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Questions tagged [multinomial-probit]

The multinomial probit is an extension of the probit regression. The dependent variable can take on 3 or more unordered values. The modell does not have to fulfill the IIA Assumption.

The multinomial probit regression can be used to model the probability that an observation is a member of a given class out of a set of >2 classes. It can be contrasted with the multinomial logit regression: They are otherwise similar, but use different link functions.

Some references:

https://sites.google.com/site/econometricsacademy/econometrics-models/multinomial-probit-and-logit-models

Long, S. J., & Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata. Texas: Stata Press.

McFadden, D. (1973). Conditional Logit Analysis of Qualitative Choice Behavior. In Frontiers in Econometrics (pp. 105–142). New York: Academic Press.

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When and why should psychologists use ordered probit models rather than the general linear model?

Psychologists often use the general linear model with ordinal independent/dependent variables (i.e. Likert scales to measure 'levels' of a psychological trait. For example, assigning numbers to the labels: "Strongly Disagree, Disagree, Neither,…
Henry Ward
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Predicted Probabilities in Multinomial Probit Model

I am trying to compute predicted probabilities from a multinomial probit model. Researching the internet has given me two ways how this can be done. The first one is basically just applying the probit link for every group independently, as seen…
yrx1702
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Difference between multinomial logit and multinomial probit

Similarly to the question Difference between logit and probit models I am wondering what is the difference between a multinomial logit and a multinomial probit. And when should I apply which of the two algorithms?
Ferdi
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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: library(mlogit) df =…
Cristian Arteaga
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Multinomial probit: can covariance of coefficients be calculated from predicted probabilities?

When producing a GLM (generalized linear model), one usually wants to have an estimate of the variance-covariance matrix of the fitted coefficients, which happens to have a closed form solution with a peculiar structure. If we denote the…
anymous.asker
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Heckman selection model: probit selection & logit outcome

I have a situation where I think I need to use a Heckman selection model to correct for endogeneity. I am interested in studying the effect of firm's market entry mode on its performance. Factors that influence a firm's performance could also be…
Rnovice
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Conditional Logistic Regression in R

As my first question addressing this matter was incomplete and unclear, I made another attempt with an improved outline. I am currently working on a project in which I have a data-set of the following form: $id_A \quad id_M \quad x_1 \quad x_2 \quad…
J. Dekker
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Estimation of Demand and Substitution in Case of Multinomial Logit Model

In MNL model, given the different alternative choices, we estimate the probability of choice across different options. E.g. using the fishing data, we can use the following calculations to estimate the probability across 4 alternatives for each…
Beta
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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 would be the best solution in my case? Is there a…
YM_HEC
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Number of features in multiclass Logistic Regression with categorical predictor

Assume that I want to predict a response with 3 classes. I have two features $X_1$ and $X_2$ where $X_1$ is continuous and $X_2$ is categorical with 5 categories. What would be the number of parameters in the case we are using softmax…
zer0square
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Identification of correlated errors with multinominal probit

Consider the multinational probit model where we observe $Y_i \in \{1, \dots, K + l\}$ with $$ \begin{align*} Y_i = l \Leftrightarrow Z_l&\geq \max(Z_1,\dots Z_{K +1}\} \qquad l \in \{1, \dots, K + 1\} \\ \vec Z = (Z_1,\dots, Z_{K + 1})^\top \mid…
Benjamin Christoffersen
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Obtaining posterior outcome probabilities Multinomial Probit given posterior parameter estimates

I'm trying to compute the individual posterior probabilities from my estimated multinomial probit model. I have obtained the latent utilities as well as the posterior parameters for beta and sigma for each individual, but now I am confused how to…
user270524
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Poisson or Multinomial Logistic Regression (or something else)?

There seems to be a lot of discussion about this on CV but none quite answer my question. I have a variable y which represents number of adverse events occurring within a four-week period. There are three types of adverse event: 'mild', 'moderate'…
llewmills
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What is an intuitive way to understand an ROC curve for a multinomial classifier?

When drawing an ROC curve for a binary classifier, we vary, say the probability threshold of one class vs the other and get the curve. However I'm confused what this means in a multinomial case where the classifier returns probabilities of the…
user1172468
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Is a win-lose model for three Presidential primary candidates appropriate? Multinomial Logit model?

I have a dataset where each record represents a collection of variables for each of the counties in New York. Five variables represent the number of tweets in that geographic area for each candidate in the presidential primary races.…
bwp8nt
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