Identify a question of interest, in particular a Y (dependent) variable that you would like to describe and some (independent) variables X that you use to explain Y. It is important that you are able to discuss what economic theory (or common sense) would suggest the relationship between Y and the Xs should be. This will give you a framework to interpret the regression results. Remember that: cross-sectional data consists of different units observed at the same point in time (eg, a sample of workers in 2019); in this case your goal is to explain why Y is different across units time series data is when we track one variable over time (eg, GDP from 1950 to 2019 at the quarterly frequency); here the goal is to explain the fluctuations over time of the variable 2. The following step is to find a suitable dataset that provides a sample for the variables you want to observe. There are lots of open datasets that can be freely downloaded on the internet. Make sure that you quickly identify a dataset, otherwise it is advisable that you change to a different project idea for which you can find data. 3. Once you have the idea and the data, it is time to apply the LRM. Some issues you should investigate and discuss are: Are the variables that you thought could be important statistically significant (at 5%)? Are there variables that you would have liked to include in the regression but could not find data for? Discuss whether you expect these omitted variables to introduce bias in your OLS estimates. Are the estimated effects (betas) of the expected sign? Interpret the estimated effects Are there one or more variables that you would like to exclude from the regression because they are irrelevant? Use the F-test Include at least one type of nonlinearity in the LRM and interpret its effect
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