********** A DO-FILE SHELL FOR YOUR PROBLEM SETS ********** // A few notes: // The text in green are comments that the program does not read // Write comments after "//" // It is a good idea to...

















********** A DO-FILE SHELL FOR YOUR PROBLEM SETS **********  // A few notes: // The text in green are comments that the program does not read // Write comments after "//"  // It is a good idea to keep your do-file organized by numbering tasks and adding many comments // In "(0) - INI" you only need to fill the information required inside the brackets "[]". Erase the brackets. // Notice that commands show up in blue; text (strings) in red // Do not forget to tell us your name and student number at the end   // (0) - INI  clear all// <----- erase="" everyhting="" in="" memory="" import="" []=""><----- loads="" the="" datafile.="" fill="" the="" []="" cd="" []=""><----- directory="" where="" output="" can="" be="" saved.="" fill="" the="" []="" (1)="" you="" can="" start="" working="" here.="" exit=""><----- stata="" will="" stop="" eading="" here.="" anything="" you="" write="" below="" will="" be="" ignored="" name="" and="" student="" number:="" romina="" renzacci,="">






Answer the problem set questions in the do-file. Test the do-file before submitting it to be sure that it runs well and that we can reproduce your output. Add comments when appropriate (use “//”) and number your answers. 1) generate a new variable that will indicate the Enterprise Value of each firm. Label the variable “enterprise_value”. Please note that only meaningful values should be taken into consideration going further (i.e., no missing values). 2) Now, re-do question 1) using a loop to drop any missing values on the variable created to obtain the Enterprise Value of each firm. 3) Sometimes, manipulating your dataset becomes useful to extract important information. Create a variable with the season to which each episode belongs. 4) We will only analyse the companies with an enterprise value higher than or equal to $500k and in which the entrepreneur is either a Male or a Female. Drop any other possibility. 5) Now you need to aggregate data at the industry level. Pick three variables of your choice and calculate their averages at the industry level using the command collapse. Please make sure that the averages that you are computing are meaningful. 6) This is the last task. You need to start by going back to the dataset that you had before performing task 2). Keep only three variables: the company name, the year, and the deal amount variable. Convert the deal amount information from long format to wide using the command reshape. a. In which year was the average deal amount the highest?
Feb 14, 2023
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