Gov2001 March 2009

gov2001@lists.gking.harvard.edu

28 participants
47 discussions

[gov2001-l] EXO 1

by charlotte.cavaille＠gmail.com

hi! Quick (but fundamental question) in exercise 1 gamma needs to be positive, so I re-parameterized...In order to get the right MLE from optim() i didn't forget to "re-parameterize" again...and i get the same result as the one i found analytically. However, things change when i look for the SE. Indeed, I get different answers analytically and with R (using the hessian). I know this comes from the re-parameterization because I don't have this issue when I change the whole function and do not re-parameterize gamma. So my question is: - if I re-parametterize, how do I apply the transformation to the hessian to get the right result how does that fit with the section notes that follows, why do we take "pnorm" (this is the first transformation that is applied in the ll.binom function) of "opt.1000 - 1.96*se" and not of "se" for instance??? #binomial log-likelihood (N = # of trials for each observation) ll.binom <- function(par, y, N){ # reparameterize pi; only search over [0,1] p <- pnorm(par) # log-likelihood out <- sum(y*log(p) + (N-y)*log(1-p)) return(out) } # compare to wald ci se <- sqrt(solve(-optim(par=2, fn=ll.binom, y=samp.1000, N=10, method="BFGS", control=list(fnscale=-1), hessian=T)$hessian)) #$ wald.ci <- c( pnorm(opt.1000 - 1.96*se), pnorm(opt.1000 + 1.96*se)) wald.ci # 0.7364839 0.7535663 - if i do not re-parameterize in order to be done with it and have both my analytical and my R result fit, how can i justify i am not re-parameterizing gamma?! thanks! charlotte

14 years, 12 months

[gov2001-l] announcements

by woolfalk＠fas.harvard.edu

Hi All, April 6, 2009, 2pm is the due date for your replication of another student groups' replication. You should email me and Patrick as well as all of the members of the student group whose work you replicated a *pdf* memo pointing out ways to make their paper and analysis better. You will be evaluated based on how helpful, not how destructive, you are. Readings for this week are: (1) King, Gary and Langche Zeng. ?The Dangers of Extreme Counterfactuals,? Political Analysis, 14, 2, (2007): 131-159. (2) Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart.?Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference,? Political Analysis, 15 (2007): 199-236. My office hours this Thursday will only be from 2 till 3 pm. Thanks, Miya Woolfalk -- Miya Woolfalk Ph.D. Student Harvard University Government and Social Policy

15 years

[gov2001-l] estimating models with weighted observations

by kajaros＠fas.harvard.edu

Hey folks, Don't know if anyone else is trying to use the "weights" argument in Zelig. Sam and I are using weighted survey data to try to estimate an oprobit model. For one of the models we are estimating, when we run Zelig with a vector of observation weights entered, we get coefficient estimates that are very close to our author's, but our standard errors are extremely small (far smaller than the author's). We aren't sure why this is or how to correct the problem. For another very similar model, including a vector of weights just results in the following error message: zelig.us2 <- zelig(as.factor(jobcomm) ~ demid + repid + hhunion + female + nonwhite + income + educ + educinc + age + hhlayoffs + ncentral + south + west, model="oprobit", weights=starting2$weight, data=starting2) Error in function (formula, data, weights, start, ..., subset, na.action, : attempt to find suitable starting values failed In addition: Warning messages: 1: In glm.fit(X, y1, wt, family = binomial("probit"), offset = offset) : algorithm did not converge 2: In glm.fit(X, y1, wt, family = binomial("probit"), offset = offset) : fitted probabilities numerically 0 or 1 occurred If anyone has any thoughts as to what might be going on, we would love to hear them. Thanks for your help, Kyle

15 years

[gov2001-l] Zelig question

by wschmidt＠hbs.edu

Does anyone know how to instruct Zelig to run a regression without the intercept? Thanks! Bill

