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.

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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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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 ...
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24 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 ...
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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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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 ...
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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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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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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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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) ...
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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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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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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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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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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 ...
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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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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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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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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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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) ...
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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, ...
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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 } ...
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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 ...
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1answer
52 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 ...
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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 ...
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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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Why does the scaled probit function coincide with the logistic sigmoid function at lambda = pi/8?

I'm currently working through through "Pattern Recognition and Machine Learning" by Christopher Bishop, and chanced upon this question while working towards my finals: My two questions are: ...
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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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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 ...
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236 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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51 views

Correct interpretation of posterior predictions in Bayesian logistic/probit regression

I have bit of a devils advocate question about how to properly determine the posterior predictive distributions from a Bayesian logistic/probit regression. Please forgive any sloppiness in notation ...
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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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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 ...
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Centering covariates and factors in probit model to interpret coefficients

This is my first post ever here so apologies if I missed any of the guidelines, or if the question is off base. I am trying to interpret regression coefficients of a probit model for both continuous ...
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Confused about motivation for variable selection - probit model

I am running a probit regression which aims to determine which variables influence whether a graduate feels 'mismatched' (e.g. not using the qualification they've learned) in their job. We are using ...
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2answers
60 views

Probit with variance not equal to 1

In probit models the latent variable is assumed to be of the form: $y=\alpha+\beta x+\epsilon$ with $\epsilon \sim N(0,1)$. What if instead $\epsilon \sim N(0,\sigma^2)$? Is there a way to estimate $...
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223 views

Why margins and mfx yield different results in R?

When I run a simple logit regression between a binary variable $y$ over another binary variable $x$, the average marginal effects obtained by the function logitmfx (...
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Calculating pseudo-$R^2$ for out-of-sample probit model forecasts

I'm trying to replicate parts of: Estrella, A., & Mishkin, F. S. (1998). Predicting U.S. Recessions: Financial Variables as Leading Indicators. Review of Economics and Statistics, 80(1), 45–61. ...
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Comparison between Logit and Probit models [duplicate]

I'm new in econometrics and I'm working in a probabilistic model and using Stata for this, but when I was going to compare the Logit and Probit I did not know which one win in this case, because there ...
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help determining ROPE for bayesian multilevel probit model

I am having difficulty determining a justifiable region of practical equivalence (ROPE) for a parameter from a multilevel probit model Below is the posterior distribution for the fixed-effect of ...
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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 ...
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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. ...
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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^*...
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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/...
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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.
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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 ...
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1answer
53 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 ...
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51 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: ...
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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.
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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 ...
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161 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)...

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