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.
329
questions
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Difference of the indices i and j in binary response models
I am currently trying to get the hang of binary probit and logit models as well as multinomial models, but I struggle to see the difference in the indices i and j respectively the difference in their ...
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8 views
Distributions other than $N(0, 1)$ for the probit-style regression link function
When we do a probit regression, we use the distribution of a standard normal to convert from the linear combination of the predictors to a probability value.
Why stop at the standard normal? Why not ...
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1answer
23 views
Which test to run … probit or logistic?
IV's: categorical School, categorical Grade Level, categorical Living Arrangement, categorical Race, categorical Illness, continuous Time.
DV: withdrawn status (withdrawn or not withdrawn)
I want to ...
0
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1answer
25 views
Decomposing a probit/logit regression
In an econometric work, I want to assess the causal effect of n variables on a binary character variable y, while I highly suspect that the relation between one of these regressors, say x (which is ...
3
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1answer
89 views
Ways to shrink standard errors in models for discrete dependent variables
Consider a simple Probit model
$$
Y_i=1\{X_i\beta+\epsilon_i\geq 0\}
$$
where $\epsilon_i$ is standard normal independent of $X_i$.
(1) Cardinality of the support of $X_i$
Is it true that (and, if yes,...
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10 views
Testing equality of coefficients from two probit regressions
I need to test the equality of coefficients from two different regressions (PROBIT). So, I performed two times the same regression (containing multiple independent variables).
The only difference is ...
4
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0answers
83 views
Why is my quasibinomial GLM estimator biased - Monte Carlo simulation
I'm playing with some Monte Carlo simulations to get an idea of the properties of some linear and non-linear models.
The linear OLS model in my case is specified as:
$Y_t = \beta_0 + \beta_1x+ \...
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0answers
3 views
Multiple choice answers and multivariate probit models
I have a survey dataset in which respondents were asked to choose maximum 3 options out of a list of 10 possible answers. I would like to find out how the likelihood of different options being chosen ...
0
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1answer
29 views
Selection equation in Heckprobit
I am trying to implement heckprobit for my dataset. The one issue I am running into is that the stata does not give estimates of the effect of the endogenous binary regressor on the dependent variable ...
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0answers
9 views
Constructing aggregated choice model
I am trying to model a situation where passengers make choices in their transportation modes when I know the selection ratio of each modes.
My dataset looks like this
...
3
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0answers
86 views
2SLS with a boolean regressor
So, I have the following linear model:
$$y = \alpha + \beta x + u$$
and $x \in \{0,1\}$, i.e. the variable $x$ is boolean. Moreover $x$ may be endogenous, and I have a set of instrumental variables $\...
0
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1answer
36 views
Endogenous Sample Selection and Heckit Correction in relation to Research Question
I am researching the funding amounts of start-ups. I am calculating two models:
...
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1answer
24 views
After running a probit, how can I generate the margins for the whole distribution?
I'm using Stata.
I ran a probit of the form
$$
\text{outcome}_i = \beta \ f(\text{income}_i) + \gamma\text{ Controls}_i
$$
Where $f(\text{income}_i)$ is a fractional polynomial.
I'm interested on the ...
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0answers
10 views
In probit model, why demeaning the data of a regressor lead to no change in numerical values for estimated coefficients except for the constant?
Supppose I have data $\{Y_i,X_{1i},X_{2i}\}_{i=1}^{n}$ generated by model $Y_i=\mathbf{1}(a+b_1X_{1i}+b_2X_{2i}>e_i)$, where $\mathbf{1}(\cdot)$ is the indicator function. I try to estimate this ...
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17 views
Probit Models - Statistical Significance and the Number of Observations
I'm currently completing a series of probit regressions where the dependent variable takes the value 0 or 1 depending on whether a civil war was experienced by that country in that year (panel data)
I ...
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0answers
21 views
A question about normalization in probit model (binary response model with normal error)
Suppose I have data on $\{Y_i,X_{1i},X_{2i}\}_{i=1}^{N}$ and the data generating process is $Y_i=\mathbf{1}(\beta_1X_{1i}+\beta_2X_{2i}>e_i)$, where $e_i\sim N(0,\sigma^2)$. Usually, we do a ...
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34 views
Is probit transformation the same as probability integral transform?
