Questions tagged [probit]

This refers generally to statistical procedures that utilize the probit function. The primary example of this tag is probit regression where the probit transformation of the parameter p of a Bernoulli distribution is used as a link.

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R package that calculates out-of-sample pseudo $R^2$ used to compare probit models [closed]

I have multiple binomial datasets that I fit probit models to. I would like to compare how well each model fits each dataset. One way I want to do this is using McFadden's out-of-sample pseduo R^2. Is ...
scott.pilgrim.vs.r's user avatar
7 votes
2 answers
91 views

Univariate approach to a Bivariate logistic regression

Consider a situation where two independent agents (out of a set of many agents) look at the same problem and attempt to solve it with a yes/no response, obtaining $(Y_{i1},Y_{i2})$ for $i \in \{1,\...
Dylan's user avatar
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Average marginal effects using the linear probability model

I read that when calculating average marginal effects using the linear probability model, you should always use heteroscedasticity robust standard errors. Why is that?
Marlon Brando's user avatar
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How to graphically represent a Probit regression after weighting using propensity scores in R?

So I have a Probit model that uses weights w, which I got from propensity scores, but I can't find any information on how to represent the Weighted Probit regression. All the information I could find ...
Google Account's user avatar
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Applying OLS regression on model with binary dependent variable to run diagnostic tests

My dependent variable is binary and am therefore using a logit regression but since the diagnostic tests to run for logit are different can I not just regress the variables with OLS and run the ...
ECEONMTRICSZ's user avatar
2 votes
1 answer
52 views

Pseudo $R^2$ for probit model: In-sample or out-of-sample?

I have a dataset test_data that measures mortality in response to dosage of a pesticide. I used a probit model that evaluates the efficacy of a single pesticide. ...
scott.pilgrim.vs.r's user avatar
1 vote
0 answers
118 views

Calculating Inverse Mills Ratio after Probit

I need to compute the Inverse Mills Ratio after the probit command in Stata. From here, I found that predict IMR1, score, will calculate it and store it in IMR1. I ...
user917983's user avatar
1 vote
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72 views

Average Marginal Effect interpretation for probit Model

I'd like to have your feedback about the interpretation of the Average Marginal Effect (AME) for a probit model. Suppose we have the following model \begin{equation} P(y=1|x)= \Phi(\beta_0 + \beta_1 ...
Maximilian's user avatar
1 vote
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Constructing a meaningful regression model [closed]

I'm developing a probit model to predict the partial effect of independent variables on the expected probability of my dependent variable being one (that is the event that a strong growth in GDP ...
Maximilian's user avatar
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A policy evaluation with a dependent variable which is continuous and in (0,1) interval

I am conducting research in which I want to investigate the effect of tax incentives on research and development intensity in a group of firms. I have access to the data of a survey that: It's only ...
Saeed DM's user avatar
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Fitting a complex GLM in R with probit link

I am trying to fit a probit model for binomial data as follows. \begin{equation} \tag{1} \%\textrm{alive} = \phi \ [ \textrm{a} - \textrm{b}t\ ] \end{equation} Where $\%alive$ is the dependent ...
Crops's user avatar
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Ordered Probit with 20+ categories

Is there any upper limit on categories I can use for the dependent variable in an ordered probit model? In my current model I have at least 20 categories, but I maybe require more (up to 50). Is this ...
JorgenM's user avatar
1 vote
0 answers
111 views

Interpretation of coefficients in fractional response (probit) model

I am learning about the estimation of fractional response models (those with a lower and upper bound, say 0 to 1), using Stata. I came across this example on the Stata page, which I'm copy-pasting ...
Spotty Giraffe's user avatar
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Probit "Postestimation", in Stata: How do i specify/utilize results of my probit "Classification tables ("estat classification" command)?

Is there a way or a specific command I can use in Stata for more insights and information after i Run the "estat classification" command and classification Tables for my probit model? I know ...
Ruby's user avatar
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Is multivariate probit the right approach?

I am interested in what determinants may drive a car manufacturing firm to sell its products only in specific countries. The same car can be available for sale in countries A and B but not in country ...
Dilian Morne's user avatar
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Probit with "fixed effects"

I know that it is not possible to run a fixed effects probit model, when fixed effects are at the individual level. In other words, it is not possible to estimate $\alpha_i$ for each individual $i$ in ...
eades's user avatar
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Bivariate restricted probit in R

I want to estimate a bivariate restricted probit model in R. In particular, I want to restrict the correlation between the models to 0. Using the GJRM package & function, I was already able to ...
JanR's user avatar
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2-stage least squares (2SLS) with 0th-stage Probit correction: Additional fixed effects in 2SLS

