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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8
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1answer
54 views

Deriving likelihood function for IV-probit

So I have a binary model where $y_1^*$ is the latent unobserved variable and $y_1 \in \{0,1\}$ the observed. $y_2$ determines $y_1$ and $z_2$ is thus my instrument. So in short the model is. ...
2
votes
1answer
24 views

What p-value threshold should I use for many-variable probit?

I have a probit with 7,000 observations and 120 dummy independent variables, of which Stata omits 31 because of low n and 2 because of collinearity. I also have a set of five dummies to make a time ...
1
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0answers
9 views

zelig package in R gives the same standard error with robust=TRUE

Any idea why robust option is not working in Zelig package? ...
0
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0answers
9 views

Estimation of a Probit model via data augmentation using JAGS [migrated]

I'm trying to estimate a Probit model with data augmentation. This works without data augmentation, but the end goal is to estimate a multinomial Probit model, where data augmentation is helpful. ...
0
votes
0answers
23 views

Probit with one variable or t-test

I have a dataset with observations from two consecutive elections with data on party choice and the voters income. I want to test if an increase in income, compared to a decrease, causes voters to ...
0
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0answers
7 views

Marginal effect of interaction variable in probit regression using Stata [migrated]

I am running a probit regression with an interaction between two dummy variables. The coefficient is displayed in the regression output but when I look at the marginal effects the interaction is ...
0
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0answers
21 views

Interpreting margins of dummy variables [duplicate]

I have estimated an ordinal probit model in Stata. The dependent variable is walkability. The main independent variables are on a Likert scale (1=agree, 2=partially agree, 3=disagree). The other ...
1
vote
1answer
36 views

Probit model: marginal effects cannot be estimated because one dummy variable was dropped for predicting failure perfectly

I have a basic question about the -margins- command in Stata: I was wondering if there was a workaround to run marginal effects for a model where one of the dummy ...
1
vote
1answer
23 views

Estimating Markov Switching Probit

I attempt to fit the following probit model to a time series where we observe the binary variable $R_{t}$ and another variable $X_{t}$, a latent unobserved variable $y^{*}_{t}$ and a state variable ...
2
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0answers
28 views

IIA assumption: difference logit and probit

Considering the following question about the Independence of Irrelevant Alternatives assumption: Alternatives to multinomial logistic regression It seems as if IIA is only a problem when using a ...
0
votes
0answers
9 views

Business examples for using Probit versus logit model [duplicate]

If some one has an example business problem where you have used probit instead of logit, please share with me. I am not able to understand the situation when to use probit versus logit model. I have ...
1
vote
1answer
37 views

Post-estimation tests for ordinal probit [closed]

I have performed an ordinal probit model in Stata and have 2 queries: The parallel line assumption test (run by oparallel or ...
3
votes
0answers
37 views

Does non-stationarity in logit/probit matter?

I would like to ask - I am using logit to investigate, if some variables improve the risk of currency crises. I have yearly data from 1980 for lots of countries (unbalanced panel), dummy variable is 1 ...
0
votes
0answers
16 views

Ordinal probit model misses one category of independent variable

I am running an ordinal probit model in STATA. the independent variable is a discrete variable and have 2 options )1,2,3) and so i place a prefix i before it. The results show the parameters for 2 and ...
3
votes
1answer
35 views

Interaction term

I wanted to ask about interaction term. I am having an ordinal probit model. The two of the independent variables that i have are discrete 1-Uni( a 0,1 dummy variable) and second is continuity (1,2,3) ...
1
vote
1answer
23 views

How to identify a binary model with varying mean and variance

I have the following model, Yi = 1.( Xiβ + εi > 0) where εi~ Ɲ(Ziδ,1). or Yi = 1.( Xiβ + εi > 0) where εi~ Ɲ(0, (µ + Ziδ)2) with a binary model, can i identify β and δ in such a model? How? and ...
0
votes
1answer
62 views

Confidence intervals for ordered probit [closed]

I'm attempting to compute confidence intervals for an ordered probit. I am a graduate student and it was suggested as one of the tasks to add to my final paper. I have found a few papers discussing it ...
1
vote
1answer
48 views

