Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with regression
Search options not deleted
user 184220
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
4
votes
1
answer
1k
views
What is the difference between a linear regression with a dummy variable and two separate re...
I am interested in the connection between a multiple linear regression including a dummy variable (0/1) and two separated regressions split up by this dummy variable, i.e. two distinct regressions for …
- 316
3
votes
1
answer
431
views
Why use a dummy instead of dropping observations in regression?
Here is an arbitrary example, say I am interested in the effect of emp on gsp at high values of emp:
data("Produc", package = "plm")
# Linear Regression
lm <- lm(log(gsp) ~ log(pcap)*emp + pc + unemp … , data = Produc)
# Linear Regression with threshold step dummy
Produc2 <- Produc %>% mutate(emp_dummy = ifelse(emp > 1800, 1, 0))
lm_dummy <- lm(log(gsp) ~ log(pcap)*emp*emp_dummy + pc + unemp, data = …
- 316
0
votes
1
answer
322
views
When/why are average marginal effects (AME) equal to marginal effect at means (MEM)?
I wonder why my two versions of marginal effects (AME and MEM) yield identical results:
webuse nlswork, clear
xtset idcode year
xtreg ln_wage age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure c.tenu …
- 316
1
vote
1
answer
993
views
How to model interaction term with squared regressors?
I run a regression with interaction of a squared continuous regressor with a categorical regressor. In Stata the double cross operator ## produces all combinations of my regressors. … Here is an arbitrary MWE:
* load data
use http://www.stata-press.com/data/r13/nlswork
* set panel structure
xtset idcode year
* fixed effects regression with interaction and square term
quietly xtreg …
- 316
0
votes
1
answer
2k
views
Fixed-effects using demeaned data: Why different standard errors when using xt/reg?
which asks for getting the same SE in time demeaned regression as in fixed effects. How can I do the same thing in Stata? … =15
drop if union ==.
drop if tenure ==.
mdesc
* Regression with Time Demeaned Data
foreach var in ln_wage union tenure age msp{
bys idcode: egen m_`var'= mean(`var')
gen tm_`var' = `var …
- 316
2
votes
0
answers
916
views
What is the difference between conditional and unconditional fixed effects?
What is the difference between unconditional and conditional (fixed effects negative binomial) regression models? … A similar question was asked for quantile regression here: What is the difference between conditional and unconditional quantile regression? …
- 316
7
votes
3
answers
9k
views
Why is the intercept in multiple regression changing when including/excluding regressors?
I have a seemingly naive question regarding the interpretation of the intercept in multiple regression. … https://www.theanalysisfactor.com/interpreting-the-intercept-in-a-regression-model/
https://statisticsbyjim.com/regression/interpret-constant-y-intercept-regression/
But the intercept is changing when …
- 316