15 years

[gov2001-l] Replication Draft Due Tomorrow

by plam＠fas.harvard.edu

Hi all, Just a reminder that your replication draft is due tomorrow. We want 6 things from your group. (1) A pdf of the article you are replicating. (2) A *PDF *of the draft of your paper. This draft must include the names (and email addresses) of your coauthors, yourreplication of the main tables/figures in the paper you are replicating and a brief discussion of how you intend to improve the existing analysis. (3) A file containing the data you used to perform the replication. (4) A code book (or key) for the data used in your replication. (5) A txt or r file containing all of the code used in your replication (the code should be well commented so others can easily follow along). (6) Anything else that may be required for another group to successfully replicate your replication of the paper you have selected. Please put all 6 things into one zip file and email it to both Miya and I by 2pm tomorrow. -- Patrick Lam Department of Government and Institute for Quantitative Social Science, Harvard University http://www.people.fas.harvard.edu/~plam

15 years

[gov2001-l] Beta Distribution Reparameterization

by clee＠hbs.edu

Hi all, I'm struggling with this problem and I am wondering if any of you could help me out: So my response variable y is in the open interval (0,1), and I want to model a dataset using the Beta distribution. My question deals with how to reparameterize the shape parameters a and b and link it to my covariates (x's) using a logit function. The best thing I've come up with so far is: 1) Define a new parameter mu, such that logit(mu) = X %*% coefficients 2) Define a new parameter s = a + b. We also know that var(y) = (mu*(1-mu))/(1+s). 3) Through this, I can solve for a and b, this is my reparamterization. The problem I keep running into is that, by solving for s from the equation var(y) = (mu*(1-mu))/(1+s), s turns out to be negative, since mu is always less than 1 and var(y) is also less than 1. Since the beta distribution is only defined for s>0, my optim routine just stops. Could someone who has experience with this tell me what piece of insight am I missing? There must be something I'm missing since my s values are all negative. Thanks a bunch! Clarence

15 years, 1 month

[gov2001-l] pset 6

by plam＠fas.harvard.edu

Problem set 6 has been graded. If you did not turn in a paper copy, grades are available in the dropbox. Just a couple notes: 1. Don't forget to set the variables to their medians (Zelig defaults to the means). Also, when you have interaction terms, you need to set it to the interaction of the medians, not the median of the interactions. For example, it should be median(target)*median(coop), not median(target*coop). Luckily Zelig does it correctly if you specify the lower order terms. 2. Scientific notation on tables is not good practice. 3. The cutpoints are also parameters in the ordered probit model. -- Patrick Lam Department of Government and Institute for Quantitative Social Science, Harvard University http://www.people.fas.harvard.edu/~plam

15 years, 1 month

[gov2001-l] seeing the log-likelihood iterations

by hersh＠fas.harvard.edu

Hi: We are replicating an article that uses a random effects model. The authors show a likelihood ratio test that Stata reports in its output. A Google search taught me that the null model in this test "corresponds to the last iteration from Fitting constant-only model." Can I ask Zelig to show me such a thing? Thanks! Eitan

15 years, 1 month

[gov2001-l] obvious

by sparshahoneysaha＠gmail.com

sorry, so obviously you want smaller residuals...but is this how we should think about fit? so if the residuals are smaller with one model specification (say 10 explanatory variables) does that make it automatically better than the one with 12 explanatory variables? what are other diagnostics that we can run (on negative binomial models or in general that work for all types) to check to see what specification is actually "better"?

15 years, 1 month

[gov2001-l] Running "tobit" in Zelig and/or using Instrumental Variables

by lauren.peritz＠gmail.com

Hello All, After running into some difficulties with missing data, we are looking into replicating one of our backup choices for papers. This paper employs a two-stage tobit model. We gather the model is similar to a two-stage least squares model in that one uses instrumental variables to deal with the situation when at least some independent variable (xi) is correlated with at least some stochastic component. We also understand the tobit model allows one to deal with censored data (e.g. non-negative y). We are wondering if anyone has experience running the tobit model in Zelig, or programming in R a two-stage model using this concept of instrumental variables. If so, perhaps we can ask you some specific questions that have arisen in our replication. The authors of our paper used Stata, which unfortunately for us, has a pre-programmed IV tobit function and provides little clue to what the function actually does (something of a 'black box'). Thank you in advance! Lauren (+ John Polley)

15 years, 1 month

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

Gov2001 March 2009