Questions tagged [probit]
This refers generally to statistical procedures that utilize the probit function. The primary example of which is probit regression where the probit transformation of the parameter p of a binary response distribution is used as a link.
2
votes
1answer
25 views
Sample selection bias and logistic regression
I am struggling with possible sample selection bias at the moment, and I was wondering whether someone has a methodological tip or possibly knows of fancy statistical/econometric tools I could use to ...
0
votes
0answers
9 views
Binary Dependent Variable Regression
I'm struggling with part (b) of the above question. If I use the probit model, then $P(Y_i|X_i=x,W_i=w,A_i=a)=\Phi(\beta_0+\beta_1x+\beta_2w+\beta_3a)$. Not sure how to proceed with the variance ...
0
votes
1answer
29 views
Interpretation of interaction term in a probit estimation
I know this question may be duplicate but I don't find any answer that I could understand. I am running panel probit estimations. Estimations include interaction terms that I am able to interpret.
...
0
votes
0answers
12 views
Categorical variable postestimation at cluster level
I have a question about categorical variable regression and post-estimation procedures.
My aim is to estimate the “probability of success” (say, odds-ratio) at an aggregate level using a lower-level ...
3
votes
1answer
199 views
How to estimate a bivariate probit (biprobit) model in R with a different set of explanatory variables? [closed]
I'm trying to estimate a bivariate probit model (also called biprobit model) in R where the set of explanatory variables is different for both binary outcomes. Thus, my setting is:
\begin{align}
Y_1^*...
0
votes
0answers
8 views
Probit model- pseudo R2
I am working with a probit model where my depended variable is whether has private loans(yes=1 no=0), while my independent variables are age, educational level, marital status, number of household ...
0
votes
0answers
15 views
Missing Data for Binary Dependent Variable
I have a Binary dependent variable (0/1) with panel data of three years(1 2 3). I want to measure the determinants of a woman choosing abortion using ordinary probit or logit. The problem is that no ...
2
votes
0answers
32 views
How does d-prime calculation relate to binomial mixed models with probit link?
for a study I tested participants in a same-different task (1AFC) about melodies. There were 3 versions of each melody (within-subjects factor "version").
So d-prime seems the natural response/...
3
votes
0answers
26 views
How to estimate the MLE for a probit panel data model in R? [closed]
Can you recommend a good package in R to calculate the MLE for a simple panel data probit model?
I would like to use GHK simulator to do it. But What I found is not for panel data in R.
0
votes
1answer
33 views
What model to use [closed]
I have data on whether an audition was successful or not and some data that could help explain the success/failure up to some extent (I have about 10 categorical variables and 2 continuous).
So the ...
0
votes
1answer
44 views
How can I regress if all the variables are categorical?
I am working with a dataset of 335 categorical variables. The dependent variable is also categorical variable, as following:
How satisfied are you with your life: 1.unhappy ... to 10. very happy.
I ...
0
votes
0answers
16 views
0
votes
0answers
26 views
Probit Hypothesis Test: $H_0: \beta_1 \geq 1$
Problem
Given the output of a probit model and no knowledge of the sample data:
...
1
vote
0answers
21 views
Can anyone explain the case of the probit model where we can identify the variance of the error term?
I was told that the variance of the error can be identified in a probit model in a specific case, and cannot find anything about it online.
2
votes
1answer
56 views
Comparing coefficients of two different probit models---is this “bad statistics”?
Apologies for any stupid mistakes, or if the answer to this question is trivial: I have no formal statistical training. Long story short: can we meaningfully compare coefficients of two different ...
0
votes
0answers
19 views
Difference in Coefficient Interpretations of Linear Probability, Logit MFX, and Probit MFX Models
I have been trying to make sense of what the difference in a LPM, Logit Marginal Effects, and Probit Marginal Effects models would be.
For instance, say I ran $employment = \beta_0+\beta_1edu+\dots+...
1
vote
1answer
67 views
Estimating elasticity using different regression models
My question is how to estimate elasticity based on a Probit model. I know the following formulas are used to estimate elasticity based on OLS (1) and logit (2) models (Ewing & Cervero, 2001):
(1)...
