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.
304
questions
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1answer
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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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0answers
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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) ...
1
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1answer
20 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
31 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 } ...
0
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0answers
32 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
23 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
39 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
66 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 ...
0
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0answers
18 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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0answers
31 views
Sigma interpretation in panel probit model
I am struggling interpreting sigma in a random effect panel probit regression and why is this term strongly significant. I understand all the others results from the regressions.
I used the ...
0
votes
1answer
26 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
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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 ...
0
votes
1answer
103 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 ...
0
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0answers
28 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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0answers
38 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
26 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
14 views
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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0answers
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How to formalize this problem for a ML solution ? “closeness” of local min/maxes of two variables
i am mostly self taught so apologies in advance if my formulation is different to what is commonly used among trained ML pros.
I have two variables X(t) and Y(t) what I wanna know is whether the ...
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0answers
26 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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0answers
7 views
Strangely identical Probit and IVProbit results
I'm currently running 3 probit regressions, each of which have IV variants. In each model the regressors and instruments are identical (the coding essentially looks exactly the same for each model, ...
0
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2answers
47 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
147 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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0answers
58 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.
...
0
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0answers
32 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 ...
1
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0answers
39 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 ...
0
votes
0answers
28 views
How to test for sample selection bias in heckit estimation?
I'm doing a heckit two step estimation by hand where i first estimate the labor force participation, compute the inverse mills ratio by using the linear predictors of the model and add it as an ...
2
votes
1answer
76 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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0answers
12 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
68 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
19 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 ...
4
votes
1answer
823 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
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0answers
21 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
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0answers
21 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
74 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
30 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
49 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
49 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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0answers
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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.
2
votes
1answer
96 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 ...
1
vote
1answer
128 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
192 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
29 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
97 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
474 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 ...
3
votes
1answer
98 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
468 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 ...
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0answers
126 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
54 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 ...
2
votes
0answers
45 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 ...
