I am currently struggling with master thesis in which I explain the tipping behaviour in taxi transport and the method you showed in the video fits perfectly to this problem. Thanks a lot for a very clear explanation!
Very interesting presentation Prof. I just completed a survey on consumer tipping behaviors in the tourism and hospitality industry in Cameroon for my Ph.D. thesis dissertation. The first part looks at the determinants that influence people to tip or not tip (dependent variable; binary=tip or not). Here, I have no issues implementing a probability approach. But in the second part, the objective is to see the factors that influence people to tip more (tip size/amount), and the dependent variable is continuous. In this second part, the focus is solely on those who tip. My worries are: 1. After running the OLS, Simple Tobit model, and truncated Tobit, I need to carry out the Heckman selection bias test, and when I do this test: a) By including two-step at the end of Heckman's command, Lamda isn't significant b) When two-step is not included at the end of the Heckman selection command, the model is very significant and good 2. What do I need to do? do I retain the (b) for interpretations by considering lnsigma or what do I do with (a) to have consistent results? I will really appreciate your help at this point Prof
Professor Jonas loans explanations through the use of tobit regression are great. This is a great job full of passion. Professor Jonas thanks a million
Hi Mike, this video has been an absolute godsent and one of the few videos online about the heckman model. Can you perhaps make a followup video briefly explaining the assumptions of the heckman model and how to evaluate if your data meets them in STATA. Additionally, I wanted to know how the heckman model reacts to log transformed data.
Hi! Thank you for the clear explanation. How should I use this model in stata if the dependent variable in the first stage is binary? Since the variable of interest is omitted in the second stage due to collinearity..
Hi Jonas....First of all, thanks for your great work doing these videos...Maybe it is in nother video, but, Which is the formula for the variable "equity"? (just for curiosity to replicate the results)...Thanks!!!
Hey Mike, great video! Thanks so much for detailed explanation. I would like to ask a question about using Heckman selection correction to control for self-selection bias. I have been doing a survey study with participants from social media sites (e.g., Twitter/ Facebook). I reached out to 500 people on social media and only received 200 responses. Because of the low response rate, I would need to correct the selection bias introduced by the low response rate. I tried to use SPSS to do the test. In the first stage of the analysis, a probit regression needs to be used to estimate the likelihood of users not participating in the survey. However, I do not have data for non-participants (as they did not respond to my invitation). Thus, the selection model is of no use because the selection dependent variable is participation status, a binary variable that accounts for participation vs. non-participation. My questions are as follows: 1) Can I use the Heckman selection correction to control for self-selection bias? If so, how do I run the selection model when the data for non-participants are unavailable? 2) Are there any other ways to address self-selection bias in an online survey study such as mine? 3) Does Heckman's correction apply to logistic regression or ordinal logistic regression? I realised its second step model is a linear regression but my dependent variable is a binary one (hence logistic regression). Many thanks for answering my questions. Much appreciated!
Mike thanks for the video do you have the source of the data? Or, failing that, a summary of the meaning of each variable? and the complete data set? (With equity data)
Hi Mike. Thanks for the explicit presentation. If i get the following from STAT what does that mean? selection equation: outcome does not vary; remember: 0 = negative outcome, all other nonmissing values = positive outcome r(2000);
Heartful thanks Mike for this clear presentation. I get more information. I have one question for you on Heckman two stage model, the question is: if the invers mill ratio/ambda/is non significant , does it mean the model (heckman) is not fit to the data?
if the inverse mills ratio is not significant. Hence; the Heckman and OLS are equal. So, you have to use OLS for model predication instead of Heckman approach
I'm using data from Bloomberg database and I have a quqntitative dependent variable. I have tried following all the steps of Heckman as decribed in your video but it has failed to work. What could be the problem?