I am dealing with a binary endogenous variable and thus trying to use a 2SLS model with Probit correction in the preliminary ("0th") stage, a procedure described in Wooldridge (2002; p.623, ...
IJN81's user avatar
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Estimating Average Marginal Effects from LASSO

I am conducting a prediction using the LASSO probit model with a dummy on the LHS and several interacted variables on the RHS. My question is: can I meaningfully estimate the Average Marginal Effect ...
lippi's user avatar
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2 votes
1 answer
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Machine learning method(s) to compare probability of success of two groups

I would like first to mention that I am relatively new to the Machine Learning (ML) world, but I have a decent background in statistics and econometrics. I am working on a research paper focusing on ...
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Marginal effects in a Probit Model

The exact problem I am trying to solve is as follows. I have a Probit specification: $$ P_t = \Phi(\beta^T x_t) $$ where $\Phi$ is a standard normal CDF and $x$ is a matrix of independent variables ...
DrStrangeLove's user avatar
1 vote
0 answers
114 views

Multivariate probit analysis in stata [closed]

I am using stata 12 as well ase stata 14.2 Here I am trying to run the multivariate probit model for my data set. But the command is not being read by stata. At the same time I am not being able to ...
Mrinal saikia's user avatar
1 vote
1 answer
89 views

Does this formula hold for the coefficients of a logistic regression $\pmb{\hat{\beta}} \sim N(\pmb{\beta}, (\mathbf{X}^T\mathbf{X})^{-1}\sigma^2)$? [duplicate]

A person on Cross Validated states that the coefficients of the general linear model follows the following distribution $$\pmb{\hat{\beta}} \sim N(\pmb{\beta}, (\mathbf{X}^T\mathbf{X})^{-1}\sigma^2)$$ ...
Julien's user avatar
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2 votes
1 answer
430 views

Power analysis for probit model in R

New to power analysis, I am trying to perform a power analysis in R to determine n individuals needed to achieve 0.80 power for a probit regression. I've tried looking into ...
scott.pilgrim.vs.r's user avatar
1 vote
0 answers
54 views

Interpreting the marginal effect for logit regression when the independent variable log transformed variable [duplicate]

I have run probit and logit regression analysis for a model, where the dependent variable is 'Purchase' which takes the value of 1 for purchase, else zero. The independent variable is the log of ...
Jui Sen's user avatar
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0 answers
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How should I operationalize my variables?

I am trying to find out whether the regime type of states signing international cooperation agreements influence the design of the agreement, more specifically whether the regime type of the members ...
EmStaLo's user avatar
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2 votes
1 answer
337 views

Average Marginal Effects interpretation problems [duplicate]

I am running a probit regression model to test my hypothesis. As one cannot deduce the magnitude of the effect of the independent variables on my dependent one, I have calculated the average marginal ...
EmStaLo's user avatar
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1 vote
1 answer
928 views

Is 0.1542 Mcfadden's Pseudo R2 acceptable in general?

I am conducting logistic regression and got 0.1542 as pseudo R2, and it is based on Mcfadden's. I've searched materials about this, because this is my first time that modelling logit models, and found ...
hogu's user avatar
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Is it possible to derive the probit model by writing it in exponential family form?

We always use latent variable approach to derive probit model, is there any way to derive probit model from the exponential family form (by using link function)? Also, does logit-normal distribution ...
doraemon's user avatar
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Intraclass correlation in generalized linear mixed model

I am working a model using (resp) (and indicator of wheeze status) as the response and age (coded as zero for children of age 9) and smoke (an indicator of maternal smoking at first year of study) as ...
Joe's user avatar
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3 votes
0 answers
100 views

How to interpret coefficient and marginal effect in probit when $\beta$ is not identified

Say we have the latent variable $y_i^*=x_i\beta-\epsilon_i$ and $\epsilon_i \sim N(0, \sigma^2)$. $y_i^*/\sigma=x_i\beta/\sigma-u_i$ where $u_i=\epsilon_i/\sigma \sim N(0, 1)$, and so can use Probit ...
jasmine's user avatar
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1 vote
1 answer
402 views

Comparing lavaan::sem to probit regression output

I am trying to improve my understanding of lavaan::sem models when using a probit link function by comparing the output to simple probit regressions. Currently, I see that the coefficients for each ...
SamPassmore's user avatar
2 votes
1 answer
1k views

Marginal effects for interaction terms

I am trying to compute probit regression that includes interaction terms. When I compute marginal effects after the main coefficients R gives me marginal effects for interaction terms and Stata doesn'...
user avatar
1 vote
1 answer
44 views

Do I use the Chi-squared test for independence to check whether or not I should put two predictor variables into the same regression model?

