Empirical Finance. Introduction to Stata. Introduction to Stata. 1. Using Stata 2. Basic Statistics 3. Advanced Commands. Prof. Dr.

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1 Empirical i Finance Introduction to Stata Universität zu Köln Seminar für ABWL und Unternehmensfinanzen Prof. Dr. Dieter Hess Prof. Dr. Dieter Hess Empirical Finance Introduction to Stata Universität zu Köln Seminar für ABWL und Unternehmensfinanzen Prof. Dr. Dieter Hess 1. Using Stata 2. Basic Statistics 3. Advanced Commands

2 1. Using Stata Main Stata window: command history output window list of variables in data set command window 1. Using Stata Basic commands: Note: open data set: use {filename}.dta You may open the data set with a simple double click from Windows Explorer. Clear all data from memory Get information about commands: clear help {command} Provide more memory for Stata (100MB) set mem 100m current directory: change current directory: Show current directory: { }: Substitute the required names [ ]: Optional addition to the command pwd cd {data_directory} dir

3 1. Using Stata Download the data set capm.dta from Ilias folder Stata Tutorial You may open the data set with a simple double click from Windows Explorer or with the use command. available variables make sure you are in the command window (after reading the data) (bottom right) when typing commands 1. Using Stata How to take a first look at the data set? Describe data in memory: Open data browser: Open data editor: describe browse edit List values of variables: list {varlist} Sort data change the order of variables sort {varlist} order {varlist}

4 Empirical Finance Introduction to Stata Universität zu Köln Seminar für ABWL und Unternehmensfinanzen Prof. Dr. Dieter Hess 1. Using Stata 2. Basic Statistics 3. Advanced Commands 2. Basic Statistics Elementary statistical operations Summary statistics (mean, std): sum {varlist} [, detail] codebook {varlist} All values calculated by a statistical command are saved as scalars. A scalar is a single value that can be used for further calculations Show saved values return list Display a scalar display {scalar name} Show frequency with histogram: Tabulate variable, show frequency: hist {variable} [, bin(number)] tab {variable}

5 2. Basic Statistics How to generate new variables? genarate variable: delete a variable: replace a variable: Examples: gen {varname} = {function} drop {varlist} replace {varname} = {function} 1) Generate a variable named rmrf that is the market excess return gen rmrf = rmarket - rriskfree 2) Generate a variable named rfoodav that is the difference between the return on the food industry portfolio and average market return sum rmarket gen rfoodav = rfood r(mean) 3) Drop the new variables drop rmrf rfoodav 2. Basic Statistics Label for variables You can add a label to a variable to document the content or to describe the variable label variable {variable} {label} gen rmrf = rmarket rriskfree label variable rmrf market excess return

6 2. Basic Statistics Label for values of a variables Beside a label for the variable you can add a label to describe the VALUE of a variable. Take a look at the variable jandum which indicates if the respective month is a January. browse tab jandum Lets define a label for the values of the variable: 1 indicates a January, 0 stands for all other months label define january_values 0 other 1 january label value jandum january_values browse tab jandum 2. Basic Statistics The if -command The option if can be added to various commands. It restricts the command to certain values of a variable. {command} if {variable} > {variable} Alternatively, the comparison can be Smaller < Equal == Not equal!=

7 2. Basic Statistics Examples sum rmarket if jandum == 1 gen UP = 1 if rmarket > 0 replace UP = 0 if UP ==. browse UP rmarket Multiple options can be added with an and-statement & or an or-statement sum rmarket if rmarket > 0 & jandum!= 1 browse rconstruction if rmarket < -5 rmarket > 5 2. Basic Statistics How to store your data file? save {filename}.dta If a file under this name already exists and you want to replace it, use: save {filename}.dta, replace

8 Now, its your turn! Exercise 1 Use the dataset Price.dta which contains monthly stock price information for some US firms Price.dta date permno prc ret shrout Explore the content of price.dta. Have a look at the data labels to learn more about the variables. 2. How many firms are in price.dta and what is the sample period? 3. Does price.dta contain a balanced panel? Exercise 2 1. Produce a histogram of all the returns in the file. Anything unusual? 2. Calculate the variable size = market capitalization of a stock Provide summary statistics to describe the distribution of size as of January Provide summary statistics to describe the return distribution for January 2005.

