I did the Hausman test in my data and this showed me that I may use fixed effects. However, when I did the fixed effects without any year, country, and sector effects, I get the results that I hope to get (a statistically and positive significant relation with the variables):
xtreg price_outliers esg_score_w roa_w eps_w bv_pershare_w lev_w size_w, fe
However, when I use i.year, i.country and i.ec_sector, I get a negative relation with the same variables:
xtreg price_outliers esg_score_w roa_w eps_w bv_pershare_w lev_w size_w i.year i.country i.ec_sector
What could be the reason for this?
A common way of including multiple fixed effects is with reghdfe, available from SSC and written by S. Correia. Here is the documentation: http://scorreia.com/help/reghdfe.html.
Code:
ssc install reghdfe
reghdfe price_outliers esg_score_w roa_w eps_w bv_pershare_w lev_w size_w, absorb(year country ec_sector)
That said, reg ... i.year i.country i.ec_sector, i.e. the pooled OLS version with dummies, should yield virtually identical results. Also see: Pooled OLS with industry specific effects (reghdfe)
Related
I have a panel dataset for multiple firms throughout 8 years and I'm trying to use a pooled OLS with industry-specific effects with the `reghdfe' command to control for a categorical variable (NAICS Industry Code). I typed
reghdfe DV IV control variables i.year, absorb(NAICS Industry Code)
Is this the correct way to use the command? Is it correct to use i.year within the variables or should I add it to the absorbed variables?
In addition I'm using a Fixed Effect Panel Regression and control for clustered standard errors. Do I have to control for clustered standard errors in the reghdfe as well or is it sufficient to just do it within the fixed effect panel regression?
You should include your variable year in the absorb() option to catch the intended use of reghdfe:
reghdfe y x, absorb(naics year)
Alternatively, you can also use reg y x i.naics i.year.
I assume NAICS codes to be numeric; otherwise, you might need to transform the variable to numeric, e.g. using egen num_naics= group(naics).
Note: The R-squared rests on different assumptions and might differ between the two commands.
Note_2: If your question is specifically about coding, everyone is better off when you provide example data. Statistical questions might be better suited for Cross Validated.
I am using PROC GLIMMIX to analyze repeated measures data about specific sexual events. The original data came from a weekly diary study of about 400 people. During each week they reported on behaviours from their most recent sexual encounter. We also have basline data on their demographics. 12 weeks of observation were collected and we had a high completion rate.
I would like to create a mixed effect model, but I am unsure exactly how this is done in SAS. I want to test the effect of event-specific factors as well as some person level demographics and would like to get odds ratios for each factor of interest. The outcome is whether or not drugs were used during the event and the explanatory factors will be things like age, gender, etc. as well as characteristics about the event (i.e., partner HIV status), whether the partner was a regular sexual partner, etc..
The code I'm working with follows this pattern:
PROC GLIMMIX DATA=work.dataset oddsratio;
CLASS VISIT_NUMBER PARTICIPANT_ID BINARY_EVENTLEVEL_OUTCOME BINARY_EVENTLEVEL_EXPLANATORY_FACTOR CATEGORICAL_PERSONLEVEL_EXPLANATORY_FACTOR;
MODEL BINARY_EVENTLEVEL_OUTCOME = BINARY_EVENTLEVEL_EXPLANATORY CATEGORICAL_PERSONLEVEL_EXPLANATORY_FACTOR /DIST=binary link=logit CL S ddfm=kr;
RANDOM ?????;
RUN;
option 1 for ?????: residual / subject=PARTICIPANT_ID
option 2 for ?????: INTERCEPT / subject=PARTICIPANT_ID
option 3 for ?????: VISIT_NUM / subject=PARTICIPANT_ID residual type=ar(1)
INTERCEPT / subject=VISIT_NUM(PARTICIPANT_ID)
option 4 for ?????: Other?
I am also unclear whether I should use ddfm=kr in my model statement or method=laplace in my proc statement -- both have been recommended elsewhere for this sort of repeated measures analysis.
I've come across several potential options for modelling this which often give similar results, but option 1 gives a statistically significant result for an event-level, while the others give non-significant results. The inclusion of the ddfm=kr makes the result of interest more significant. The method=laplace does not allow for option 1.
