Re: [R] error in vcovNW

2015-12-19 Thread Saba Sehrish via R-help 
Hi
Thanks for the reminder.
Actually I want to analyse whether present value of variable A is Granger 
caused by lag values of B and test linear hypothesis "B1,B2,B3,B4,B5=0".
Therefore, to get robust standard error NeweyWest estimates are applied.
Saba 

On Saturday, 19 December 2015, 23:26, Achim Zeileis 
 wrote:
 

 On Sat, 19 Dec 2015, Saba Sehrish wrote:

> Thank you. The issue is resolved by scaling the data in millions.

That solves the numerical problem but the second issue (inappropriateness 
of the Newey-West estimator for an autoregressive model) persists.

> Saba
> 
> 
> On Saturday, 19 December 2015, 15:06, Achim Zeileis
>  wrote:
> 
> 
> On Sat, 19 Dec 2015, Saba Sehrish via R-help wrote:
> 
> > Hi I am using NeweyWest standard errors to correct lm( ) output. For
> example:
> > lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
> > vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))
> >
> > I am using package(sandwich) for NeweyWest. Now when I run this command,
> it gives following error:
> > Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum))
> :system is computationally singular: reciprocal condition number =
> 7.49468e-18
> >
> > Attached herewith is data for A, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are
> > simply lag variables. Can you help me removing this error please?
> 
> Without trying to replicate the error, there are at least two issues:
> 
> (1) You should scale your data to use more reasonable orders of magnitude,
> e.g., in millions. This will help avoiding numerical problems.
> 
> (2) More importantly, you should not employ HAC/Newey-West standard errors
> in autoregressive models. If you use an autoregressive specification, you
> should capture all relevant autocorrelations - and then no HAC estimator
> is necessary. Alternatively, one may treat autocorrelation as a nuisance
> parameter and not model it - but instead capture it in HAC standard
> errors. Naturally, the former strategy will typically perform better if
> the autocorrelations are more substantial.
> 
> > Saba
> 
> 
> 
>

  
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Re: [R] error in vcovNW

2015-12-19 Thread Achim Zeileis 

On Sat, 19 Dec 2015, Saba Sehrish wrote:


Thank you. The issue is resolved by scaling the data in millions.


That solves the numerical problem but the second issue (inappropriateness 
of the Newey-West estimator for an autoregressive model) persists.



Saba


On Saturday, 19 December 2015, 15:06, Achim Zeileis
 wrote:


On Sat, 19 Dec 2015, Saba Sehrish via R-help wrote:

> Hi I am using NeweyWest standard errors to correct lm( ) output. For
example:
> lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
> vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))
>
> I am using package(sandwich) for NeweyWest. Now when I run this command,
it gives following error:
> Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum))
:system is computationally singular: reciprocal condition number =
7.49468e-18
>
> Attached herewith is data for A, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are
> simply lag variables. Can you help me removing this error please?

Without trying to replicate the error, there are at least two issues:

(1) You should scale your data to use more reasonable orders of magnitude,
e.g., in millions. This will help avoiding numerical problems.

(2) More importantly, you should not employ HAC/Newey-West standard errors
in autoregressive models. If you use an autoregressive specification, you
should capture all relevant autocorrelations - and then no HAC estimator
is necessary. Alternatively, one may treat autocorrelation as a nuisance
parameter and not model it - but instead capture it in HAC standard
errors. Naturally, the former strategy will typically perform better if
the autocorrelations are more substantial.

> Saba





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and provide commented, minimal, self-contained, reproducible code.

[R] error in vcovNW

2015-12-18 Thread Saba Sehrish via R-help 
Hi I am using NeweyWest standard errors to correct lm( ) output. For example:
lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))

I am using package(sandwich) for NeweyWest. Now when I run this command, it 
gives following error:
Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum)) :system 
is computationally singular: reciprocal condition number = 7.49468e-18

