Basic commands Flashcards
wildcards
- random string
? - one character
create a new variable
generate [new variable name] = function
DELETE A VARIABLE
drop [variable name]
CREATE A NORMALLY DISTRIBUTED VARIABLE
generate [new variable name] = rnormal()
SHOW DATA
list [variable name]
CONVERT STRING VARIABLE TO NUMERIC VARIABLE
destring [string variable name], replace|generate
DECLARE DATA SET TO BE TIME SERIES
tsset [date variable]
USE A SUBET OF THE DATA
regress … if [variable] [condition]
. indicates a missing observation and has a large value; hence, “if [variable] < .” omits missing variables
& indicates “and”
== indicates “equality”
| indicates “or”
OLS REGRESSION
regress [dependent variable] [regressor 1] [regressor 2] … [regressor N]
OLS REGRESSION WITH HETEROSKEDASTICITY CORRECTION
regress [dependent variable] [regressor 1] [regressor 2] … [regressor N], vce(hc3)
PANEL REGRESSION (GLS when using random effects)
xtreg [dependent variable] [regressor 1] [regressor 2] … [regressor N], [option]
For [option], use RE for random effects, BE for time-specific fixed effects, and FE for cross-sectional fixed effects.
Prediction
based on last run OLS
predict [new variable name]
Forecast Standard Error
based on last run OLS
predict [new variable name], stdb
Residuals
based on last run OLS
predict [new variable name], residuals
Estimated covariance matrix:
based on last run OLS
estat vce
Residual plus fixed effects (total residual)
based on last run Panel Data regression
predict [new variable name], ue
Fixed effects (individual specific residual component) (based on last run Panel Data regression)
predict [new variable name], u
Non-specific residual:
based on last run Panel Data regression
predict [new variable name], e
TEST FOR NORMALITY
sktest [variable name]
Note: The null hypothesis is normality.
PORTMANTEAU (Q) TEST FOR SERIAL CORRELATION
wntestq [variable name], lags(#)
CORRELOGRAM
corrgram [variable name]
BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY
hettest
run this after running a regression
TESTS FOR ENDOGENEITY
estat endogenous (run this after running a regression)
Transformation:
First difference in the variable
D.[variable name]
Transformation:
Variable lagged on period
L.[variable name]
GRAPHING
twoway (scatter [y1 variable] [y2 variable] … [x variable])
plot [y variable] [x variable]
Write all subsequent commands and output to a file
log using [filename]
Write all subsequent commands and output to a text file
log using [filename], text
Suspend logging
log off
Resume loggin
log on
Stop logging and closes the log file
log close
RESTRICT OPERATION TO A SUBSET OF THE DATA
[command] in [starting observation]/[ending observation]
GENERATE CORRELATION MATRIX
correlate [variable name, variable name, …]
Gives number of observations, mean, standard deviation, minimum value, and maximum value of all variables in a list
summary [variable name, variable name, …]
Give summary measures for all variables
summary
Gives a large number of summary measures, including median, skewness, and kurtosis
summary [variable name], detail
Gives the result of arithmetic operations on two or more variables, i.e. +, -, /, *, ^. Also used for logarithmic (log(argument)) and exponential (exp(argument)) operations.
display [arithmetic operation]
Gives Pr(Z < z) for a standard normal variable Z.
display normal(z)
Gives the value z for which Pr(Z < z) equals p, Z~N(0,1)
display invnormal(p)
Gives Pr(T > t) for a t-distributed variable T with n degrees of freedom
display ttail(n,t)
Gives the value t for which Pr(T>t) equals p, for a t-distributed variable T with n degrees of freedom
display invttail(n,p)
Gives Pr(F>f) , for an F-distributed variable F with n1 and n2 degrees of freedom
display Ftail(n1,n2,f)
Gives the value f for which Pr(F > f) equals p, for an F-distributed variable F with n1 and n2 degrees of freedom
display invFtail(n1,n2,p)
Searches help and online databases for information on the command or statement
FINDIT [command]
Provides help on a specific command
HELP [command]
Searches help text for the specified terms
SEARCH [terms]
