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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Finding likelihood for an individual observation with cumulative distribution function of a normal distribution [duplicate]

I have to write a probit model, which will describe the effect of an education program on a grade from an exam. The data is the following: GPA: grade point average TUCE: test score PSI: ...
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heavier tails means that it is less sensitive to outlying data for logistic and probit

This is the description of logistic regression and probit regression as generalized linear model from wiki: Both the logistic and normal distributions are ...
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Does the insignificance of a regressor imply insignificance of differenced predicted probabilities with respect to this regressor in probit model?

Suppose I fit a probit model (with two regressors) to data and obtain predicted probabilities: $\widehat{Pr}(Y=1|x_1,x_2)=\Phi(\beta_0+\widehat{\beta}_1x_1+\widehat{\beta}_2x_2)$. For simplicity, ...
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Coeficients from binomial probit model with more than one predictor

A bird observed at time t will have started moult when its time of onset is before t. This occurs with probability given by Prob (started moult before time t) = Φ[(t − μ)/σ], where the function Φ ...
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Recovering the distribution of an independent variable of a probit model

I have the following model: $$ y_i^* = \beta_0 + \beta_1 x_i + \epsilon_i,$$ where I assume that $\epsilon \sim N(0,1)$ and $x \sim N(\mu, \sigma^2)$. I dont observe $y^*$, only $y_i=1 \text{ if } y_i^...
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How to identify small, moderate or large data sample sizes

I want to run a probit or a logit model and I am curious about the choise regarding the data sample size that I have. In a previous answer in a question 'Probit vs Logit' I read that "Probit is ...
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Probit vs logistic regression in ML

Why is the probit model not as popular as logistic regression for binary classification among the machine learning community? It is not or hardly mentioned in serious text books on the topic.
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Calculating marginal effects in probit-model

Is there any literature where the calculation of marginal effects in probit is explained? I don't find anything. The only thing I found is this: How do I interpret a probit model in Stata? Is the ...
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Weighting for modelling probability of selection

I want to use inverse probability weighting in some regressions and to estimate some weighted means from a non-representative sample. I plan to estimate a probit model for probability of selection ...
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Conditional probit to unconditional model?

I have a study in R in which I use public disclosures from companies and stock market data to estimate the probability that a new public disclosure (dividend announcement, etc...) will be published: \...
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Recode ordinal variables to binary one

I have an ordinal variable describes self-assessments of IT/ICT literacy with 0-100 scale (only integers), where 0 – very bad, 100 – very good (sample size: 10000 respondents). I want to investigate ...
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How to calculate the standard error of coefficient from probit regression analysis

folks. I try to get the standard errors of coefficients manually from probit regression analysis in R. These are 0.69012 and 0.03565 from the following R program. ...
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What is the prediction model to predict the probability?

In a paper, Dasgupta, 2019 used Difference-in-Difference approach to see whether anticollusion laws implemented by different countries (staggered implementation) affect firms financial flexibility. ...
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How to deal with unbalanced binary independent variables in logistic regressions

Suppose I want to investigate the impact of some binary independent variables (let’s say: sex and height [tall/short]) on my binary response (alcohol consumption for instance). The distribution of my ...
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Output from glmer with a probit link (lme4 package in R)

I want to estimate the fixed effects and the covariance matrix (or standard deviation of the random effects terms) for a GLMM with a probit link using glmer. Most of the documentation that I can find ...
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Is running an OLS a valid way of running a probit model?

I came across some models where the dependent variable (Y) and the independent variables (Xs) are transformed through a cumulative normal Φ function, then the modeler runs an OLS regression. Is this a ...
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Diagnostics for Biprobit Model

What is the procedure for assessing the fit of a biprobit model? Unfortunately, I couldn't find this material being covered in the standard statistics or econometric textbooks, which is odd, given the ...
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Multivariate logit: evaluate contributions of predictors to estimated probabilities

In a logistic regression with multiple regressors, is there a way to analyze the contribution of the predictors on the dependent variable? (e.g. how would one understand why did the probabilities ...
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Prediction in Tobit II Model vs. Probit x Linear Model

I am estimating a Tobit Type II model with a binary selection (1/0) variable and a continuous outcome variable (0 to infinity, if selection = 1). I then would like to predict the conditional ...
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Forbidden Regression Problem?

I am quite confused about the forbidden regression problem, and unsure if I have one in this case. Stage 1: Probit Model: Prob(Distress) = Set of regressors that predict vulnerability. The predicted ...
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simulation of an item response theory 2PL model [closed]

I would like to simulate a 2PL probit model. I want to set up some arbitrary item parameters $a=(a_1,...,a_J)$, $b=(b_1,...,b_J)$ and abilities $\theta=(\theta_1,...,\theta_I)$ ($J$ number of items ...
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Signs of MarginalEffects and CoefficientEstimates in Multivariate Probit

Could someone explain that the sign of coefficient estimates and their corresponding marginal effects in the Multivariate Probit Model is the same or they could be different? IF they are different, ...
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Sensitivity Testing - Using Probit GLM to Infer Parameters of the Independent Variable

I am an engineer working with some sensitivity data. In this type of experiment, you test a device at a certain stimulus and observe whether or not it fails (binary outcome). For example, you might ...
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Bad controls, Probit, and prediction

I have three related questions: For causal inference, does a variable that is an outcome of the variable of interest also need to be confounded with the outcome variable for it to be a bad control? ...
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understanding R2 in probit

I try to create a model to predict football (socker) results with a performance variable. It doesn't really matter how this performance is calculated since any performance variable is an adequote ...
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Probit Regression and Reverse Causality

In the context of my Master's Thesis, I am using cross-sectional data to investigate the impact of digitalisation and it's various tools on the propensity to adopt various sustainable behaviours for ...
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How to interpret marginal effects when both DV and IVs are in fractional form?

