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.

learn more… | top users | synonyms

1
vote
0answers
9 views

The likelihood of response variables in variational Bayesian probit regression

I read the paper Explaining Variational Approximations (J.T. Ormerod & M.P. Wand) and there is a part where they explain variational probit regression with auxiliary variable since the posterior ...
0
votes
0answers
20 views

testing nonlinear hypothesis glm R

I estimate probit model: \begin{align*} P(y=1|x_1, x_2, x_3) = \Phi(\alpha_0 + \alpha_1 x_1 + \alpha_2 x_2+ \alpha_3 x_3) \end{align*} using: ...
1
vote
1answer
20 views

Tests for mprobit

I have a multinomial probit model with explicative variables that are also all either categorical or binary. I would like to know which are the main statistical tests that I should do before analysing ...
1
vote
0answers
21 views

Probit Model: Interpretation of marginal effects if explanatory variables are proportions

How do I interpret the marginal effect of an explanatory variable that is a proportion in a probit model? For example if I get a marginal effect of 0.8 does this mean that if the proportion increases ...
0
votes
1answer
26 views

Definition of ordered probit

Can someone please provide a definition of the ordered probit model so that I can calculate the value of $\Pr(y \le k | \mathbf{x})$, i.e. probability of observation falling in the $k$-th class or ...
0
votes
0answers
13 views

Two-Part Model with Panel Regression

Can I estimate in the first part (using probit/logit) of the 2P-Model the probability of an event to occur (using an indicator variable where ind = 0 if dep=0 and ind = 1 if dep>0) and in the second ...
0
votes
1answer
19 views

Probit/Logistic Regression: Predicted probabilities vs. marginal effects

I have a very basic question on post-estimation for probit/logistic regression models (frequentist). The non-linear character of the link functions requires us to do post-estimation for a sensible ...
1
vote
0answers
25 views

Instrument testing for multinomial endogenous variable and multinomial outcome

I need to manually test the validity of instruments (IVs) for the following model: $Provider_{ij}=\beta_0+\beta_1Insur1_{ij} +\beta_2Insur2_{ij} +\beta_3Insur3_{ij}+\beta_4Insur4_{ij}+X\beta + Y\delta ...
0
votes
0answers
17 views

Estimating unexplained variance from multivariate probit output (for imputation protocol)

I have a use case in which survey data underreports program participation, and I need to impute new recipients from within the survey data. There are two (exogenously provided) sub-objectives: ...
1
vote
0answers
32 views

Is there a theoretical distribution for the residuals of a logit/probit regression?

I know there are a number of ways to define residuals for logit/probit regression but is there any theoretical distribution? I've also heard these residuals can be bimodal so maybe there is no closed ...
0
votes
0answers
35 views

how to perform correlation analysis between binary and continuous variables?

Can any one tell me how to perform correlation analysis between binary and continuous variables? I have to do this analysis for publication wherein we are trying to construct between binary and ...
2
votes
1answer
96 views

How can I calculate this integral? $\int_{-\infty}^{\infty} \Phi (a + bX) \phi (c + eX) dx$

Suppose we have the density and distribution of the standard normal. How can one calculate the integral: $\int_{-\infty}^{\infty} \Phi (a + bX) \phi (c + eX) dx$ Note this is not included in the ...
0
votes
1answer
133 views

Using IV Probit in Stata

I am trying to estimate an IV model where my dependent variable is on the 0-1 scale, which is why I want a Probit estimator. However, my independent variable is a continuous, endogenous variable. For ...
0
votes
1answer
46 views

Computing elasticity of log-transformed variable in probit model

I would like to estimate the own-and cross-price elasticities of demand of a health product. Consider following model: ...
2
votes
0answers
19 views

Signing the inconsistency of a control functions estimator with an incomplete set of instruments

I've got a model of the form $$ y = 1\left(\alpha+\delta x+\beta_1\tilde v+\tilde\epsilon>0\right) $$ $x$ is endogenous, and $\tilde v$ is a control function residual: $$ \tilde v = ...
0
votes
0answers
12 views

Interaction vs. moderation, main effects

I am new to this forum and I hope that my questions have not been answered in other posts. I am still confused about moderation and interaction as some sources tell me that they are the same, others ...
0
votes
0answers
58 views

Beta coefficients of marginal effects

I am writing my master thesis and one of the main regression I perform is a probit. In order to compare the effect of different variables I want to show the different beta coefficients, but not of the ...
0
votes
0answers
19 views

Probit via MCMC with rare events

I like to estimate a mixed model via MCMC. I had to rely on MCMC, because there are some features in the model like spatial dependence that are much better to estimate with help of MCMC. My model is a ...
0
votes
0answers
46 views

How to estimate Heckprobit when there is a continuous endogenous regressor?

My model is following Y1=f(Y2 X1); Y1 is a binary variable and whether Y1 observed is depend on X1, X2. Meanwhile, Y2 is endogenous explanatory variabel which determined by Y2=f(X1 X3). My approach ...
0
votes
0answers
11 views

How do I prove that the the constant in the Probit model minimises its error of prediction?

Lets say the probit model Pr (Y=1|X) = F(xb). Where F is Fi and b is beta. How do I show that b = arg min E(y − Φ (xd))2
1
vote
0answers
22 views

Using Probit for Prediction - What to do about variance in the estimates?

Suppose I am estimating the following model: $Pr(Y=1|X) = \Phi(X'\beta + \epsilon)$ where $X = [X_1,X_2]$. Further suppose I end up with estimates for $X_1$ and $X_2$ of $\beta_1 = \beta_2 = ...
1
vote
1answer
43 views

In the linear probability model (probit or logit), should $\sum_i \hat p_i = 1$?

In the linear probability model (probit or logit), should $\sum_i \hat p_i = 1$?
1
vote
1answer
151 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 ...
1
vote
1answer
57 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
votes
1answer
65 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
votes
1answer
25 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
votes
1answer
31 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 ...
0
votes
1answer
40 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 ...
0
votes
1answer
95 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 ...
1
vote
2answers
90 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 ...
0
votes
0answers
35 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 ...
3
votes
0answers
48 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 ...
1
vote
2answers
102 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 ...
0
votes
1answer
25 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? ...
1
vote
0answers
18 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) = ...
1
vote
1answer
662 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 ...
0
votes
2answers
83 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
vote
0answers
20 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 ...
0
votes
1answer
61 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 ...
2
votes
0answers
25 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
votes
0answers
107 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 ...
0
votes
1answer
49 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 ...
1
vote
2answers
72 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 ...
0
votes
0answers
34 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 ...
9
votes
1answer
117 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
81 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
vote
0answers
75 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
votes
0answers
64 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
votes
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 ...
1
vote
0answers
233 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 ...