Message-ID: <20050925144109.GT437@lubyanka.local>
Date: 2005-09-25T14:41:09Z
From: Ajay Shah
Subject: Question on lm(): When does R-squared come out as NA?
I have a situation with a large dataset (3000+ observations), where
I'm doing lags as regressors, where I get:
Call:
lm(formula = rj ~ rM + rM.1 + rM.2 + rM.3 + rM.4)
Residuals:
1990-06-04 1994-11-14 1998-08-21 2002-03-13 2005-09-15
-5.64672 -0.59596 -0.04143 0.55412 8.18229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.003297 0.017603 -0.187 0.851
rM 0.845169 0.010522 80.322 <2e-16 ***
rM.1 0.116330 0.010692 10.880 <2e-16 ***
rM.2 0.002044 0.010686 0.191 0.848
rM.3 0.013181 0.010692 1.233 0.218
rM.4 0.009587 0.010525 0.911 0.362
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.044 on 3532 degrees of freedom
Multiple R-Squared: NA, Adjusted R-squared: NA
F-statistic: NA on 5 and 3532 DF, p-value: NA
rM.1, rM.2, etc. are lagged values of rM. The OLS seems fine in every
respect, except that there is an NA as the multiple R-squared. I will
be happy to give sample data to someone curious about what is going
on. I wondered if this was a well-known pathology. The way I know it,
if the data allows computation of (X'X)^{-1}, one can compute the R2.
--
Ajay Shah Consultant
ajayshah at mayin.org Department of Economic Affairs
http://www.mayin.org/ajayshah Ministry of Finance, New Delhi