The image shows the original marginal data $u$ and $v$ on the left, which has a bounded support, and their probit transformations $r$ and $s$ on the right, which has an unbounded support.
The ...
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Item response theory with any cdf link
The IRT applications mostly use as link functions the logit and the probit, which are the cumulative distribution function (cdf) of the logistic and normal distributions, the resulting models gives ...
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13 views
Formula of a zero-inflated negativ binomial with link to probit
I'm looking for the right notation of the probit linkfunktion in a zero-inflated negative binomial. g is the negative binomial and π the link function.
Thank you very much!
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1answer
23 views
IV changes the sign of exogenous variable
After implementing an IV probit model, the signs of many exogenous covariates' coefficients have been flipped, compared to those in the baseline probit model. These signs are now at odds with the past ...
1
vote
1answer
31 views
Probit model with Gaussian noise
Assume we have the following model setup
$$\Phi^{-1}(D)=\alpha+\beta X+\epsilon$$
where $\epsilon\sim N(0,\sigma^{2})$ and $D_{i}=\{0,1\}$. This implies that
$$\text{Pr}(D_{i}=1\,|\,X,\epsilon)=\Phi(\...
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0answers
8 views
Dummy Y variable and interaction effect
I am working on a project where I am studying the effect of a policy. There are 4 groups of people, male-control, female-control, male-treatment and female-treatment. I want to see if the difference ...
1
vote
1answer
111 views
Interpret regression output using a likert scale IV (using STATA)
I want to run a regression with an binary DV and a likert scale ( 1=strongly disagree, 2=disagree, 3=agree, 4=strongly agree) IV. I think the right model to use is a probit regression, or what would ...
1
vote
1answer
40 views
Using margins after probit estimation to equal probabilities between almost identical individuals
I'm considering a Probit model for the probability that a student will finish the course based on their hours of study, age, sex, origin, how they passed the previous course and labor market situation ...
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1answer
52 views
Distinguish between probit/ logit
I often read that:
If we believe that the functional form of the dependent variable is a cumulative normal
density, we may use probit and if we believe that the dependent variable follows
a logistic ...
4
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0answers
31 views
Predictive model (binary) doesn't seem to fit my own data
I have tried to create a predictive model based on the probit model (common in my field). The model is given as:
$$\operatorname{Prob} = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{t}\exp\left(-\frac{x^2}{2}...
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0answers
26 views
All else equal, should an MLE estimation have a lower standard error than OLS?
If I have a model with Y$\in${0,1}, and am estimating y= $\beta$x+$\eta$
My understanding is if I use a probit model say, I am imposing structure on the DGP by assuming Y|x=$\eta$ is distributed ...
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0answers
11 views
Two stage model where both dependent variables are dichotomous
New here so apologies if I do not explain myself as well as I should.
I have survey data of 2 decisions that participants make: the decision to vaccinate themselves (yes/no) and their children (yes/...
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0answers
55 views
Running a Monte carlo simulation of a probit model on Stata
I am trying to run a MC simulation for a probit model on Stata using existing variables. In all the examples I saw, the authors generate the regressors (generally only one) as well as y* and y=(y*>0) ...
0
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0answers
37 views
probit prediction and calculation of the standard error of its difference in R
I'm a beginner in using R.
I want to know how to calculate the standard errors of the difference between predicted outputs.
Please read through the below. I hope you can get what I mean.
Below is ...
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46 views
Probit function, Difference in difference approach, Standard errors in R
I'm a novice R user.
I'm dealing with some CPS data to evaluate how labor force participation of single female with children changed in response to some policy implementation.
The below is my ...
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0answers
11 views
GEEGLM binomial modelling with Probit or logit
I am fitting a GEE model in R (multivariate binomial) There are no repeated measures and assume there is a within cluster correlation. If I use a probit link, do I have to exponentiate the output ...
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0answers
20 views
Is the random slope for a binary, categorical variable in a mixed model also reported in reference to one of the categories?
I'm wondering if I should be interpreting an estimated random slope for a binary categorical variable in the same way that I should be interpreting it if it were a fixed effect. That is, is it ...
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20 views
Calculate/understand the total sample mean of a binary dependent variable from the fixed effect estimates of a model?
was hoping you could help me to understand the model that I have just fit; in particular, I'm interested in the calculus of how the fixed estimates fit back together to make estimates for total sample ...
