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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29 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.
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15 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
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1answer
38 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 ...
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1answer
12 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 ...
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2answers
28 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) = ...
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1answer
63 views
+50

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 ...
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0answers
15 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 ...
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0answers
24 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 ...
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1answer
36 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 ...
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1answer
39 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
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1answer
58 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 ...
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0answers
32 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 ...
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1answer
64 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 ...
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0answers
23 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 ...
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0answers
7 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 ...
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0answers
23 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
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1answer
47 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 ...
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0answers
35 views

Is the stationarity condition necessary for estimating logit/probit model?

I'm going to estimate both a logit and a probit model. Since both the models contain lagged explanatory variables, I want to know if the stationarity condition for this variables has to be verified. ...
0
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1answer
11 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
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1answer
25 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
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1answer
73 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 ...
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0answers
13 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 ...
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0answers
21 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 ...
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0answers
22 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 ...
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1answer
31 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
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0answers
37 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
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1answer
40 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
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1answer
119 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 ...
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1answer
99 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
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1answer
182 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
559 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
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1answer
72 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 ...
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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
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1answer
175 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). ...
2
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0answers
50 views

Autoregressive distributed lag (ADL) models and Dummy variables

Is it okay to use an Autoregressive Distributed Lag (ADL) model with a dummy variable as the dependent variable? Or should I use a combination of logit/probit with an ADL model? I realize it might ...
2
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1answer
83 views

Probit or Logit in Generalized Linear Model [duplicate]

I'm trying to apply GLMs on a dataset in which dependent variable Y is dichotomous. I applied either logit and probit models, and probit fitted better than logit model. How do I justify the choice of ...
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0answers
58 views

Two-stage probit least squares

I am estimating a two-stage probit least squares (2SPLS) model. From the readings I have done so far, it appears the first stage of the 2SPLS has to be estimated with a probit, and then a continuous ...
1
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1answer
74 views

GLM link function for bimodal probit fitting?

I am trying to model a set of data I have physical reason to believe can be represented by a bimodal normal cumulative distribution function (Technically it is a bimodal log-normal CDF, but I think I ...
4
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1answer
50 views

Null hypothesis of probit model Wald test

Say I estimate the following probit model: $$ins = \Phi(\alpha + \beta_1 age + \beta_2 educ + \beta_3 hg + \beta_4chronic + \beta_5 hisp + \beta_6 lin) + u$$ where: $ins = 1$ for any individual who ...
5
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0answers
64 views

Identifiability in generalized linear random effect model?

Suppose I observe binary $Y_{ij}$ for $i = 1, ..., N$ and $j = 1, ..., J$ and I want to model $$\Pr(Y_{ij} = 1 \mid \lambda_{i}) = \Phi(\lambda_{ij}), \qquad [Y_{ij} \perp Y_{ij'} \mid \lambda_i]$$ ...
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0answers
117 views

Statistical Analysis help for thesis - Correlation, Probit, Tobit and Moderation

Hello CrossValidated users! I am writing here cause I need some guidance on my statistical analysis which has turned out far too complex for my basically begineer stats skills and my self research. ...
2
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1answer
90 views

Estimating standard error in a probit: econometrics or programming problem?

This question has two parts, as I do not understand whether my problem is theoretical (identification of the parameters) or practical (insufficient R skills). Econometrics Most "probit" style ...
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0answers
28 views

Probit and probability of being a member of a group

I was wondering if there was a specific way to convert probit regression results into a probability that a test subject is a member of group 1 (vs group 0) if you break up the dependent variable into ...
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0answers
54 views

Conjugate Prior for Probit likelihood function

I am trying to do a Bayesian analysis in which my likelihood function is a probit function on two parameters. From various sources, I found out that Normal distribution is a conjugate prior to probit ...
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0answers
41 views

robust standard errors newey-west in probit model

In R there are a way to calculate robust standar error newey when we adjust a linear model, using The function NeweyWest() in the ...
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0answers
13 views

How to model my data into a probit

I am beginning my thesis, and I need some advice. I am trying to estimate a probit model. The binary dependent variable is employment status, and the independent variables include network size, age, ...
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39 views

dynamic probit model

I want to know if you can helpme I have a binaray response yt take values 1,0 and a covariate Xt continua and I have to estimate the parameters of the model using maximun likelihood method ...
2
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0answers
56 views

Probit regression in R giving singular Hessian matrix

I am trying to run a probit regression using panel data in R by first computing the log likelihood and then using the optim function to optimize. Scale of ...
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89 views

Probit regression in R using panel data - Do we need to normalize the predictors?

I am trying to run a probit regression using panel data by first computing the log likelihood and then using the optim() function to optimize. I have a couple of ...
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0answers
68 views

Mixture of probits: understanding truncated-based likelihoods

I am trying to implement a mixture model of probits to infer the best decision boundary for every latent subpopulation. When doing Gibbs sampling, we eventually have to compute $P(y^* | w_c)$ where ...