Linear probability model

Is there any advantage or any situation when the Linear probability model is superior than Logit model and Probit model, apart from its simplicity.
0
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0answers
24 views

Interpretation of multinomial probit using MNP package

I used MNP to analyze the election in 1992 and I want to know the probability that non-partisan voters vote for Bush when the personal economic condition varies and the effect of education is ...
2
votes
1answer
76 views

2SLS probit vs LPM

I am using 2SLS to estimate the effect of education on the probability that one works. In the first stage I regress education on my instrument and the other exogenous control variables. The same ...
0
votes
1answer
20 views

writing ordinal probit model equation

assyme the following function walkabality f(conectivity, gender, age, uni, conectivityuni) where wallkability is a dep ordinal variable (1-3 i.e agree, partially agree, disagree) gender m agem uni are ...
2
votes
2answers
32 views

Why is simulation used for probit choice probabilities?

As explained in this answer, the probability of a certain outcome in a Logit model can be written as $$ P=\int_{\varepsilon=-\beta'x}^{\infty} f(\varepsilon)d\varepsilon\\ = 1- F(-\beta'x) = ...
4
votes
1answer
117 views

Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an ...
1
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0answers
16 views

Binary decision, evaluating Bayesian probit regression?

Laplacian logistic regression. I have a training set of data and an evaluation set. The response is binary. I have to verify the models by calculating posterior predictive on the evaluation set. Last ...
1
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0answers
27 views

Relationship between the parameters of the Normal distribution and parameters in the probit with multiple predictors?

According to A. Agresti (2007, p. 73) in binary probit regression: "The parameters of the normal distribution relate to the parameters in the probit by mean (mu = -alpha/beta) and standard deviation ...
1
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1answer
39 views

Hypothesis of probit model

I would like to know if we need to test the assumption of residuals' normality when we have probit model? And if this assumption is violated how can I correct it with Stata? In the case of Probit ...
-1
votes
1answer
53 views

Predicted probabilities from probit

Assume following probit model: $y_i$ = $\phi$($\beta_0$+$\beta_1x_1$+$\beta_2x_1^2$+$\beta_3d_1$+$\beta_4d_2$) where $d_1$ and $d_2$ are dummies or in Stata: ...
2
votes
1answer
107 views

2SLS - logit/probit in the second stage?

I just have a quick question: what if I'm interested in estimating a logit/probit model in the second stage, can I follow this two-step procedure by running OLS in the first stage (endogenous variable ...
0
votes
0answers
42 views

probit consistent estimation

I have a probit model $$Pr(LFP=1) = β_1 + β_2\ln(WW_i) + β_3 KL6_i + β_4 NWIFEINC_i + β_5 WA_i + β_6 WE_i + u_i$$ with $$\ln(WW_i) = α_1 + α_2WE_i + α_3 AX_i + α_4 AX_i^2 + e_i $$ WW is a continuous ...
0
votes
1answer
81 views

Generating data from Probit regression, cut off 0 and variance 1 necessary?

I am trying to create a dataset using a Probit regression model in R, where I have an intercept and three covariates. I first fix a set of coefficients for the three covariates, generate these ...
0
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0answers
32 views

Penalized ML estimation of non-linear probit

I have a model of the form $P(y_i=1) = \Phi(\frac{w_1^{\beta'x_i}-w_2^{\beta'x_i}}{\sigma' x_i})$ where $y_i$ is a binary response, $\Phi$ is the normal CDF, $w_1$ and $w_2$ are non-negative ...
0
votes
0answers
12 views

interaction term in ordinal probit model

I am working on an ordinal probit model. If i plan to introduce an interaction term between a dummy variable and an ordinal variable (having 3 and in some cases 5 categories) , then is such an ...
0
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0answers
40 views

Heckman Probit Model the number of explanatory variable in selection model?

