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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41 views

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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331 views

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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267 views

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 ...
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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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61 views

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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184 views

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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1answer
2k views

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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880 views

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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1answer
519 views

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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101 views

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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263 views

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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525 views

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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219 views

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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Example Probit Regression

I try to set up a probit model in R. At first I want to model the typical example of commuters deciding between driving by car or using the train instead. There are the following coefficients: $\...
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cluster robust standard error in R after glm

my regression is the following glm_test <- glm(formula=FormulaWritten,data=Regress_Sample,na.action=na.exclude,family=binomial(probit)) I want to compute ...
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959 views

Linear Probability Model, Probit and Logistic Models gives different significance level for a variable

I am now working with an econometrics project, where the dataset contains lots of binary(dummy) variables. Since the linear probability model (LPM) I constructed by directly regressing independent ...
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1answer
93 views

Mediation Analysis for time varying and invariant DV's

I have time-varying independent variables (IV) ($x_1,x_2,x_3...$) and a time varying dependent variable (DV) ($m_1$). I also have a time invariant dependent variable ($m_2$) that is dichotomous. I ...
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418 views

Binary probit with rare events

I am fitting a binary probit model on a data with rare events in the response variable. I have several questions: 1- How small should the portion of 1's in the binary response variable be to enable ...
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352 views

Logit/Probit: algorithm did not converge and sampling weights

I am running a logit regression in R. I get a warning which signals the missing algorithm convergence. My experience suggests that the problem may be due to the number of dummies in the model and/or ...
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128 views

Discrepancies in logit regression using raw vs weighted probability

I'm trying to analyze some binary data that have been consolidated into probability data over time. The probability data is the dependent variable, pertaining to P(event), and I'd like analyze this as ...
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318 views

glm link functions for multinomial and ordered probit regression?

Here's what I understand, could someone please tell me if I'm wrong, and how? For a categorical variable $Y$, the expected value $\text{E}(Y)=\mu=\sum_{y}i\cdot\text{P}(Y=i)$. Using the descriptions ...
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Regression in SPSS [closed]

I have collected data from 400 students through questionnaire on .5 likert scale about their learning of different skills when they are taught by Masters and M,Phil and PhD qualified teachers. I ...
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277 views

How to calculate LA50 (percentage TBSA at which 50 percent of burn patients will die)?

I have this dataset on burns. https://www.dropbox.com/s/vtnbz6dwk3arztx/finalthesis2.csv?dl=0. These are the relevant variables in my data: ...
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84 views

Fitting Gaussian distribution with indirect observations

I want to estimate the mean and variance of a gaussian random variable $X$. The realizations of $X$, i.e. $x$, are not observable. Instead, $a$ and $b$ are observed, which are related to $X$ via $...
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746 views

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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35 views

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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1answer
446 views

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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2k views

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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326 views

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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212 views

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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83 views

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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243 views

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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48 views

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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1answer
607 views

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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1answer
3k views

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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1answer
3k views

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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1answer
188 views

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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1answer
2k views

Jags Implementation of Multivariate Response Probit Model

I am trying to implement the latent variable interpretation of a probit model with vector response (described on wiki here), but am receiving an error. In this model, we have a matrix $X$, $n \times ...
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771 views

Interpret coefficients from logit/probit models with inverse definition of dependent or independent variable

I have a couple of empirical studies examining the determinants of credit ratings. Here, the dependent variable is a binary variable indicating whether a firm has a credit rating or not ($rating$). ...
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63 views

Link between marginal effects of probit estimation and proportions in sample

Suppose you are estimating a model with a binary dependent variable $Y_i$ and where the explanatory variables are a randomly assigned binary treatment $D_i$ and a vector of covariates. My ...
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1answer
44 views

How to show probability satisfies probit model

This is an old past paper question that I am struggling to understand, so any help or hints would be appreciated... Consider the choice between two options, such as two product brands. Let $U_0$ ...
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1answer
2k views

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 ...
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1answer
17k views

How to choose between logit, probit or linear probability model?

To decide whether to use logit, probit or a linear probability model I compared the marginal effects of the logit/probit models to the coefficients of the variables in the linear probability model. ...

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