3
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1answer
190 views
Comparison of logit and probit estimations
There are a lot of questions concerning logit and probit relations (led by 20523), but I'm still confused with a seemingly simple issue.
On the one hand, often we see that for 'rule-of-thumb' ...
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1answer
9 views
Network linkage formation prediction
Is there a statistical model that can study determinants of network linkage formation? For a set of companies (where we observe their industry, annual revenue, etc), we see which pairs are connected ...
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0answers
9 views
Regression analysis of the inverse probability function of a Probability of Default
I have come across some econometric regression models which instead of using the Probability of Default (for Credit Risk purposes) of certain loans, -or instead using logistic regression analysis of ...
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1answer
38 views
truncated model estimation, on an interval of unobserved variable Y*
$Pr[L<Y^*<U]=Pr[Y^*<U]-Pr[Y^*<L]$
$=F^*(U)-F^*(L)$
$lnL_n(\theta)=\sum_{i=1}^nd_iln[F^*(U|x_i,\theta)-F^*(L|x_i,\theta)]$
^is the above likelihood function appropriate for ...
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0answers
63 views
Converting probit coefficients into effect sizes for meta-analysis
I am conducting a meta-analysis but the raw study coefficients are probit coefficients. I am planning to convert these to log odds to meta-analyse through the formula: 1.61 * probit_coefficient.
Q1) ...
1
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1answer
62 views
Can one estimate a probit regression using OLS? Or it has to be done with maximum likelihood?
Can one estimate a probit regression using OLS? Or it has to be done with maximum likelihood?
One could take the inverse cumulative probability distribution function and calculate the probability, ...
4
votes
1answer
40 views
Simulate probit model using values of the latent variable
I am trying to simulate a probit model using a latent variable Z of the following form:
\begin{aligned}
y_{i} & = \begin{cases}
1 & \; \text{if } z_{i} > 0\\
0 & \; \text{if } ...
1
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0answers
38 views
Not recovering true coefficient with recursive bivariate probit model on simulated data
I have built a simulated dataset to try to build my intuition about the recursive bivariate probit model. The challenge I'm running into is that I'm unable to recover the true coefficient in my ...
1
vote
1answer
125 views
Can the output of a probit regression with a Z-score predictor be interpreted as a standardised effect size?
I want to obtain a standardised effect size for a regression with a binary outcome. If I standardise the (continuous) predictor and run a probit regression, the resulting coefficient can be ...
2
votes
1answer
264 views
How to compute Average partial effects?
If we have a model like this:
$$\hat {Prob}(Y=1|X) = F(\hat \beta_0 + \hat \beta_1 age + \hat \beta_2 education + \hat \beta_3 salary)$$
(suppose education is a scalar variable here) where $F$ is a ...
3
votes
1answer
98 views
Logit - probit regression
I was running regression of - determinants of acceptance into a social science college. I found this unrelated paper (Screenshot of relevant page attached herewith). Here, they have computed logit and ...
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1answer
27 views
What is the econometric unordered (alternative-specific) multinomial probit model?
Possibly a very trivial question, however I am somehow unable to find any mathematical definition of this model. From other posts on this website I perfectly understand the difference between an ...
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15 views
signification of average partial effect in probit regression
Consider the probit regression:
where "yi" and "di" are dummy variables and "xi" is a continuous scalar regressor.
Is the following statement true?
Even if the coefficient estimates "B1" and "B3" are ...
0
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1answer
389 views
CDF for a probit model
The cdf for a probit model is:
$$
\Phi(\varepsilon)=\int_{-\infty}^\varepsilon \frac{1}{\sqrt{2\pi}} \exp\left(-\frac{t^{2}}{2}\right) \, dt
$$
My very simply question—that I should know the answer ...
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0answers
63 views
Understand the Holmes and Held (2006) Bayesian probit MCMC algorithm
Holmes and Held (2006) suggest a simple approach to reduce autocorrelation in the MCMC algorithm proposed by Albert and Chib (1993). HH (2006) propose to update $\beta$ and $z$ jointly, making use of ...
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41 views
Adapting weights for a glm() binomial regression [closed]
I've been facing a common problem. I can't use my dataset’s weights to estimate a binomial family model. I've been using the glm() function to estimate a probit model, but when my weights variable is ...