0
votes
1answer
68 views
Interpretation of variable in a probit model
I am struggling interpreting the coefficient of a variable which is expressed as a proportion in a probit model. As it currently stands , I am interpreting the average partial effects of this variable ...
1
vote
1answer
23 views
Interpreting regression results for different units of measure
Could you kindly help me with interpreting the results from the Probit model for different units of measure of the covariates?
Consider the Probit Model
$$
Y=1\{X\beta+\epsilon \geq 0\}
$$
with $\...
0
votes
1answer
51 views
How to deal with missingness of dependent variable in unbalanced probit model
I am trying to estimate a probit model on the probability of suicide over the next year in a population. Unfortunately for this research, suicide rates are very very low so the probability of suicide ...
2
votes
1answer
143 views
In R, How can I calculate the elasticity of Y with respect to X, when Y is binary?
I have a dataset that I am doing in R and I need to calculate elasticities in it. To simplify my model, I have Y = XB + u, and I need to find the elasticity of Y with respect to X.
My investigation ...
1
vote
0answers
40 views
Bivariate probit with a fixed rho
In a series of papers by Altonji, Elder and Taber -- for example 1 -- they check the robustness of bivariate binary choice models by seeing what values of $\rho$ (the correlation coefficient between ...
3
votes
1answer
61 views
Compare link function: generalized linear mixed binomial
How does one compare the Goodness of Fit for different models of some given binomial data using Generalized Linear Mixed Models.
Specifically we want to know whether a model with a logit link gives a ...
2
votes
1answer
277 views
Understanding Maximum Likelihood Estimation (MLE) and its confidence intervals
I'm trying to figure out if I am actually understanding MLE correctly, or at least applying it correctly to my data. My data consists of several patients for which I have some data, which is used in ...
0
votes
0answers
76 views
Probit model specification
im writing a paper that involves using a probit regression, would this be a correct summary of what a probt model is and how it works?:
"the probit specification is a binary-dependent model that ...
0
votes
1answer
33 views
Using probit regression coefficients to derive probabilities? [closed]
I'm reading a paper here that uses a Probit Regression. I'm not entirely familiar with how this works. But, I'm wondering if there's a way to use the coefficients from Table 3 to derive the ...
0
votes
0answers
18 views
Soccereconomics project predicting soccer results
I am currently research soccereconomics for a small class project.
I have download data on the results of each time, which also gives me a price that the team can be traded on.
My question is, I ...
0
votes
0answers
17 views
how to report marginal effects of variables omitted due to perfect separation?
i have a few variables that come up as omitted in a probit model but are necessary for a later heckman model, when i write up my results what is the custom for reporting the missing coefficients in ...
2
votes
0answers
36 views
Is the decision surface for a Probit model linear?
For example, if we threshold our classification on $0.5$, is the decision surface necessarily linear? It is linear for Multinomial Logit models, but it's not obvious to me that it is for Probit ...
0
votes
0answers
20 views
Can natural log transformed variables used in probit models and if so how are their coefficients to be interpreted?
i have several log-transformed continuous variables in my model and want to estimate their impact on likelihood of sale. can i include (natural) log transformed variables in a probit model? if so how ...
0
votes
0answers
117 views
Bivariate Probit/Logit : precise interpretation of coefficients
I am working with a bivariate Probit/logit model and I am a little bit confused over the interpretation of the coefficients. I my model, my two dependant variables are positively correlated. Tel me ...
1
vote
1answer
37 views
OLS and Probit possible on large sample enough?
I think I understood that normality of residuals may not be a problem if the sample is large enough (cf, here).
My question is: Would my sample be large enough to be analysed using a probit and an ...
0
votes
1answer
109 views
Pseudo R2 and prob>chi2
I am running a profit regression and I am finding difficulties in interpreting the prob>chi2 =0.0000 statistic. Also, should I be concerned that Pseudo R2=0.0209? How do I interpret it.
Thank you.
1
vote
0answers
37 views
Bivariate probit : is there a heteroscedastic version of the model?
I know there exists a version of the simple probit model which is robust to heteroscedasticity (the heteroscedastic probit model). Is there an equivalent for the bivariate probit model? Is there a way ...