I may not be answering your question, but might be able to provide a couple of directions:
To start with the simplest part, your MODEL statement looks correct to me as you want to test event-level factors and person-level demographics which are thus considered as fixed effects.
Now, as far as the random effects are concerned:
the RANDOM statements you propose for options (1) and (2):
(1) RANDOM _residual_ / subject=PARTICIPANT_ID;
or
(2) RANDOM intercept / subject=PARTICIPANT_ID;
are modeling two different parts of the random effects: the R-side and the G-side, respectively.
If you are already familiar with PROC MIXED, you may want to notice that your option (1) of using RANDOM _residual_ in PROC GLIMMIX is equivalent to using the REPEATED statement in PROC MIXED that tells that you have repeated measures for PARTICIPANT_ID, which is clearly your case (Ref: "Comparing the GLIMMIX and MIXED Procedures")
As for option (3):
RANDOM VISIT_NUM / subject=PARTICIPANT_ID residual type=ar(1) INTERCEPT / subject=VISIT_NUM(PARTICIPANT_ID);
here you are modeling the time component of the repeated measures (visit_num) as a random effect, and this should be included when you believe that there would be a random variation of the response at each of the measurements times (i.e. at each event). At first glance, I don't believe this is relevant in your case, since you are taking this into account already by the fixed effects... but of course I may be wrong by not seeing your data.
Up to here is what I can contribute at this time.
As next steps for you to have a better understanding, I would suggest that you:
Read the Overview of the PROC GLIMMIX documentation, in particular the mathematical model specification and all 3 sections therein:
The Basic Model
G-Side and R-Side Random Effects and Covariance Structures
Relationship with Generalized Linear Models
If you are still unsure, ask your question at communities.sas.com which might be able to help you better.
HTH
(cross-posted at http://www.statalist.org/forums/forum/general-stata-discussion/general/1370770-margins-plot-of-treatment-effect-rather-than-y-for-values-of-a-covariate)
I'm running a multivariate regression (outcome variable is continuous, happens to be GPA). The covariate of interest is a dummy variable for treatment status; another of the covariates is a pre-score. We want to look at how the treatment effect differs at various values of pre-score. The structure of the model is not complicated:
regress GPA treatment pre_score X3 X4 X5...
What I want is a graph that shows what the treatment effect is (values of Beta1) at various values of pre-score (X2). It's straightforward to get a graph with values of the OUTCOME at various values of X2:
margins, at(pre_score= (1(0.25)5)) post
marginsplot
I have consulted an array of resources and tried alternatives using marginscontplot, coefplot with recast, the dy/dx option, and so forth. I remain unsuccessful. But this seems like something that there must be a way to do; wanting to know if a treatment effect varies for values of a control (say, income) must be common.
Can anyone direct me to the right command, or options for Margins, to output values of Beta1 (coefficient on treatment dummy), rather than of Y (GPA), at values of the pre_score?
Question was resolved at Statalist. Turns out that Margins alone can't do what I was trying to; the model needs to be run with an interaction term. Then it's simple.
I want to run block bootstrap, where the blocks are countries, and include country indicator variables. I thought the following would work.
regress mvalue kstock i.country, vce(bootstrap, cluster(country))
But I get the following error.
. regress mvalue kstock i.country, vce(bootstrap, cluster(country))
(running regress on estimation sample)
Bootstrap replications (50)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.xxxxx 50
insufficient observations to compute bootstrap standard errors
no results will be saved
r(2000);
It seems that this should work. If the block bootstrap picks the same country for every block, then it seems it should just drop the intercept.
Is my error coding or conceptual? Here is some code using the grunfeld data.
webuse grunfeld, clear
xtset, clear
generate country = int((company - 1) / 2) + 1
regress mvalue kstock i.country, vce(bootstrap, cluster(country))
The problem here is not with your coding, but is conceptual. The problem is that you cannot identify each coefficient in each regression in each bootstrap sample. Not all "countries" are included in the dataset for each bootstrap repetition. You can diagnose what is going on with the vce( , noisily) sub-option:
. regress mvalue kstock i.bscountry, vce(bootstrap, cluster(country) noisily)
Errors are generated because some coefficients are missing when the regression runs with particular bootstrap samples. In each regression you can see that some countries dummies are being omitted due to collinearity. This should be expected and makes a lot of sense -- the country dummies could =0 for all observations in the bootstrap sample if the country was not drawn!
