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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Pratio and the Probit Model

The question is: The below displays results for a probit model where the dependent variable, Y, equals 1 if Coke was chosen by individual i and 0 if Pepsi was chosen by individual i. Y = 1.4727 - ...
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47 views

How to choose between logit, probit or linear probability model?

To decide whether to use logit, probit or a linear probability model I compared the marginal effects of the logit/probit models to the coefficients of the variables in the linear probability model. ...
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39 views

Tie out probit models from polr and mlogit

I'd like to run a probit regression on the "B1_df" data frame with 3 categorical outcome variables (rank 1,2 or 3). I cannot use glm because there are 3 outcome variables. I would like to be able to ...
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15 views

Is it meaningful to compare a logit and probit model with the same no. of observations and independent variables using AIC and BIC?

For my dissertation i want to show that the logit and probit model produce the same results. So far i've compared the marginal effects, percent correctly predicted and done a scatter plot of the ...
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1answer
103 views

Probit/Logit Model - How to find the parameter $\beta$

I am confused on how to calculate the beta in a probit/logit model Probit Model $P(Y_i=1)=\Phi(X'\beta)$ Logit Model: $P(Y_i=1)=e^{X'\beta}/(1+e^{X'\beta})$ These formula's are great but how do I ...
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17 views

Explanation for negative cross-price elasticities if the two alternatives cannot be complements

I would like some help in interpreting some odd cross-price elasticities that I got from my model. I estimated the following multinomial probit model and calculated the elasticities post-estimation: ...
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1answer
30 views

Standard error for intercept only model in probit regression

How do you calculate the standard error for intercept for an intercept only probit regression model? I was expecting the formula to be: 1 / sqrt(N * p * (1 - p)) where N = no. of obs, p = mean(y). ...
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1answer
15 views

conditional marginal effect at the mean is the same as the average of the conditional marginal effect in the Linear Probability model?

I know that MEM and AVE is rather different in a probit and logit model, but it is true that these two values are the same in the Linear Probability model ?
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11 views

Are there any situations in which an OLS estimation of probability (with a binary dependent variable) might come in handy? [duplicate]

Most textbooks that I've read mention the nonsensical results that you might obtain with an OLS estimation of a probability of a variable being 0 or 1 -- as such, you use the transformations of the ...
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20 views

OLS and Probit Model regarding a dummy variable being dropped

I've been trying to run a regression using a probit model, but I keep getting a dummy variable being dropped from the regression (in Stata's output) because it predicts the success perfectly. The OLS ...
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22 views

How to estimate the parameters of the following log-likelihood function?

I would like to estimate the parameters based on the famous Merton model used probability of default modelling: Suppose firms' logarithmic returns are following the standard normal distribution and ...
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20 views

Instrumental variable standard errors smaller than naive estimator standard errors

I recently estimated the following model using 2 different estimators, multinomial probit and instrumental variable (IV) multinomial probit: $Product_{i}=\beta_0 + ...
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1answer
42 views

A very basic probit model doubt

So, I have just started going through binary choice models, and while explaining probit models- they start with how by specifying alternate distributions of the error term- we can use different ...
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1answer
22 views

Binary random variable, big data frame: does my approach make sense?

I have a large data frame with about 1100 columns containing integers and about 30'000 rows. The last column contains a binary random variable which attains values 0 and 1. 30% of the data frame ...
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1answer
30 views

Is it correct to use sampling weights for reducing multicollinearity in Probit?

