Questions tagged [probit]

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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How to identify value of variable $A$ at which variable $B$ exhibits discontinuity

I have reason to believe that an indicator variable $B$ is generated by an underlying process that disproportionately assigns a value of 1 to $B$ once another variable $A$ has passed a certain ...
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In Bayesian estimation, when can regression coefficients and scale parameter be jointly identifiable? When not?

Exercise 14.2 in Koop, Poirier and Tobias's book (i.e. Bayesian econometric methods) talks about the case that in probit model, the regression and scale parameter are not jointly identified. I ...
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How can I use matching to create a pseudo panel?

I would like to know if there is a way to build a pseudo panel dataset using repeated cross sectional survey data by matching individuals across rounds using something similar to propensity score ...
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Predicted probabilities - Tobit/probit - differences

For long I have been a silent reader of your forum, but now I have to ask you for your expertise, as I haven't found an answer to my question yet. Short to my back story: My aim is to calculate the ...
187 views

Cluster analysis using the posterior distribution of a Bayesian correlation matrix

Background and Problem I recently ran a Bayesian multivariate epidemiological meta-analysis on prevalence estimates for several disorders. This analysis included a probit-based model to deal with the ...
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Probit coefficients sum to one

I was recently asked the reasons and implications of constraining the coefficients of an Ordered Probit estimation to sum to one. I sincerely never heard of it. I'm aware of the convexity property ...
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Are multivariate probit models with the same set of explanatory variables for each outcome more efficient that piecewise probit regressions?

I understand that multivariate probit models are analogous to SUR models. In the SUR case, there's no efficiency gain by fitting a SUR model over several independent OLS regressions when the model ...
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Bayesian confidence interval of ED50

I want to calculate Bayesian confidence interval of PD 50 (median protective dose) values as shown in this paper DOI: 10.1016/j.vaccine.2006.12.049. My data is ...
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Heckman Selection model based on logistic regression

I am working with censored data and would like to employ a selection model. As far as I can see, the most frequently applied selection model is the Heckman selection model that assumes a two stage ...
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How to use multinomial probit coefficients to predict?

I fitted a multinomial probit model with one independent categorical variable Y (levels 1,2,3) and two explanatory variables X1 and X2. Using mlogit package in R like this: ...
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vcovHC (heteroskedasticity) in pooled and panel probit

I run two types of regressions in R: 1) Using a panel probit of the following form ...
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When can we use random effects?

I use a probit model with longitudinal data including the same individuals (here customers) for 9 months. My dependant variable (event: does customer leave the company this month) is binary. The event ...
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Where is penalized probit regression?

I am trying to fit penalized model for binary outcome with few events and correlated covariates. Probit and logistic regression models are among the most widely used models for binary outcome. I am ...
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monotonic transformation, probit vs logit

In my firm I am developing a model using a probit model. I noticed that when benchmarking with a logit specification, the logit slightly improves the model goodness-of-fit. Talking with a colleague ...
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Multivariate multinomial probit

I would like to jointly estimate 4 variables. Two of them are categorical and the two others are binary. So I thought about a "multivariate multinomial probit model", but did not find much. What ...
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How to calculate likelihood of probit regression model conditioned on group membership?

Consider a probit regression model with Bernoulli likelihood. Assuming there are $K$ groups, we assume, $$\mathbb{E}[y_i|G_i = k] = \Phi(\theta_k)$$ where $\Phi(\cdot)$ is standard Gaussian CDF. ...
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Interpreting bivariate/multivariate probit model (Rstan implementation)

I'm having trouble with inference from the posterior predictive distribution I've generated from a multivariate probit model I constructed using Rstan. My primary interest in the model is to estimate ...
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How to do status-quo probit analysis in R

I am wanting to estimate the median age of menarche. I have the age of girls and whether or not they were currently menstruating. From the literature review, I see that a probit analysis is used for ...
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Deciding between Probit and Multi-nominal Probit if one of three categories is very unlikely? Proving randomness of that category?

I have a choice variable, which is either A or B or C. But apparently C is hardly chosen. And for me it seems like it more "happens to be chosen". I want to estimate which some continuous covariates, ...
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Probit with Likert Scale independent variables

I am doing some research on effectiveness. I have data from a questionnaire with likert scale answers and a dependent variable which is a dummy variable. I have done some research before with nominal ...
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Latent variable interpretation of generalized linear models (GLMs)

Short version: We know that logistic regression and probit regression can be interpreted as involving a continuous latent variable that gets discretized according to some fixed threshold prior to ...
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Run fixed effect and logit regression on a national survey that need to be “weighted” in R?

I am a beginner user of R. I am using a national survey to test what variables influence the participation in complementary pensions (the participation in complementary pension is voluntary in my ...
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Steps of multinomial probit estimation

Does anybody have any source containing explanation of steps in estimating coefficients of multinomial probit model (from likelihood function to first and second derivatives)? Thanks in advance. EDIT:...
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using glmer instead of lmer with transformed variables

I am working with response time data. In our domain (eye-movements), there is an influential model (the LATER model by Carpenter) that makes clear predictions about how the reciprobit plot of the data ...
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Log-likelihood function of probit model

I need help with the maximization of log-likelihood function of probit model. I don't understand how the term $s_i$ in the second derivative ended there as a standalone term? The $\Phi(s_i)$ is normal ...
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Probit coefficients estimation

I am learning how to estimate coefficients of probit model with maximum likelihood. However, I don't know the exact solution how to derive second order conditioning equation from the first order ...
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Partial/marginal effects after probit regression

Is it plausible to get positive coefficients after running a probit but negative partial/marginal effects? If so, what is the intuition?
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GLM.fit() in Matlab vs. Python Statsmodels: why the different results?

In what ways is Matlab's glmfit implemented differently than Python statsmodels' GLM.fit()? Here is a comparison of their ...
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Endogenous ordered probit (Stata, R)

main equation $y_1 = y_2 \beta + x_1 \gamma + u_i$ Instrumental equation $y_2=x_1 \pi_1 + x_2 \pi_2$ I have a binary endogenous variable $y_2$ in my main estimation equation. My instrument $x_2$...
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Ordered Probit Regression Results Interpretation

Suppose I have an opinion survey on some topic. Both my dependent variable and independent variable are categorical variables. My question is, if I use the ordered probit model, how do I interpret the ...
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Derive z- or t-statistic from p-values of regression coefficients from a probit/logit model

I have the results from an empirical study reporting the results for a probit and logit model. They just report the p-values of the regression coefficients. I want to derive the corresponding $t$-/$z$-...
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Is there any other way to get LD50 for these cases when Probit does not yield results?

To determine a lethal dose 50 (LD50) (ie the dose or concentration of a compound that kills 50% of the insects) I have done a Probit regression. In some cases, the p square CHI is > 0.05 or the ...
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Elasticity vs marginal effects in probit models with logarithmic and dummy independent variables

I am trying to estimate a model with probit in stata of this form: p(y=1|x)=a+bi(ln(xi))+bj(xj)+e where xj are dummy variables and ln(xi) are continuos variables in logarithms. How do i interpret ...
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Model comparison using lower bound from variational approximation

I applied variational approximation for probit regression model and got the lower bound for the log marginal likelihood. When I compare models with different covariates using lower bound, I found that ...