0
votes
0answers
99 views
Deriving the gradient vector of a Probit model
Consider the probit regression model where the pdf of $y_{i}$ is
$$f(y_{i};\mathbf{\beta}) = \mu_{i}^{y_{i}}\left ( 1 - \mu_{i} \right )^{1 - y_{i}},$$ where $y_{i} = \left \{ 0,1 \right \}$ and $\...
0
votes
0answers
85 views
Goodness of fit after pglm regression
I'm studying individual characteristics driving a fraudulent behavior.
I'm using a panel probit estimator with random effects.
...
2
votes
1answer
68 views
R/Stata Can I include a categorical variable in a Logistic regression when one of its values perfectly predicts one of the outcomes?
Suppose I have the following data
df = data.table('y'= c(1,1,1,1,1,1,1,0,0,0,0), 'x' = c(1,1,1,1,1,1,1,1,1,0,0))
where x = 0 perfectly predicts y = 0. I ...
1
vote
0answers
16 views
Is a binary probit model which predicts the outcome of every observation to be true still useful for analysing effects of explanatory variables?
So I have a binary probit model for labour participation restricted to a specific age group and region. The remaining explanatory variables include sex, education, and having dependent children (all ...
0
votes
0answers
12 views
A question about collineartiy with the dependent variable in a probit model
I am using a probit model to measure voter consistency. A voter is considered consistent on an issue if she holds a position on an issue and then votes for a party who holds the same issue (a voter ...
1
vote
1answer
129 views
Difference between probit of log(x) and probit using inverse lognormal cdf of x
Topic: probit and logprobit regression.
Context: I have to implement a model of size at maturity in a wild population and I must choose a binomial linear model to estimate the parameters based on ...
3
votes
2answers
68 views
What can you infer about the effects of explanatory variables in a binary probit model?
Obviously in linear regression, the coefficient tells you whether the effect of a change in an explanatory variable on the response variable is positive or negative and how much a change of one unit ...
1
vote
1answer
104 views
Which p-value should I use ? (Probit and interaction terms)
I ran a Probit model to determine which variables/interaction terms are significant. I got these results:
Probit: "mino" is not significant.
Whith the margins command (Averadge marginal effect) I ...
0
votes
1answer
163 views
How to calculate probit marginal effects over groups with Stata [closed]
How to properly calculate marginal effects of a probit model to capture marginal effect of one variable over a group. The setup is the following (simplified version):
Data:
...
0
votes
0answers
117 views
Logistic regression model / decision trees to an ordered factor
A have a data frame with shops with a number of independent variables (all categorical), I'm trying to find a decent model to predict a dependent variable that is ordered and categorical (ie "small", "...
1
vote
1answer
96 views
Is there heteroskedasticity in binomial GLMs?
We know a linear probability model (LPM) will produce heteroskedastic errors by definition because of how the variance of a bernoulli r.v. is defined. My question is whether the same is true for logit/...
1
vote
0answers
52 views
test the significance of the marginal effects of a spatial probit model
I'm using the CRAN package spatialprobit to estimate a Spatial Probit. The function sarprobit calculates the marginal effects ...
0
votes
0answers
19 views
Are there problems with estimating regressions of longitudinal data by year?
I am studying the behavior of 900 firms for a period of 10 years. The data is a balanced panel. Multivariate analysis consists of Tobit estimations for each year and probit estimations for each year. ...
1
vote
0answers
127 views
How can predictions in a Probit model be made dependent on dynamic dummy variables?
I'm not sure if my question title is very clear, so let me clarify.
I'm running the following probit regression on data that is sorted by date (ascending):
...
0
votes
0answers
332 views
Heckmann two-step in probit in R
I haven't found an answer to this online:
Is there any package / easy to implement way to estimate a probit with heckman correction in R? The corresponding function in STATA would be heckprob
2
votes
0answers
36 views
Recursive nonlinear models [closed]
I have the following recursive linear model
$X = \varepsilon_1$
$Z = \beta_2 X + \varepsilon_2$
$Y = \beta_3 Z + \varepsilon_3$
By solving the model its easy to see that $E[Y|X, Z]$ is linear too. ...
