gov1000-list December 2004

gov1000-list@lists.fas.harvard.edu

29 participants
56 discussions

by Kevin Quinn

> so we aren't supposed to be doing any regressions at all in problem > 1 - just looking at the data and doing whatever we can to make the > relationship more linear? > Yes, for problem 1 you don't need to fit any linear regression models. You could just eyeball the necessary bivariate relationships and transform them to linear relationships if the original untransformed relationships are not linear. If the original relationships look pretty close to linear you can just report that fact. My suggestion of using a loess curve is not necessary, but it might help you discern empirical relationships in some cases. Hope this helps. Best, Kevin > so we aren't supposed to be doing any regressions at all in problem > 1 - just looking at the data and doing whatever we can to make the > relationship more linear? >

19 years, 5 months

WLS with bad weights

by Kevin Quinn

Hi Everyone, Here is the answer to Vipin's question about the possibility of getting arbitrarily good fits to the data (as judged by R^2 and size of t-statistics) by choosing a particular set of weights for WLS. This is something that it is possible to do. For instance, suppose you have n observations and k columns in X. Then give k arbitrarily chosen observations weight 1 and the remaining observations weight very very close to 0. WLS in this case is basically the same thing as OLS applied to only the observations with weight 1. Since you have as many columns in X as observations you will get an R^2 of 1 and huge t-statistics. For example: > x <- rnorm(100) > y <- rnorm(100) > w <- c(1, 1, rep(1e-10, 98)) > ols.out <- lm(y~x) > wls.out <- lm(y~x, weights=w) > summary(ols.out) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -2.52978 -0.77233 0.02590 0.64279 2.05652 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.0003379 0.0969588 0.003 0.997 x 0.0708301 0.0921538 0.769 0.444 Residual standard error: 0.9696 on 98 degrees of freedom Multiple R-Squared: 0.005992, Adjusted R-squared: -0.004151 F-statistic: 0.5908 on 1 and 98 DF, p-value: 0.444 > summary(wls.out) Call: lm(formula = y ~ x, weights = w) Residuals: Min 1Q Median 3Q Max -4.653e-05 -1.233e-05 -3.695e-07 8.839e-06 5.572e-05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.637e-01 1.431e-05 11441 <2e-16 *** x -1.209e+00 2.493e-05 -48497 <2e-16 *** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 1.678e-05 on 98 degrees of freedom Multiple R-Squared: 1, Adjusted R-squared: 1 F-statistic: 2.352e+09 on 1 and 98 DF, p-value: < 2.2e-16 The moral of the story is that if you have to use WLS you should really believe (based on a priori knowledge combined with diagnostic plots) that your weights are sensibly related to the error variance and that observations that are getting close to 0 weight really are so extreme that it is worth basically tossing them out of the dataset. When in doubt, White's var-cov estimator is a pretty safe way to get standard errors. Reporting several sets of results (OLS, WLS, and OLS with White SEs) is also a good way to convince readers that your results are not just the artifact of a crazy weighting decision. Hope this helps. Best, Kevin ------------------------------------------------------ Kevin Quinn Assistant Professor Department of Government and Center for Basic Research in the Social Sciences 34 Kirkland Street Harvard University Cambridge, MA 02138

19 years, 5 months

Hot specials

by Kecia Townsend

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19 years, 5 months

Washington Mutual: Urgent Security Notification For Client [Wed, 01 Dec 2004 05:24:43 -0700]

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19 years, 5 months

FINAL PAPER DUE DATE

by Kevin Quinn

Just a reminder that the final papers are due JANUARY 3 AT 5PM. Note that the papers are no longer due on the last day of class. This will give you two extra weeks to complete the paper. Best, Kevin ------------------------------------------------------ Kevin Quinn Assistant Professor Department of Government and Center for Basic Research in the Social Sciences 34 Kirkland Street Harvard University Cambridge, MA 02138

19 years, 5 months

Local R & XEmacs

by Ryan Thomas Moore

Hi, everyone. Many people have begun to think about working on Gov1000 or other course papers over the winter break. For those who may not have fast internet connections at home, working on your local machine may be preferable at times to working on the remote XDesktop. Some students have downloaded R and XEmacs for their local machine, in order to use these programs without being connected. If you want to do so, see the 2-page hyperlinked guide at http://www.courses.fas.harvard.edu/~gov2001/handouts/Windows/Xemacs.pdf If anyone needs help with this process, let me know and I'm happy to get your laptop set up. Others in the class might be able to help as well. Best, Ryan ------------------------------------------ Ryan T. Moore ~ Government & Social Policy Ph.D. Candidate ~ Harvard University Homepage: http://www.people.fas.harvard.edu/~rtmoore/ Gov1000: http://www.courses.fas.harvard.edu/~gov1000/

19 years, 5 months

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gov1000-list December 2004