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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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comparing non-nested models with different specifications based on AIc/BIC criteria

I am trying to determine if I can use the AIC/BIC criteria for model selection in the case of a multivariate probit model. I have two models with different specifications: e.g. Model-1: mvprobit ( Y1 =...
Jay Shah's user avatar
1 vote
0 answers
33 views

How to show that MLE of probit regression does not exist due to data separability

Claim The claim is the is the following: Assume we have the simple probit model $E(y_i|x_i ) = Φ(α+\beta x_i)$. Now suppose that $y_i = 1$ for all $x_i ≤ 10$ and $y_i = 0$ for all $x_i > 10$. Then $...
Marlon Brando's user avatar
4 votes
1 answer
44 views

Fixed effects - issue

Suppose I have a longitudinal dataset in which each country $i$ is observed at different points in time $t$. Suppose that my dependent variable is a dummy variable, and I want to estimate the ...
Maximilian's user avatar
7 votes
2 answers
97 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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25 views

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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31 views

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
0 votes
0 answers
22 views

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
3 votes
1 answer
73 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
183 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
0 answers
94 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
0 answers
26 views

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
1 vote
1 answer
33 views

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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0 answers
33 views

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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0 answers
44 views

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
142 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
0 votes
1 answer
1k views

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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1 vote
0 answers
35 views

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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0 answers
97 views

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
  • 23
2 votes
1 answer
39 views

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 ...
Abdahrt's user avatar
  • 21
1 vote
0 answers
769 views

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
139 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
104 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
541 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
55 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
  • 111
1 vote
0 answers
18 views

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
  • 41
2 votes
1 answer
393 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
  • 41
1 vote
1 answer
2k 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
  • 23
0 votes
0 answers
87 views

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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3 votes
0 answers
80 views

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
106 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
  • 367
1 vote
1 answer
550 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
2k 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
111 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
49 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
110 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
  • 11
0 votes
1 answer
24 views

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
  • 33
8 votes
3 answers
215 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
9 votes
2 answers
974 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
  • 532
3 votes
1 answer
136 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
  • 1,373
1 vote
0 answers
126 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
280 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
711 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
  • 2,966
3 votes
0 answers
88 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
674 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 votes
0 answers
86 views

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
321 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
2 votes
0 answers
42 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
1 vote
0 answers
1k views

How to deal with unbalanced binary independent variables in logistic regressions

Suppose I want to investigate the impact of some binary independent variables (let’s say: sex and height [tall/short]) on my binary response (alcohol consumption for instance). The distribution of my ...
Herman Cherniaiev's user avatar

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