress y on x and include ad AR(1) term in EViews and it will produce results similar to if you manually do CO. Can anyone shed some light on this?
The output that I get when I run "ls y c x ar(1)" is titled ARMA Maximum Likelihood (BFGS) under Method even though I used least squares command. So my question is as follows: can I use this output and interpret coefficients like how I'd interpret the coefficients from the feasible GLS?
Cochrane Orcutt
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Re: Cochrane Orcutt
On the Options page of the Estimation dialog, change the ARMA method to CLS
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Re: Cochrane Orcutt
EViews Gareth wrote:On the Options page of the Estimation dialog, change the ARMA method to CLS
So if I change the estimation from ARMA to CLS, I can interpret the model like how I'd interpret my feasible GLS model obtained from getting estimated rho via CO procedure?

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Re: Cochrane Orcutt
Yes, although EViews does iterated CochraneOrcutt.
Re: Cochrane Orcutt
startz wrote:Yes, although EViews does iterated CochraneOrcutt.
By Iterated CO, you mean the part where it says Convergence achieved after XX iterations, right?
I've changed my estimation method to CLS. One thing worries me. My R squared is extremely high (0.997). Is that normal? Also, seeing as to how I've already corrected for the AR(1) via CO and Feasible GLS, does it make sense to use HAC robust standard errors?
Also, my DW statistic in the ARMA CLS output still points to the existence of AR(1) in the model, since my DW stat is 1.15. Is this normal?
If my AR(1) variable has a coefficient marginally >1, does this suggest that my process is mildly explosive?
It's quite a relief that I can just interpret the ARMA CLS output the same way I would interpret the FGLS output. So, to clarify, the coefficient of my regressors still measure marginal effects of that particular variable on Y while holding all else constant, right?
I'm assuming that the AR(1) variable doesn't require any interpretation with regards to coefficient?
Sorry for the whole host of questions. My prof seems to be taking forever to reply me.

 Nonnormality and collinearity are NOT problems!
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Re: Cochrane Orcutt
tutonic wrote:By Iterated CO, you mean the part where it says Convergence achieved after XX iterations, right?
right
I've changed my estimation method to CLS. One thing worries me. My R squared is extremely high (0.997). Is that normal? Also, seeing as to how I've already corrected for the AR(1) via CO and Feasible GLS, does it make sense to use HAC robust standard errors?
The high Rsquare is in part because of the explanatory power of the AR(1) termt
Also, my DW statistic in the ARMA CLS output still points to the existence of AR(1) in the model, since my DW stat is 1.15. Is this normal?
This may suggest second order serial correlation
If my AR(1) variable has a coefficient marginally >1, does this suggest that my process is mildly explosive?
unfortunately, yes
It's quite a relief that I can just interpret the ARMA CLS output the same way I would interpret the FGLS output. So, to clarify, the coefficient of my regressors still measure marginal effects of that particular variable on Y while holding all else constant, right?
I'm assuming that the AR(1) variable doesn't require any interpretation with regards to coefficient?
right
Sorry for the whole host of questions. My prof seems to be taking forever to reply me.

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Re: Cochrane Orcutt
It's actually not iterated CorchraneOrcutt. It's straight nonlinear least squares. In general, this doesn't matter, but sometimes does for instrumental variables. I believe we have a discussion of this in the manual.
Re: Cochrane Orcutt
startz wrote:tutonic wrote:By Iterated CO, you mean the part where it says Convergence achieved after XX iterations, right?
rightI've changed my estimation method to CLS. One thing worries me. My R squared is extremely high (0.997). Is that normal? Also, seeing as to how I've already corrected for the AR(1) via CO and Feasible GLS, does it make sense to use HAC robust standard errors?
The high Rsquare is in part because of the explanatory power of the AR(1) termtAlso, my DW statistic in the ARMA CLS output still points to the existence of AR(1) in the model, since my DW stat is 1.15. Is this normal?
This may suggest second order serial correlationIf my AR(1) variable has a coefficient marginally >1, does this suggest that my process is mildly explosive?
unfortunately, yesIt's quite a relief that I can just interpret the ARMA CLS output the same way I would interpret the FGLS output. So, to clarify, the coefficient of my regressors still measure marginal effects of that particular variable on Y while holding all else constant, right?
I'm assuming that the AR(1) variable doesn't require any interpretation with regards to coefficient?
rightSorry for the whole host of questions. My prof seems to be taking forever to reply me.
That's great. The mildly explosive process is actually very useful in my analysis.
So, should I then correct the standard errors using HAC as a safety measure or is it already redundant now since FGLS theoretically provides BLUE estimates?
If there is a possibility of second order correlation, can I just run "ls y c x AR(1) AR(2)" to fix this? I tweaked with my estimation and added in the AR(2) into my model. DW stat is now 2.53 so no issues there but now my coefficients are all very insignificant, when economic intuition says they ought to be. There shouldn't be a problem with the data since it's coming from a reputable source.
Or is there another way to correct for second order autocorrelation that I'm not aware of?
You're such a great source of help startz. Thanks!
EViews Glenn wrote:It's actually not iterated CorchraneOrcutt. It's straight nonlinear least squares. In general, this doesn't matter, but sometimes does for instrumental variables. I believe we have a discussion of this in the manual.
I'll take a look at it. Thanks.

 Nonnormality and collinearity are NOT problems!
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Re: Cochrane Orcutt
Unfortunately, having an AR(1) coefficient above 1 means that many things in the regression don't work as it implies the errors do not have a finite variance.
Adding an AR(2) is the right thing to do. The fact that the results aren't as expected doesn't make it wrong.
My guess is that your dependent variable is nonstationary, which may be accounting for a variety of problems.
Adding an AR(2) is the right thing to do. The fact that the results aren't as expected doesn't make it wrong.
My guess is that your dependent variable is nonstationary, which may be accounting for a variety of problems.
Re: Cochrane Orcutt
startz wrote:Unfortunately, having an AR(1) coefficient above 1 means that many things in the regression don't work as it implies the errors do not have a finite variance.
Adding an AR(2) is the right thing to do. The fact that the results aren't as expected doesn't make it wrong.
My guess is that your dependent variable is nonstationary, which may be accounting for a variety of problems.
It seems like I'm opening up a can of worms.
My estimated AR process was nonstationary when I only included the AR(1) in my previous specification. With the AR(2) term added in, there is no prompt for nonstationarity right now in my EViews output but the terms are still statistically insignificant.
I did the ADF test for all my variables and, when testing for unit root in 1st difference, only 1 of my X variable fails to reject the null, aka is nonstationary and my Y variable is also nonstationary.
However, the X and Y variable in question is stationary when testing unit root in 2nd differences.
Is there any way for me to proceed? Should I respecify my problem variables as [xx(2)] & [yy(2)]?
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