Hello professor, thank you for your video its very useful. I am running a zero-inflated model but there is endless iterations in the fitting full model, with endless (backedup and non-concave). would you know what may be the main problem?
could you elaborate on the Heckman Model when one wants to compute the Lambda by hand , we already have learned the first step is Probit regression and in theory I know that we could use the results of this regression to compute Lambda and place the Lambda into the second Step which is the Linear regression nonetheless I don't know how to compute the Lambda in the STATA by hand (manually)
Hi, how can I predict the errors of the Tobit Regression? The usual way "predict r, residuals" doesn´t work, or how can I know that is correctly specified
Since Tobit uses MLE, residuals are not an option. You can calculate the equivalent by using “predict yhat” to get the linear prediction of y, then “gen uhat =y - yhat “ to get the error.
@@mikejonaseconometrics1886 hi, my dependant variable Y is sensorized between 0 and 1, i need to predict the errors of the tobit regression, when i tried to calculate " yhat " is it normal that the values obtained of yhat contain values higher than 1 ?? for example yhat = 1,19 ( while the values of y are between 0 < Y < 1 ) ???? thank you in advance
@@mikejonasecon9650 Yes actually my dependent variable is "firm efficiency score" which is bounded between 0 and 1. I followed your steps and i used " tobit y x, ll(0) ul(1) ". then i tried to determine " yhat", but lot of "yhat" values are greater than 1, i didn't find where the mistake! Sir, also should i take the output of "tobit y x, ll(0) ul(1) " as the final results of tobit regression or there is other steps should i do to make results more robust ?? please reply!
Excellent videos from uncle Mike, and viewers are not clicking "Like"! Thank you.
Mate, this is an astonishingly well explained video. Thanks a lot
Great Great Explanations
Thank you for making these tutorials, they have helped me a lot. I hope you can do more and upload videos more often. Greetings from Colombia.
I am currently struggling with master thesis in which I explain the tipping behaviour in taxi transport and the method you showed in the video fits perfectly to this problem. Thanks a lot for a very clear explanation!
good speaker. The data science people need more of this.
Thanks, Jonas for this great presentation
Very interesting presentation Prof. I just completed a survey on consumer tipping behaviors in the tourism and hospitality industry in Cameroon for my Ph.D. thesis dissertation. The first part looks at the determinants that influence people to tip or not tip (dependent variable; binary=tip or not). Here, I have no issues implementing a probability approach. But in the second part, the objective is to see the factors that influence people to tip more (tip size/amount), and the dependent variable is continuous. In this second part, the focus is solely on those who tip. My worries are:
1. After running the OLS, Simple Tobit model, and truncated Tobit, I need to carry out the Heckman selection bias test, and when I do this test:
a) By including two-step at the end of Heckman's command, Lamda isn't significant
b) When two-step is not included at the end of the Heckman selection command, the model is very significant and good
2. What do I need to do? do I retain the (b) for interpretations by considering lnsigma or what do I do with (a) to have consistent results?
I will really appreciate your help at this point Prof
Thanks you for that great presentation; it give me clear sense why we do Tobit . Great presentation.
Professor Jonas loans explanations through the use of tobit regression are great. This is a great job full of passion. Professor Jonas thanks a million
So helpful!! Thank you so much!!!!
Thank you for that fantastic explanation!
I don't think you realized how many econ grad students you've helped
Hi Mike, this video has been an absolute godsent and one of the few videos online about the heckman model. Can you perhaps make a followup video briefly explaining the assumptions of the heckman model and how to evaluate if your data meets them in STATA. Additionally, I wanted to know how the heckman model reacts to log transformed data.
Hi Jonas thanks again for a great video explaining the Tobit model.
Glad it was helpful!
Hi! Thank you for the clear explanation. How should I use this model in stata if the dependent variable in the first stage is binary? Since the variable of interest is omitted in the second stage due to collinearity..
Thank you for great explanation. I wish I can include you in the reference part of my thesis.
Glad it was helpful!
Hi Jonas....First of all, thanks for your great work doing these videos...Maybe it is in nother video, but, Which is the formula for the variable "equity"? (just for curiosity to replicate the results)...Thanks!!!
Hey Mike, great video! Thanks so much for detailed explanation. I would like to ask a question about using Heckman selection correction to control for self-selection bias.
I have been doing a survey study with participants from social media sites (e.g., Twitter/ Facebook). I reached out to 500 people on social media and only received 200 responses. Because of the low response rate, I would need to correct the selection bias introduced by the low response rate.
