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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.

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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/...
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Calculating the CDF value of a random variable [closed]

I have the following equation: It seems that there is a random variable inside the CDF brackets of a normal distribution in the second half of the equation ($z_1$). It seems that we can use ...
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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.
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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 ...
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1answer
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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 ...
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Fitting an “extended probit model”

I am trying to fit a probit model on a survival data. ...
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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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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.
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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 ...
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Difference in Coefficient Interpretations of Linear Probability, Logit MFX, and Probit MFX Models

I have been trying to make sense of what the difference in a LPM, Logit Marginal Effects, and Probit Marginal Effects models would be. For instance, say I ran $employment = \beta_0+\beta_1edu+\dots+...
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1answer
58 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)...
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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 ...
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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 $\...
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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 ...
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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 ...
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Bivariate probit with a fixed rho

In a series of papers by Altonji, Elder and Taber -- for example 1 -- they check the robustness of bivariate binary choice models by seeing what values of $\rho$ (the correlation coefficient between ...
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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 ...
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1answer
208 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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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 ...
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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 ...
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Soccereconomics project predicting soccer results

I am currently research soccereconomics for a small class project. I have download data on the results of each time, which also gives me a price that the team can be traded on. My question is, I ...
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how to report marginal effects of variables omitted due to perfect separation?

i have a few variables that come up as omitted in a probit model but are necessary for a later heckman model, when i write up my results what is the custom for reporting the missing coefficients in ...
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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 ...
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Can natural log transformed variables used in probit models and if so how are their coefficients to be interpreted?

i have several log-transformed continuous variables in my model and want to estimate their impact on likelihood of sale. can i include (natural) log transformed variables in a probit model? if so how ...
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Bivariate Probit/Logit : precise interpretation of coefficients

I am working with a bivariate Probit/logit model and I am a little bit confused over the interpretation of the coefficients. I my model, my two dependant variables are positively correlated. Tel me ...
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OLS and Probit possible on large sample enough?

I think I understood that normality of residuals may not be a problem if the sample is large enough (cf, here). My question is: Would my sample be large enough to be analysed using a probit and an ...
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Pseudo R2 and prob>chi2

I am running a profit regression and I am finding difficulties in interpreting the prob>chi2 =0.0000 statistic. Also, should I be concerned that Pseudo R2=0.0209? How do I interpret it. Thank you.
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Bivariate probit : is there a heteroscedastic version of the model?

I know there exists a version of the simple probit model which is robust to heteroscedasticity (the heteroscedastic probit model). Is there an equivalent for the bivariate probit model? Is there a way ...
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Deriving the gradient vector of a Probit model

Consider the probit regression model where the pdf of $y_{i}$ is $$f(y_{i};\mathbf{\beta}) = \mu_{i}^{y_{i}}\left ( 1 - \mu_{i} \right )^{1 - y_{i}},$$ where $y_{i} = \left \{ 0,1 \right \}$ and $\...
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Goodness of fit after pglm regression

I'm studying individual characteristics driving a fraudulent behavior. I'm using a panel probit estimator with random effects. ...
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1answer
67 views

R/Stata Can I include a categorical variable in a Logistic regression when one of its values perfectly predicts one of the outcomes?

Suppose I have the following data df = data.table('y'= c(1,1,1,1,1,1,1,0,0,0,0), 'x' = c(1,1,1,1,1,1,1,1,1,0,0)) where x = 0 perfectly predicts y = 0. I ...
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Is a binary probit model which predicts the outcome of every observation to be true still useful for analysing effects of explanatory variables?

So I have a binary probit model for labour participation restricted to a specific age group and region. The remaining explanatory variables include sex, education, and having dependent children (all ...
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A question about collineartiy with the dependent variable in a probit model

I am using a probit model to measure voter consistency. A voter is considered consistent on an issue if she holds a position on an issue and then votes for a party who holds the same issue (a voter ...
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Difference between probit of log(x) and probit using inverse lognormal cdf of x

Topic: probit and logprobit regression. Context: I have to implement a model of size at maturity in a wild population and I must choose a binomial linear model to estimate the parameters based on ...
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What can you infer about the effects of explanatory variables in a binary probit model?

Obviously in linear regression, the coefficient tells you whether the effect of a change in an explanatory variable on the response variable is positive or negative and how much a change of one unit ...
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Which p-value should I use ? (Probit and interaction terms)

I ran a Probit model to determine which variables/interaction terms are significant. I got these results: Probit: "mino" is not significant. Whith the margins command (Averadge marginal effect) I ...
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How to calculate probit marginal effects over groups with Stata [closed]

How to properly calculate marginal effects of a probit model to capture marginal effect of one variable over a group. The setup is the following (simplified version): Data: ...
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Logistic regression model / decision trees to an ordered factor

A have a data frame with shops with a number of independent variables (all categorical), I'm trying to find a decent model to predict a dependent variable that is ordered and categorical (ie "small", "...
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68 views

Is there heteroskedasticity in binomial GLMs?

We know a linear probability model (LPM) will produce heteroskedastic errors by definition because of how the variance of a bernoulli r.v. is defined. My question is whether the same is true for logit/...
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test the significance of the marginal effects of a spatial probit model

I'm using the CRAN package spatialprobit to estimate a Spatial Probit. The function sarprobit calculates the marginal effects ...
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80 views

Probit model result in Std. error =“Inf” and p.values = 1

I have some issues with estimating probit models in R. I have an unbalanced panel dataset of 13,400 firm-year observations. When I estimate my model certain variables result in standard errors that go ...
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Build scorecard from binomial regression results

I have a dataset with a dependent binomial variable (say: sellable car yes/no) and a bunch of categorical variable (say: automatic/manual gear, suv/sport car, old/new car, etc.). My final aim is to ...
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Are there problems with estimating regressions of longitudinal data by year?

I am studying the behavior of 900 firms for a period of 10 years. The data is a balanced panel. Multivariate analysis consists of Tobit estimations for each year and probit estimations for each year. ...
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How can predictions in a Probit model be made dependent on dynamic dummy variables?

I'm not sure if my question title is very clear, so let me clarify. I'm running the following probit regression on data that is sorted by date (ascending): ...
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266 views

Heckmann two-step in probit in R

I haven't found an answer to this online: Is there any package / easy to implement way to estimate a probit with heckman correction in R? The corresponding function in STATA would be heckprob
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Recursive nonlinear models [closed]

I have the following recursive linear model $X = \varepsilon_1$ $Z = \beta_2 X + \varepsilon_2$ $Y = \beta_3 Z + \varepsilon_3$ By solving the model its easy to see that $E[Y|X, Z]$ is linear too. ...
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Difference between predicted value and expected value for binary model

For a binary model with Y as the dependent variable and X1, X2, and X3 as independent variable, my understanding is that the predicted value is the value of Y at specified values of of X1,X2,X3. Ex. ...
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time series mainly characterized by structural breaks - how to model?

I am given a financial time series that is characterized by a bunch of structural breaks, i.e. the series isn't moving (literally at all), but at some points in time the series jumps up or down. Then ...
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Derivation of confidence and prediction intervals of predictions for probit and logit (and GLMs in general)

The derivation of the prediction interval for the linear model is quite simple: Obtaining a formula for prediction limits in a linear model . How to derive the confidence and prediction intervals for ...
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residuals of fractional probit regression

I have a data set whose dependent variable is confined to the range [0,1]. I modeled it using fractional probit regression in R ...