I ran a fractional probit model in which both IV and DV are in fractional form (i.e. proportions). Now I am having difficulty in interpreting the marginal effects (dydx). Suppose, the variable "...
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Probit model with error term

I'm writing a probit model but I don't know if the error term is written correctly. I wrote my model like this: $$\Pr(Y_i=1|X_i)=Φ(x'_i\beta) + ν_i$$ where $v_i$ is the error term. Is this correct?
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95% Confidence interval for extrapolated value from linear regression?

I'm writing a program to estimate the lower limit of detection for a nucleic acid assay. A typical analysis will have 5 concentrations with 10 independent replicates each, the dependent variable being ...
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Heteroscedasticity in categorial/binary data

I fitted a probit model in R using the glm function where my dependent variable is a binary variable and my indepentent variables are also binary and categorial variables. I also fitted a ...
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Chamberlain’s random effect probit model

Why is Chamberlain’s random effect probit model referred to as a random effect model, even though the idea behind such a model is to introduce a correlation between the unobserved heterogeneity term, ...
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Probit with fixed effects

Could anyone elaborate on why fixed effects (or within estimator) will not work in the probit setting? Thanks in advance.
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Is it useful to implement clustered SE in the probit-type models?

For my research, I am implementing a two-stage Heckman procedure. I am working with panel data, so I was wondering if it is common and actually needed to use clustered standard errors for the first ...
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Robustness check for cross-sectional data by merging data sets and creating year dummy variable

I am currently working on the effects of maternal education on child mortality with cross-sectional data. I got data sets for 2008, 2010 and 2014. I am thinking of doing a robustness checks and I ...
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fmm, lcprob(m): regress y x

I am wondering if someone would help me to understand the the stata command posted here: https://www.stata.com/stata-news/news32-4/spotlight-fmm/ the article explains that the command fmm 2, lcprob(...
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How to use gsem when the independent variables are binary?

According to this website, "Binary—probit, logit, complementary log-log". But does the "binary" here mean independent variable or the latent variable (that is determined by the ...
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How do I interpret the results from xtprobit random effects model with robust standard errors in stata?

My probit model has panel data. The dependent variable is a binary outcome that survives = 1 and not survives = 0. I am estimating South Africa's export trade relationships that are import-product ...
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Is it possible (and even correct) to calculate a confidence interval from an interpolated value?

I am using a probit model to calculate the limit of detection of a diagnostic test. For this, in R, I used glm(): ...
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1 vote
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Classification of logit and probit models

Savolainen et al. (2011) has a review of discrete choice models used in accident analysis. The paper offers a classification of these models (see image), but some models (e.g. 'Partial proportional ...
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Logit First-stage and Logit Second-Stage in Experiment with 2 binary Instrumental Variables

We are heavily discussing how to analyze our experiment. There is a binary treatment and a binary dependent variable. Therefore I think I should not use OLS but rather probit/logistic regression. It ...
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Comparing prediction intervals to find probability of outcome

I had a thought I was curious about. I was reading some of the other posts, but they didn't answer the question specifically. Say I have a regression y = X'B ~ N(mu,sigma2) From this regression, I ...
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2 votes
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Probits used in LD_50 is the same as the Wikipedia's probit definition? [duplicate]

During my searches I've come to a strange position; The Probit definition in Wikipedia is simple; $$\operatorname{probit}(p) = \sqrt{2}\,\operatorname{erf}^{-1}(2p-1)$$ Then, I've come to a source ...
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Hazard-ratio of marginal effects?

I am doing a study on manipulated hospital discharges using a similar methodology as this paper. In short, we observe that patients are more likely to be discharged directly after a higher tariff rate ...
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Relationship between dependant and independent variables

I'm completing an assignment that asked me to run some probit regression on some variables, and asked bunch of supporting questions. I am struggling to interpret one of them, it asks to "have a ...
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1 vote
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why is the standard error so high for my probit model [closed]

I have created two probit models, and even though p values are significant and sample spaces are large- 10000 samples in both- standard errors seem to be about 6.27e. What could be the reason for that?...
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Difference of the indices i and j in binary response models

I am currently trying to get the hang of binary probit and logit models as well as multinomial models, but I struggle to see the difference in the indices i and j respectively the difference in their ...
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Distributions other than $N(0, 1)$ for the probit-style regression link function

When we do a probit regression, we use the distribution of a standard normal to convert from the linear combination of the predictors to a probability value. Why stop at the standard normal? Why not ...
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Which test to run ... probit or logistic?

IV's: categorical School, categorical Grade Level, categorical Living Arrangement, categorical Race, categorical Illness, continuous Time. DV: withdrawn status (withdrawn or not withdrawn) I want to ...
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Decomposing a probit/logit regression

In an econometric work, I want to assess the causal effect of n variables on a binary character variable y, while I highly suspect that the relation between one of these regressors, say x (which is ...
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2 votes
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Ways to shrink standard errors in models for discrete dependent variables

Consider a simple Probit model $$ Y_i=1\{X_i\beta+\epsilon_i\geq 0\} $$ where $\epsilon_i$ is standard normal independent of $X_i$. (1) Cardinality of the support of $X_i$ Is it true that (and, if yes,...
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