I run a Heckman Probit model which is sometimes called as Heckit. It consists two parts like this: |1| Y X1 X2 X3, |2| select(Y2 X1 X2 X3) Y covers Y2 but not vice versa. The question is whether i ...
3
votes
1answer
56 views

non-classical measurement error in a binary outcome model

I have a binary outcome model that I am estimating with a probit, so $$\Pr(Y=1\vert x,z)=\Phi(\alpha +\beta\cdot x^* + z'\gamma)$$ I am interested in the marginal effect of $x^*$ on $\Pr(Y=1\vert ...
0
votes
1answer
17 views

How to compute the pdf for logit/probit models?

According to the probit/logit models, the change in probability due to a change in an explicative variable x is given by the following equation: P(Y = 1 |X) = ...
1
vote
1answer
35 views

Interpretation of Odds in Probit Regression

Logistic regression is concerned about modelling log-odds, i.e. logits. Hence, the odds of the computed probabilities can be interpreted accordingly. However, when estimating a probit model, one could ...
3
votes
1answer
99 views

Comparison of log-likelihood of two non-nested models

I know I can only use the log-likelihoods of two models as selection criterion if they are nested. However, I don't understand this completely. Why isn't it possible to apply this reasoning to ...
0
votes
0answers
18 views

Is the residual term from probit or logit model orthogonal to $X\beta$?

It is well known fact that for OLS model, $Y=X*\beta+ \epsilon$. $Y$ can be decomposed into two orthogonal components: $X\beta$ and residual term $\epsilon$. The two terms are independent of each ...
0
votes
0answers
22 views

Which type of model suits the best for my dataset? (y=Binary; x1,x2=Nominal)

My dependent variable is winning (Binary: WIN-1, DRAW/LOSE-0). I classified soccer teams by there post-match data. Therefore, I've got a cluster membership nominal variable for each team (x1) and a ...
0
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0answers
28 views

Probit regression with misclassified binary dependent variable in R

Is there a generalized linear mixed-effects model implementation for R that could handle misclassified binary data? Unless I have overlooked something in the documentation, glmer with ...
1
vote
1answer
74 views

What to do when parallel regression assumption violated

When the dependent variable in a regression model is ordinal, I know that we often use ordered probit/logit to estimate the model. These have an assumption called the parallel regression assumption. ...
3
votes
0answers
38 views

Fast Algorithm for Bayesian Measurement Model

I want to estimate a Bayesian Measurement model. That is I am concerned with the rating of each judge $j$ of the value of some trait $z$ for each observation $i$. Not all raters will have rated each ...
0
votes
1answer
41 views

Probit assumptions of unbiasednes

Can someone tell me what are the assumptions of unbiasednes for simple probit model like this $ Prob(y=1|x) = G^{-1}(\beta_0 + x\beta) $ I know that dependent variable models are estimated by MLE so ...
4
votes
1answer
134 views

Clarifications about probit and logit models

I know that there is a very good explanation of the technical differences of probit and logit model in this question. However, I would appreciate some common sense clarifications which can be very ...
1
vote
1answer
170 views

Random-effects probit model

I am currently using a mixed binomial model with the following specification in a paper I recently submitted (using lme4): ...
6
votes
1answer
283 views

2SLS but second stage Probit

I am trying to use instrumental variables analysis to infer causality with observational data. I have come across a two-stage least squares (2SLS) regression which is likely to address the endogeneity ...
5
votes
1answer
979 views

Consistency of 2SLS with Binary endogenous variable

I have read that 2SLS estimator is still consistent even with binary endogenous variable (http://www.stata.com/statalist/archive/2004-07/msg00699.html). In the first stage, a probit treatment model ...
4
votes
1answer
89 views

Using predicted probabilities as regressors

I am working on a project where I investigate growth in wages due to migration. I correct for the endogeneity in the decision to migrate (only those that are most likely to gain from migration will ...
0
votes
0answers
15 views

Multiple choice models

As it is known, there are logit and probit models based on logistic and normal distribution. Can I build a new same model based on other distibution? For example, it may be double exponentional ...
4
votes
1answer
181 views

Regression model for road accidents data

I want to model road accidents data to identify 1) the major causes of accidents and 2) predictors that can explain the accident severity measured by the passengers injury level (minor, major, fatal). ...