If you are really trying to estimate the coefficients on the country dummies, you are going to have to find another approach than bootstrapping with K clusters if K is the number of countries. If you don't care about the coefficient dummies you could use another command that simply absorbs the fixed effects and only reports the coefficients on the other independent variables (e.g., areg or xtreg). One way think about what is going on is that it is analogous to this:
.bootstrap, cluster(country) idcluster(bscountry) noisily: regress mvalue kstock i.bscountry
With the idcluster() option, each country that is drawn in a bootstrap sample is given its own ID number. If a country is drawn twice then there are two dummies. (The coefficients for the two dummies naturally turn out to be identical or near-identical.) However, the coefficients in this output are are completely meaningless because bscountry "2" will be different countries in different bootstrap iterations. Since you would ignore any output on the dummies, you might as well use a model like areg or xtreg since they run more quickly.
Although there are many applications where bootstrapping with clusters would work fine, the problem here is the inclusion of cluster dummies in the regression. This all begs the question of whether this exercise makes any sense at all. If you are trying to estimate the coefficients for the country dummies, then certainly not. Otherwise, the solutions above might be OK, but it is hard to say without knowing your research question.
I am running Logit Regression in Stata.
How can I know the explanatory power of the regression (in OLS, I look at R^2)?
Is there a meaningful approach in expanding the regression with other independent variables (in OLS, I manually keep on adding the independent variables and look for adjusted R^2; my guess is Stata should have simplified this manual process)?
The concept of R^2 is meaningless in logit regression and you should disregard the McFadden Pseudo R2 in the Stata output altogether.
Lemeshow recommends 'to assess the significance of an independent variable we compare the value of D with and without the independent variable in the equation' with the Likelihood ratio test (G): G=D(Model without variables [B])-D(Model with variables [A]).
The Likelihood ratio test (G):
H0: coefficients for eliminated variables are all equal to 0
Ha: at least one coefficient is not equal to 0
When the LR-test p>.05 do not reject H0, which implies that, statistically speaking, there is no advantage to include the additional IV's into the model.
Example Stata syntax to do this is:
logit DV IV1 IV2
estimates store A
logit DV IV1
estimates store B
lrtest A B // i.e. tests if A is 'nested' in B
Note, however, that many more aspects have to checked and tested before we can conclude whether or not a logit model is 'acceptable'. For more detauls, I recommend to visit:
http://www.ats.ucla.edu/stat/stata/topics/logistic_regression.html
and consult:
Applied logistic regression, David W. Hosmer and Stanley Lemeshow , ISBN-13: 978-0471356325
I'm worried that you are getting the fundamentals of modelling wrong here:
The explanatory power of a regression model is theoretically determined by your interpretation of the coefficients, not by the R-squared. The R^2 represents the amount of variance that your linear model predicts, which might be an appropriate benchmark to your model, or not.
Identically, the presence or absence of an independent variable in your model requires substantive justification. If you want to have a look at how the R-squared changes when adding or subtracting parts of your model, see help nestreg for help on nested regression.
To summarize: the explanatory power of your model and its variable composition cannot be determined just by crunching the numbers. You first need an adequate theory to build your model onto.
Now, if you are running logit:
Read Long and Freese (Ch. 3) to understand how log likelihood converges (or not) in your model.
Do not expect to find something as straightforward as the R-squared for logit.
Use logit diagnostics on your model, just like you should be after running OLS.
You might also want to read the likelihood ratio Chi-squared test or run additional lrtest commands as explained by Eric.
I certainly agree with the above posters that almost any measure of R^2 for a binary model like logit or probit shouldn't be considered very important. There are ways to see how good of a job your model does at predicting. For example, check out the following commands:
lroc
estat class
Also, here's a good article for further reading:
http://www.statisticalhorizons.com/r2logistic