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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44 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
33 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 ...
0
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1answer
31 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: ...
0
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1answer
24 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 ...
0
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1answer
22 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
35 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
23 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
56 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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0answers
16 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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38 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
82 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
19 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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10 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) = ...
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1answer
143 views

Seemingly unrelated bivariate probit for endogeneity: interpretation of Rho

I would like to estimate the effect of health insurance coverage on type of healthcare provider chosen--either public or private--at last illness using a nationally representative sample of people in ...
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2answers
63 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 ...
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0answers
13 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
33 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
22 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
39 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
33 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
36 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
26 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
95 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. ...
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1answer
45 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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26 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
38 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
23 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
124 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 ...
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1answer
33 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
42 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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78 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 ...
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1answer
42 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
84 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
68 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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42 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 ...
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1answer
139 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
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1answer
33 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
36 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
166 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
27 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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37 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
46 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
103 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
261 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
45 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
109 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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46 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 ...