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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46 views

Logistic/Probit Regression if the response variable is not a probability

I am working on a model which involves predicting a ratio between 0 and 1 using a number of variables. The ratio in question cannot be thought of as a probability. I am wondering if a logistic ...
1
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0answers
5 views

Adding variables to conjoint

Could you please help me with conjoint analysis. I would like to conduct choice based conjoint. I have created attributes and levels of the product. My dependent variable is willingness to pay. So ...
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1answer
24 views

What exactly are some fundamental differences between probit model and logistic regression [duplicate]

It seems that both these refer to cases where the regressed (dependent) variable can only take certain values, as opposed to a linear regression. So what is the difference between probit and logistic ...
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0answers
18 views

Truncated normal hurdle (for corner solution responses): how to use results for prediction?

I have fitted a (Cragg's) truncated normal hurdle model over a dataset in which the dependent variable is either zero or positive. The model consists of two parts: a probit which estimates the ...
2
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0answers
19 views

Regularized (LASSO) probit regression

I have a binary variable Y that is a dichotomization of an unknown latent variable, generated by a regression model with normal error. Therefore it makes sense to fit a probit model to Y. R enables me ...
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1answer
27 views

Good resource on Probit and Logit Analysis

I am looking for a book (containing a chapter)/pdf on Probit and Logit Analysis, with Logit Regression. I read Casella Berger but I find the topic is rather poorly written. Also, some universities ...
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1answer
13 views

Marginal effects from Bayesian probit

I'm trying to run a standard Bayesian probit model, and I can't find any packages in R that will give me marginal effects (the most common way to interpret probit results in my field), nor do they ...
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0answers
22 views

Random Probability Matching for unknown prior in multi arm bandit setting

I am currently reading this paper by Steven Scott, which describes random probability matching as a practical solution to the multiarmed bandit. I am having trouble understanding some steps and ...
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1answer
91 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
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1answer
31 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 ...
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18 views

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

Any idea why robust option is not working in Zelig package? ...
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0answers
29 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 ...
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0answers
22 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 ...
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0answers
61 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
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1answer
28 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
35 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 ...
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0answers
10 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 ...
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0answers
53 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 ...
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0answers
19 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
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1answer
38 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) ...
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1answer
25 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 ...
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1answer
74 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 ...
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1answer
61 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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30 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
107 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
24 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
34 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
143 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 ...
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0answers
21 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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31 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
44 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
63 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
177 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
44 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
95 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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40 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
15 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
46 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
61 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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1answer
27 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
49 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
126 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
22 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
24 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
32 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
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1answer
96 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
45 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
44 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
147 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
217 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): ...