I tried to use SPSS to do the test. In the first stage of the analysis, a probit regression needs to be used to estimate the likelihood of users not participating in the survey. However, I do not have data for non-participants (as they did not respond to my invitation). Thus, the selection model is of no use because the selection dependent variable is participation status, a binary variable that accounts for participation vs. non-participation.
My questions are as follows:
1) Can I use the Heckman selection correction to control for self-selection bias? If so, how do I run the selection model when the data for non-participants are unavailable?
2) Are there any other ways to address self-selection bias in an online survey study such as mine?
3) Does Heckman's correction apply to logistic regression or ordinal logistic regression? I realised its second step model is a linear regression but my dependent variable is a binary one (hence logistic regression).
Many thanks for answering my questions. Much appreciated!
Mike thanks for the video do you have the source of the data? Or, failing that, a summary of the meaning of each variable? and the complete data set? (With equity data)
that would be awesome!!
Do we have to consider outcome stage results and not selected stage results for the final analysis?
Hi Mike. Thanks for the explicit presentation. If i get the following from STAT what does that mean?
selection equation:
outcome does not vary; remember:
0 = negative outcome,
all other nonmissing values = positive outcome
r(2000);
Can you do tobit for a wage regression when theres missing values
Thanks you for that great tutorial. How should I use the Heckman estimation in panel data?
Heartful thanks Mike for this clear presentation. I get more information. I have one question for you on Heckman two stage model, the question is: if the invers mill ratio/ambda/is non significant , does it mean the model (heckman) is not fit to the data?
if the inverse mills ratio is not significant. Hence; the Heckman and OLS are equal. So, you have to use OLS for model predication instead of Heckman approach
Could you please differentiate between the twostep heckman and maximum likelihood?
i am having trouble with my tobit regression on stata, there status no uncencored observation, could you help me with that problem?thanks
Good explanation on Tobit, would you upload a video on double hurdle model? Thanks
I'm using data from Bloomberg database and I have a quqntitative dependent variable. I have tried following all the steps of Heckman as decribed in your video but it has failed to work. What could be the problem?
Mike Jonas! your presentation is very good and SMART! but, would you mind to share me a link on how to arrange tobit on do file for regressing?
Hello professor, thank you for your video its very useful. I am running a zero-inflated model but there is endless iterations in the fitting full model, with endless (backedup and non-concave). would you know what may be the main problem?
could you elaborate on the Heckman Model when one wants to compute the Lambda by hand , we already have learned the first step is Probit regression and in theory I know that we could use the results of this regression to compute Lambda and place the Lambda into the second Step which is the Linear regression nonetheless I don't know how to compute the Lambda in the STATA by hand (manually)
how we can do marginal effects after heckman?
Hi, how can I predict the errors of the Tobit Regression? The usual way "predict r, residuals" doesn´t work, or how can I know that is correctly specified
Since Tobit uses MLE, residuals are not an option. You can calculate the equivalent by using “predict yhat” to get the linear prediction of y, then “gen uhat =y - yhat “ to get the error.
@@mikejonaseconometrics1886
hi, my dependant variable Y is sensorized between 0 and 1, i need to predict the errors of the tobit regression, when i tried to calculate " yhat " is it normal that the values obtained of yhat contain values higher than 1 ?? for example yhat = 1,19 ( while the values of y are between 0 < Y < 1 ) ???? thank you in advance
@@nourjedda5227 Did you use "tobit y x, ll(0) ul(1)"? that should preclude fitted values above the censoring point.
@@mikejonasecon9650 Yes actually my dependent variable is "firm efficiency score" which is bounded between 0 and 1. I followed your steps and i used " tobit y x, ll(0) ul(1) ". then i tried to determine " yhat", but lot of "yhat" values are greater than 1, i didn't find where the mistake!
Sir, also should i take the output of "tobit y x, ll(0) ul(1) " as the final results of tobit regression or there is other steps should i do to make results more robust ?? please reply!
Great lesson. Thank you. PS.The voice is very low.