Attached herewith is data for A, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are simply lag 
variables. Can you help me removing this error please?
SabaA   B
739171876.1 -30023111.44
487266676   21283768.23
372851476.2 -40442678.43
63229603.27 10656220.9
42006490.16 -11533497.55
190745334.6 -5394116.27
172710138.6 -15091006.48
231059302.6 23568469.87
519602621.8 64131342.59
997358074.8 23623980.29
291864614.4 65303351.45
80844732.71 69354076.9
701170068.3 106386633.8
440463911.3 105165515.5
67256920.87 57943316.76
64101070.8  50209212.89
-71028831.0331292473.88
-197854142.532805225.46
-189290263.34638671.93
-520470164.7962640792.4
-471115277.3-1093666458
-955868238  -102261874.8
-1098715609 -101020121.9
-738546938.5-6916.12
-1085874990 -136045443.9
193157212.1 -2473692.63
-6269415.53 -28891931
199824564.8 5127403.1
302376261.5 6655585.13
-67851220.11-13741489.54
-370952947  -24219268.21
34404761.25 27283468.9
-428849252.4-85765593.88
-924463014  -112574045.5
-495270249.6-2965265.14
-668618574.5-39930551.16
-10436100.7790010638.89
-281751636.5-22157882.66
-385194083  43186980.6
104681563.1 40450660.38
-15283793.5260454998.18
-26567438.3752683189.8
-98612309.0825319905.01
21402708.99 44019777.51
-74846057.0545104511.78
-951203476.39858962.32
-338231274.186293283.74
-424023473.5102767273.6
20027128.13 185851266
-815545.8   163237321.2
46996041.85 194808435
134571135.3 122988858.9
-183703166  53086443.78
212728895.5 73301796.9
-197466304.2-11713239.02
-393762814.711580149.74
-343324235.6-13610112.45
-260888613.910047787.51
-759009960.6-151251490.8
-383721497  -151251490.8
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Re: [R] error in vcovNW

2015-12-18 Thread Saba Sehrish via R-help 
Thank you. The issue is resolved by scaling the data in millions.
Saba 

On Saturday, 19 December 2015, 15:06, Achim Zeileis 
 wrote:
 

 On Sat, 19 Dec 2015, Saba Sehrish via R-help wrote:

> Hi I am using NeweyWest standard errors to correct lm( ) output. For example:
> lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
> vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))
>
> I am using package(sandwich) for NeweyWest. Now when I run this command, it 
> gives following error:
> Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum)) 
> :system is computationally singular: reciprocal condition number = 7.49468e-18
>
> Attached herewith is data for A, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are 
> simply lag variables. Can you help me removing this error please?

Without trying to replicate the error, there are at least two issues:

(1) You should scale your data to use more reasonable orders of magnitude, 
e.g., in millions. This will help avoiding numerical problems.

(2) More importantly, you should not employ HAC/Newey-West standard errors 
in autoregressive models. If you use an autoregressive specification, you 
should capture all relevant autocorrelations - and then no HAC estimator 
is necessary. Alternatively, one may treat autocorrelation as a nuisance 
parameter and not model it - but instead capture it in HAC standard 
errors. Naturally, the former strategy will typically perform better if 
the autocorrelations are more substantial.

> Saba

  
[[alternative HTML version deleted]]

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and provide commented, minimal, self-contained, reproducible code.

Re: [R] error in vcovNW

2015-12-18 Thread Achim Zeileis 

On Sat, 19 Dec 2015, Saba Sehrish via R-help wrote:


Hi I am using NeweyWest standard errors to correct lm( ) output. For example:
lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5)
vcovNW<-NeweyWest(lm(A~A1+A2+A3+A4+A5+B1+B2+B3+B4+B5))

I am using package(sandwich) for NeweyWest. Now when I run this command, it 
gives following error:
Error in solve.default(diag(ncol(umat)) - apply(var.fit$ar, 2:3, sum)) :system 
is computationally singular: reciprocal condition number = 7.49468e-18

Attached herewith is data for A, A1,A2,A3,A4,A5,B1,B2,B3,B4,B5 are 
simply lag variables. Can you help me removing this error please?


Without trying to replicate the error, there are at least two issues:

(1) You should scale your data to use more reasonable orders of magnitude, 
e.g., in millions. This will help avoiding numerical problems.


(2) More importantly, you should not employ HAC/Newey-West standard errors 
in autoregressive models. If you use an autoregressive specification, you 
should capture all relevant autocorrelations - and then no HAC estimator 
is necessary. Alternatively, one may treat autocorrelation as a nuisance 
parameter and not model it - but instead capture it in HAC standard 
errors. Naturally, the former strategy will typically perform better if 
the autocorrelations are more substantial.



Saba

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and provide commented, minimal, self-contained, reproducible code.