I tried to estimate a probit model: $$y=b_1x_1+b_2x_2+b_3x_3,$$ where all variables are string and $x_1$ and $x_2$ are correlated. $x_1$ means GINI index and $x_2 = \text{self-employment rate}\cdot ...
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17 views

Mean and Standard Error of True Percentages (Not Binomial Proportions)

In my line of work I occasionally deal with data on the percent lipid of fish fillet (environmental sampling). The question came up today about how to calculate the mean and standard error and ...
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24 views

The likelihood of response variables in variational Bayesian probit regression

I read the paper Explaining Variational Approximations (J.T. Ormerod & M.P. Wand) and there is a part where they explain variational probit regression with auxiliary variable since the posterior ...
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1answer
46 views

testing nonlinear hypothesis glm R

I estimate probit model: \begin{align*} P(y=1|x_1, x_2, x_3) = \Phi(\alpha_0 + \alpha_1 x_1 + \alpha_2 x_2+ \alpha_3 x_3) \end{align*} using: ...
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1answer
21 views

Tests for mprobit

I have a multinomial probit model with explicative variables that are also all either categorical or binary. I would like to know which are the main statistical tests that I should do before analysing ...
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32 views

Probit Model: Interpretation of marginal effects if explanatory variables are proportions

How do I interpret the marginal effect of an explanatory variable that is a proportion in a probit model? For example if I get a marginal effect of 0.8 does this mean that if the proportion increases ...
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1answer
33 views

Definition of ordered probit

Can someone please provide a definition of the ordered probit model so that I can calculate the value of $\Pr(y \le k | \mathbf{x})$, i.e. probability of observation falling in the $k$-th class or ...
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21 views

Two-Part Model with Panel Regression

Can I estimate in the first part (using probit/logit) of the 2P-Model the probability of an event to occur (using an indicator variable where ind = 0 if dep=0 and ind = 1 if dep>0) and in the second ...
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1answer
28 views

Probit/Logistic Regression: Predicted probabilities vs. marginal effects

I have a very basic question on post-estimation for probit/logistic regression models (frequentist). The non-linear character of the link functions requires us to do post-estimation for a sensible ...
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20 views

Estimating unexplained variance from multivariate probit output (for imputation protocol)

I have a use case in which survey data underreports program participation, and I need to impute new recipients from within the survey data. There are two (exogenously provided) sub-objectives: ...
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34 views

Is there a theoretical distribution for the residuals of a logit/probit regression?

I know there are a number of ways to define residuals for logit/probit regression but is there any theoretical distribution? I've also heard these residuals can be bimodal so maybe there is no closed ...
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40 views

how to perform correlation analysis between binary and continuous variables?

Can any one tell me how to perform correlation analysis between binary and continuous variables? I have to do this analysis for publication wherein we are trying to construct between binary and ...
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1answer
116 views

How can I calculate this integral? $\int_{-\infty}^{\infty} \Phi (a + bX) \phi (c + eX) dx$

Suppose we have the density and distribution of the standard normal. How can one calculate the integral: $\int_{-\infty}^{\infty} \Phi (a + bX) \phi (c + eX) dx$ Note this is not included in the ...
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1answer
246 views

Using IV Probit in Stata

I am trying to estimate an IV model where my dependent variable is on the 0-1 scale, which is why I want a Probit estimator. However, my independent variable is a continuous, endogenous variable. For ...
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1answer
70 views

Computing elasticity of log-transformed variable in probit model

I would like to estimate the own-and cross-price elasticities of demand of a health product. Consider following model: ...
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21 views

Signing the inconsistency of a control functions estimator with an incomplete set of instruments

I've got a model of the form $$ y = 1\left(\alpha+\delta x+\beta_1\tilde v+\tilde\epsilon>0\right) $$ $x$ is endogenous, and $\tilde v$ is a control function residual: $$ \tilde v = ...
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22 views

Interaction vs. moderation, main effects

I am new to this forum and I hope that my questions have not been answered in other posts. I am still confused about moderation and interaction as some sources tell me that they are the same, others ...
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76 views

Beta coefficients of marginal effects

I am writing my master thesis and one of the main regression I perform is a probit. In order to compare the effect of different variables I want to show the different beta coefficients, but not of the ...
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21 views

Probit via MCMC with rare events

I like to estimate a mixed model via MCMC. I had to rely on MCMC, because there are some features in the model like spatial dependence that are much better to estimate with help of MCMC. My model is a ...
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73 views

How to estimate Heckprobit when there is a continuous endogenous regressor?