I don't have a statistical background, so a lot of this stuff confuses me (sorry if this is a bad question). But, I am trying to construct a few Probit regression models in R on a dataset that ...
user17073706's user avatar
1 vote
1 answer
47 views

Linear Probability Model, General Formulation, Pedantic Question

I'm reviewing the very basics of discrete-choice models (binary choice, multinomial, tobit, etc.), and I seem to have taken for granted that latent variable models are just one (very convenient and ...
ECON10105's user avatar
1 vote
0 answers
105 views

Normality tests for latent variable in probit regression

I am performing a probit regression where the latent variable y* is conceptually important. I already have the model defined with regressors: categorical variables, quadratic terms, continuous ...
Neo Avi's user avatar
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1 answer
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Are binarized rankings problematic as dependent variables?

I have data on 150 sprint races $r$ and on the rankings and training routines of 20 athletes $i$ who have participated in each race. I am interested in finding out whether spending more time training ...
S2345v's user avatar
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8 votes
3 answers
184 views

Formal Definition of Identification

This definition of identification (the bracketed part) is confusing to me because (based on my obvious misunderstanding) it fails for probit: Probit with 2 covariates: $f=\Theta(X_1\theta_1+X_2\...
Panel Noob's user avatar
8 votes
2 answers
900 views

Why probit regression is less interpretable than logistic regression?

What I've seen very often, is that people are saying, that logistic and probit regression are giving very similar results, however, logistic regression is more interpretabe. And in this question, I ...
John's user avatar
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3 votes
1 answer
122 views

Under what conditions do different choices of link function for GLMs result in considerably different models?

The statistical folklore I have heard is that the choice of link function usually does not considerably affect the resulting fit of a GLM. For example, usually probit regression and logistic ...
cgmil's user avatar
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1 vote
0 answers
98 views

Can I use a conditional logit for fixed effects (Chamberlain, 1980) if my assumptions are that the errors are normally distributed?

Consider that we have a dichotomous variable $D_{i,t}$ that takes the value of 1 if $Y_{i,t}\geq 0$ and 0 otherwise. And $Y_{i,t}$ is a latent variable defined by: $Y_{i,t} = \eta_{i} + \sum\limits_{k\...
Alejandro Hirmas's user avatar
1 vote
0 answers
244 views

McKelvey and Zavoina's pseudo-$R^2$ score range

I am trying to build a probit regression model. Looking for some proper goodness of fit indicator for my model I read that McKelvey and Zavoina's pseudo-$R^2$ could be the best index for this purpose. ...
fredi96's user avatar
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5 votes
1 answer
626 views

heavier tails means that it is less sensitive to outlying data for logistic and probit

This is the description of logistic regression and probit regression as generalized linear model from wiki: Both the logistic and normal distributions are ...
user6703592's user avatar
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1 vote
0 answers
11 views

Does the insignificance of a regressor imply insignificance of differenced predicted probabilities with respect to this regressor in probit model?

Suppose I fit a probit model (with two regressors) to data and obtain predicted probabilities: $\widehat{Pr}(Y=1|x_1,x_2)=\Phi(\beta_0+\widehat{\beta}_1x_1+\widehat{\beta}_2x_2)$. For simplicity, ...
ExcitedSnail's user avatar
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3 votes
0 answers
81 views

How to identify small, moderate or large data sample sizes

I want to run a probit or a logit model and I am curious about the choise regarding the data sample size that I have. In a previous answer in a question 'Probit vs Logit' I read that "Probit is ...
Collin Focas's user avatar
5 votes
1 answer
587 views

Probit vs logistic regression in ML

Why is the probit model not as popular as logistic regression for binary classification among the machine learning community? It is not or hardly mentioned in serious text books on the topic.
abkg's user avatar
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0 answers
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Conditional probit to unconditional model?

I have a study in R in which I use public disclosures from companies and stock market data to estimate the probability that a new public disclosure (dividend announcement, etc...) will be published: \...
JaimePagina's user avatar
0 votes
0 answers
170 views

Recode ordinal variables to binary one [duplicate]

I have an ordinal variable describes self-assessments of IT/ICT literacy with 0-100 scale (only integers), where 0 – very bad, 100 – very good (sample size: 10000 respondents). I want to investigate ...
Herman Cherniaiev's user avatar
1 vote
0 answers
301 views

How to calculate the standard error of coefficient from probit regression analysis

folks. I try to get the standard errors of coefficients manually from probit regression analysis in R. These are 0.69012 and 0.03565 from the following R program. ...
Sangwoo.Statistics's user avatar
1 vote
0 answers
41 views

What is the prediction model to predict the probability?

In a paper, Dasgupta, 2019 used Difference-in-Difference approach to see whether anticollusion laws implemented by different countries (staggered implementation) affect firms financial flexibility. ...
Phil Nguyen's user avatar

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