What are the basic concepts of logit, probit and tobit models. What are the main differences between these models. When will we use each model and why? I need detailed explanation of these models with suitable practical applications.
these three models are used when the regression model has binary response variable(i.e, the dependent variable is dichotomos event.
Logit model follows logistic distribution while probit model follows lognormal distribution.
The tails of logistric distribution are fatter than lognormal distribution.
logistribution distribution is widely applied in analyzing the behaviour of stock returns which tends to have fat tails.
utility theory or rational choice perspecitive on human behaviour provides theoretical basis for probit theory.
for more imformation on these models, you can refer 'Basic Econometrics' by Gujarati.
you can also refer my article titled "Determinants of In-The-Money Expiration of Call option Contracts- An empirical evidence from Call options on Nifty-50 Index"
weblink : http://journals.sagepub.com/doi/abs/10.1177/0972150916660402
These three are among a variety of methodologies to understand the relationships of data as explained in this 19 page essay . You may want to consider your data to determine if they fit these models and what insights, if any, might be gained.
http://rdc.uwo.ca/events/docs/presentation_slides/2010-11/Fernando-Logit2011.pdf
Thank you Dr. Bala Ramanathan Subramanian. I like your essay. It is very helpful. But sir if u have send me the book from where u collected these materials then i will be very thankful to you.
I read this question and wanted to clear some things up ....
Logit models are used for discrete outcome modeling. This can be for binary outcomes (0 and 1) or for three or more outcomes (multinomial logit). The logit model operates under the logit distribution (i.e., Gumbel distribution) and is preferred for large sample sizes.
Probit models are mostly the same, especially in binary form (0 and 1). However, for three or more outcomes (in this context, it's typically ranking or ordering) it operates much differently. It uses a single regression equation, in which inferences from marginal effects can only be made on the "extreme" (upper and lower rankings) with any certainty. I can elaborate if more information is needed.
Tobit models are entirely different. It has nothing to do with binary or discrete outcomes. Tobit models are a form of linear regression. Specifically, if a CONTINUOUS dependent variable needs to be regressed, but is skewed to one direction, the Tobit model is used. The Tobit model allows regression of such a variable while censoring it so that regression of a continuous dependent variable can happen. It allows the analyst to specify a lower (or upper) threshold to censor the regression at while maintaining the linear assumptions needed for linear regression. Refer to my paper for more information.
i read this paper but can u apply this model in rural and urban areas.
@Pravin
Yes, you can apply these models in many scenarios. The key is to ensure your data is prepared properly and that you use the correct model based on the nature of your response variable.
Feel free to message me if you would like to ask questions.
Logit and Probit models are normally used in double hurdle models where they are considered in the first hurdle for eg. adoption models (dichotomos dependent variable) and Tobit is used in the second hurdle. In this, the dependent variable is not binary/dichotomos but "real" values. For eg in adoption of improved maize seed by farmers in a particular location, They may be asked if they are will adopt the improved seed(answers: yes and no, then logit or probit models are used depending on the distribution). This is the first hurdle. If yes then how much will they pay for this seed in a particular amount of money. In this case we use Tobit model with the amount they will pay as dependent variable. This takes care of the second hurdle. You can read more on adoption models
Dear Sami Ullah,
models you mentioned, in econometric called limited dependent models.
Logit and Probit models can be used for modeling the binary variables ( when your dependent variable is binary like Yes or No and your aim is assessing impact of some independent variables on dependent variable)
my teacher suggests use both of these models for modeling binary variable, the researcher after estimation can choose one of these models using the percentage of right prediction (PRP).
Tobit model can be used for modeling the censored data, for example, consider the situation that decision maker wants to hire some labor, he/she offers a quantity for a wage.
there is a threshold for the wage that decision maker tend to hire labor, but labor doesn't want to work. in this situation, data before the threshold are censored.
I hope it could help.
bests...
Logit modelbis a regression model where the dependent variable is categotical, it could be binary commonly coded as (0 or 1) or multinomial. While probit model is a model where the dependent variable can take only two values.
However, both logit and probit models are appropriate when the researcher is attempting to model a norminal dependent variables such as male/female, yes/no, agree/disagree.
I found these slides very useful in answering your question.
http://www.ihdindia.org/sarnet/pdfmodule5/probit%20and%20logit-madesh.pdf
@Bala Ramanathan Subramanian your article is no more online
Please can anyone suggest which performs better between logit and probit when you have several missing data with large sample size? Thank you
Good day!
Can anyone provide a reading on mathematical principles of Tobit regression please?
- Badges
- Science topic