Today I had course of econometrics 2 already the 66% of course is done! almost finishing! I had short seminar of tax and allowance, and I finished the review of papers with apps in the title.
today's course with Martin was about social experiments, as part of treatment effects. The main assumptions, the estimation of the coefficient related to our casual effect, and the realization of an experiment were explained. We saw some examples: Project STAR (to decide the optimal size of classes), also one about discrimination between white and black people in the job market in USA.
The idea behind the assumptions in order to have unbiased estimator with OLS are two: the expected error for those under treatment is the same to the expected error for those w/o treatment, saying that there is any type of common characteristic for one of the two groups. This assumption is similar to the heterogeneity of individuals in the sample. the second assumption corresponds to a casual effect stands no matter what are the individuals that were used in the experiment, and that were currently part of the treatment population. the typical design consists of random assignation for the treatment, constant experiment, without the knowledge of the population. the inclusion of covariates and the relationship of groups (multi level regression) can be considered to improve the qualitiy of the regression. What is important for this analysis is the coefficient for the causal effect, and its covariance. is it possible to do social experiment for business? I don;t think we can, however to understand the preferences of users it is something we could do!
taxes: Don't have to do anything! until I don't finish the 30%, or I'm freelance, or Lily gets income and I finish the 30%. P-income is the form that we have to do!
reading of literature in apps: what is app ecosystem, what is an app, why apps are interesting, users are concerned with privacy, permissions, and privacy, who are developers, what developers do to protect their prviacy, who are these actors? do they know about management? With this elements I have to prepare an introduction to the transaction between app developers and users. where data is part of the transaction. the revenue models used by apps, the user's privacy paradox, concerns, and responsibilities of app developers.
For tomorrow: what I can do is to include three more papers, how to create value in the ecosystem plat, especially the paper of app developer perspective.
Print all the notes and make a nice structure. Start writing also is OK
I left to do afterward the revenue models. Easier for me! I gues ;)
today's course with Martin was about social experiments, as part of treatment effects. The main assumptions, the estimation of the coefficient related to our casual effect, and the realization of an experiment were explained. We saw some examples: Project STAR (to decide the optimal size of classes), also one about discrimination between white and black people in the job market in USA.
The idea behind the assumptions in order to have unbiased estimator with OLS are two: the expected error for those under treatment is the same to the expected error for those w/o treatment, saying that there is any type of common characteristic for one of the two groups. This assumption is similar to the heterogeneity of individuals in the sample. the second assumption corresponds to a casual effect stands no matter what are the individuals that were used in the experiment, and that were currently part of the treatment population. the typical design consists of random assignation for the treatment, constant experiment, without the knowledge of the population. the inclusion of covariates and the relationship of groups (multi level regression) can be considered to improve the qualitiy of the regression. What is important for this analysis is the coefficient for the causal effect, and its covariance. is it possible to do social experiment for business? I don;t think we can, however to understand the preferences of users it is something we could do!
taxes: Don't have to do anything! until I don't finish the 30%, or I'm freelance, or Lily gets income and I finish the 30%. P-income is the form that we have to do!
reading of literature in apps: what is app ecosystem, what is an app, why apps are interesting, users are concerned with privacy, permissions, and privacy, who are developers, what developers do to protect their prviacy, who are these actors? do they know about management? With this elements I have to prepare an introduction to the transaction between app developers and users. where data is part of the transaction. the revenue models used by apps, the user's privacy paradox, concerns, and responsibilities of app developers.
For tomorrow: what I can do is to include three more papers, how to create value in the ecosystem plat, especially the paper of app developer perspective.
Print all the notes and make a nice structure. Start writing also is OK
I left to do afterward the revenue models. Easier for me! I gues ;)
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