My model is following Y1=f(Y2 X1); Y1 is a binary variable and whether Y1 observed is depend on X1, X2. Meanwhile, Y2 is endogenous explanatory variabel which determined by Y2=f(X1 X3). My approach ...
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11 views

How do I prove that the the constant in the Probit model minimises its error of prediction?

Lets say the probit model Pr (Y=1|X) = F(xb). Where F is Fi and b is beta. How do I show that b = arg min E(y − Φ (xd))2
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25 views

Using Probit for Prediction - What to do about variance in the estimates?

Suppose I am estimating the following model: $Pr(Y=1|X) = \Phi(X'\beta + \epsilon)$ where $X = [X_1,X_2]$. Further suppose I end up with estimates for $X_1$ and $X_2$ of $\beta_1 = \beta_2 = ...
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1answer
48 views

In the linear probability model (probit or logit), should $\sum_i \hat p_i = 1$?

In the linear probability model (probit or logit), should $\sum_i \hat p_i = 1$?
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1answer
177 views

Predicted probabilities for probit model in R - categorical variable

I am running a probit regression with a random effect: m1<-glmer(Binary~Explan+(1|Random),family=binomial(link="probit")) where Explan is a three-level ...
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1answer
70 views

Interpreting interaction effects in probit regression model

I have run a probit regression model with one 2-way interaction and am having trouble interpreting the results. Both variables are categorical and so one level of ...
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1answer
82 views

Interpreting Marginal Effects

I am running a probit glmer, with a binary response varaible and a categorical explanatory variable with three dummy levels and have tried to calculate the marginal effect using the following code: ...
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1answer
28 views

Same result for post-hoc test and probit regression

I am getting the exact same results for a probit regression and post-hoc tests (simultaneous tests for linear hypotheses) - is this because I have used a dummy variable in the probit model and so it ...
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1answer
33 views

Is it necessary to report standard errors with marginal effects?

I've run a probit regression in R with a random effect and can find no way to get the marginal effects with s.e. and p values. I have therefore tried to calculate the marginal effects 'by hand' by ...
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1answer
41 views

Size of Regression Coefficients

I have run a probit regression and the size of my coefficients seem to be quite big with respect to other similar studies. For example, 0.254 vs 1.207 - does this mean anything in particular or is it ...
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1answer
143 views

Marginal Effects and Standard Errors in R for probit model

I ran a probit regression using the following code: m1<-glmer(Success~Name.Origin+(1|Job.ID),family=binomial(link="probit")) However, I am now unsure how ...
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2answers
121 views

Simultaneous tests for general linear hypothesis question

I have run a probit regression and am now trying to run post-hoc tests. I am trying to compare differences between a 3 level factor variable. I am confused about the difference between running a ...
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53 views

Multiple regression with hierarchical predictors (pros and cons of flattening hierarchical data)

I want to model data that has been empirically gathered. The target (dependent variable) values of the model is binary, so I am likely to use logit (or probit) model. Business rules of thumb used by ...
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51 views

How to interpret log-transformed predictors in probit regression?

I am running a probit model with several continous and one log-transformed predictor (firm size as total assets). I am unsure how to interpret the coefficient of -0.341 on that variable. I used the ...
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2answers
119 views

How to constrain cumulative Gaussian parameters so that the function will intersect one given point?

I am analyzing data from one study where participants had to choose (between two stimuli) the one with higher intensity. One way to look at the data is to fit the proportion of correct choices as a ...
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1answer
25 views

Why is probit regression favouring the Gaussian distribution?

Probit regression is based on the model $P(Y=1 | X) = \Phi(X'\beta)$, where $\Phi$ is the standard normal cumulative distribution function (cdf). Would it make sense to replace $\Phi$ by another cdf? ...
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20 views

Estimate growth rates in a probit/logit model

I consider a situation where k different kinds of bacteria grow together in a petri dish and each kind of bacteria exhibits exponential growth, i.e. the population size over time is given by $N_i(t) = ...