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.
318
questions
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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 ...
0
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0answers
9 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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12 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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6 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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30 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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0answers
8 views
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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0answers
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
21 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
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1answer
27 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
$$\text{Pr}(D_{i}=1\,|\,X,\epsilon)=\Phi(\...
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8 views
Dummy Y variable and interaction effect
I am working on a project where I am studying the effect of a policy. There are 4 groups of people, male-control, female-control, male-treatment and female-treatment. I want to see if the difference ...
1
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1answer
61 views
Interpret regression output using a likert scale IV (using STATA)
I want to run a regression with an binary DV and a likert scale ( 1=strongly disagree, 2=disagree, 3=agree, 4=strongly agree) IV. I think the right model to use is a probit regression, or what would ...
1
vote
1answer
23 views
Using margins after probit estimation to equal probabilities between almost identical individuals
I'm considering a Probit model for the probability that a student will finish the course based on their hours of study, age, sex, origin, how they passed the previous course and labor market situation ...
2
votes
1answer
51 views
Distinguish between probit/ logit
I often read that:
If we believe that the functional form of the dependent variable is a cumulative normal
density, we may use probit and if we believe that the dependent variable follows
a logistic ...
4
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0answers
31 views
Predictive model (binary) doesn't seem to fit my own data
I have tried to create a predictive model based on the probit model (common in my field). The model is given as:
$$\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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44 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) ...
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0answers
28 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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0answers
36 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
10 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
17 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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0answers
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 ...
2
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1answer
133 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' ...
0
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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
8 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 ...
2
votes
1answer
36 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
42 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
26 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
35 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 } ...
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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
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1answer
68 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
147 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
90 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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31 views
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:
...
0
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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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0answers
13 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
votes
1answer
288 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
53 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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0answers
38 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 ...
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0answers
29 views
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 ...
0
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2answers
67 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 $...
1
vote
1answer
280 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 (...
3
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0answers
106 views
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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0answers
41 views
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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0answers
58 views
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 ...
2
votes
1answer
103 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
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1answer
112 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.
...
5
votes
1answer
1k 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^*...
2
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0answers
114 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
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0answers
34 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.
