The goal of the final (individual) project is to apply the econometric tools dis

The goal of the final (individual) project is to apply the econometric tools discussed in class to an economic or business problem chosen by the student. The topic of the project could be inspired by an issue discussed in a course or based on real events. After having identified a topic of interest, the second step is to gather data that can be used in the empirical investigation. The deliverable for the project is a report that should be max 10 pages long (font 12, spacing 1.5) with the following structure:
Introduction: explanation of the topic of your project, its relevance, the main findings in the literature, and a brief discussion of your results.
Survey of the literature: here you discuss a few articles/papers that have investigated the topic. It is probably a good idea to start with the earlier papers and explain in reasonable detail the technique, data, and results. The discussion of latest papers on the topic should be related to the earlier ones: what are the latest papers adding or doing different compared to the earliest ones? different data and/or econometric technique? are their results different?
Model: provide an explanation of the empirical model that is used in the project. It can be a model suggested by economic theory or an empirical model inspired by the problem at hand.
Data: report the data source, the variables included in the dataset, and the sample size
Empirical Application: in this section you discuss the results of the model estimation and the econometric issues that you addressed in arriving to the final specification. In the final specification you might have dropped some irrelevant variables, played with the functional form, allowed for heteroskedasticity, serial correlation, stochastic trends, endogeneity, etc.
Conclusion: summarize the results of your project and whether they confirm or refute earlier results. The tools learned in class are: linear regression, with one and multiple regressors, hypothesis testing, panel data, nonlinear regression models, binary dependant variables, time series, instrumental variable regression, you have to Use pyhton for the regression, please make sure no plagiarizing at all and don′t make this too advanced.
for python, the tutor must be familiar with matplotlib, ggplot, pandas, statsmodel, numpy. The tools learned in class are linear regression with one or multiple variables, Nonlinear regression, panel data, binary dependent variables, instrumental variable regressions