9 Exercise 3 1. Explore the relation between size and return by producing a scatter plot. 2. Provide the mean and standard deviations of the returns for each month separately (Jan through Dec). Can you see a January effect in the sample? Exercise 4 1. Produce a new data set containing the annual returns for each stock (remember there is a compounding effect!). 2. Provide a graph of the annual return distribution for 2005

10 Empirical Finance Introduction to Stata Universität zu Köln Seminar für ABWL und Unternehmensfinanzen Prof. Dr. Dieter Hess 1. Using Stata 2. Basic Statistics 3. Advanced Commands 3. Advanced Commands You can create a log file that documents your complete command history Start t recording log file log using {filename} Close log file log close You can save your commands in a do file that Stata can execute automatically: Open do file editor doedit dit {filename.do} Execute do file do {filename.do}

11 3. Advanced Commands How to import data that is not in Stata format? If other programs allow you to copy the data, then simply py open Stata, go to the variable editor (edit) and paste the data. Alternative: Use other software (Excel) to save data in.txt or.csv format. There has to be only one observation per line and the data has to be separated by tabs, commas, or other delimiters (for example ; ). The first line contains the variable names. Example: line 1: variable 1; variable 2; variable 3; line 2: 3.31; 1.24; 5.72; You can open the data set using the insheet command insheet {varlist} using {filename} [, options] insheet using Data.csv, delimiter(;) 3. Advanced Commands How to combine to Data sets? The command merge allows you to combine two data sets merge {identifier} using {data set} Find variables that t uniquely identify the observations. Sort data according to the identifiers. If there is no overlapping information then you can simply append the data set: append using {data set}

12 3. Advanced Commands In large data sets you often want to analyze subsamples : specific sample periods (years, months) industry specific data (sic-code) The command bysort executes commands for the subsample of variables that are equal in the given variable list bysort {varlist}: {command} bysort jandum: sum rmarket 3. Advanced Commands The command egen extends the gen command and can be combined with mathematical formulas egen {varname} = {functions} Examples for the functions are Example: mean(variable), sum(variable), min(variable) Variable that stores the average excess return for january and non-january observations: bysort jandum: egen return = mean(rmarket rriskfree)

13 3. Advanced Commands Plotting graphs in Stata Scatter plots, line plots and histograms are a handy way to take a look at the data and get an idea of its distribution Scatter plot: scatter {y-variable} {x-variable} Line plot: line {y-variable} {x-variable} Now, its your turn! Exercise 5 Use the following datasets Price.dta date permno prc ret shrout Index.csv Excel CSV file containing a (non-comprehensive) list of S&P 500 constituents year month day permno gvkey

14 Exercise 5 (contd.) 1a) Read the excel data set index.xls into stata. Hint: try out help insheet 1b) Produce a date variable that is compatible with date in price.dta. Inspect the format of your newly created date variable! 1c) Save your created data in index.dta. Exercise 5 (contd.) 2. Merge the two datasets (index.dta and price.dta) to find out which firms in price.dta are S&P 500 index members. Note that membership can change over time. 3. Explore the newly created variable _ merge. What does it tell you? 4. Create a dummy variable (in price.dta) do describe membership at a given month (1=member, 0=not member)

15 Exercise 6 1. Count the number of S&P firms for each month. 2. Summarize the size of S&P firms and non-s&p firms 3. Summarize the monthly return of S&P firms and non-s&p firms Exercise 7 1. Test whether there is a significant difference between the average monthly returns on S&P 500 firms and non-s&p firms. (Hint: first try out help ttest ) 2. Test whether there is a significant difference between the average size of S&P 500 firms and non-s&p firms.

16 Exercise 8 1. Prepare a data set containing the following 3 variables - date (monthly frequency) - sp_dummy = 1 for S&P500 firms, 0 else - mean_ret = average monthly return of S&P500 firms if sp_dummy=1 average monthly return of non-s&p firms if sp_dummy=0 Delete all other variables Keep only observations that are unique in date - sp_dummy Save the data in average.dta Exercise 8 (contd.) 2. Use average.dta (from the previous exercise) to test whether there is a significant difference between the average monthly returns of S&P 500 firms and non-s&p firms.

17 4. Literature Literature on Stata: Kohler, U., Kreuter, F. (2008): Datenanalyse mit Stata, 3. Auflage, Oldenbourg. (available in English: Data Analysis Using Stata) A very nice online tutorial on Stata: