Stata cheat sheet analysis
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This document provides examples of Stata commands for specifying different types of variables and indicators in regression models, as well as changing the base category and interaction terms. It demonstrates how to specify indicator variables, set the base category, treat variables as continuous or create interaction and factorial interaction terms in regressions.
![OPERATOR EXAMPLE
specify rep78 variable to be an indicator variablei. regress price i.rep78specify indicators
ib. set the third category of rep78 to be the base categoryregress price ib(3).rep78specify base indicator
fvset command to change base fvset base frequent rep78 set the base to most frequently occurring category for rep78
c. treat mpg as a continuous variable and
specify an interaction between foreign and mpg
regress price i.foreign#c.mpg i.foreigntreat variable as continuous
# create a squared mpg term to be used in regressionregress price mpg c.mpg#c.mpgspecify interactions
o. set rep78 as an indicator; omit observations with rep78 == 2regress price io(2).rep78omit a variable or indicator
## regress price c.mpg##c.mpg create all possible interactions with mpg (mpg and mpg2
)specify factorial interactions
DESCRIPTION
CATEGORICAL VARIABLES
identify a group to which
an observations belongs
INDICATOR VARIABLES
denote whether
something is true or false
T F
CONTINUOUS VARIABLES
measure something
Declare Data
tsline spot
plot time series of sunspots
xtset id year
declare national longitudinal data to be a panel
generate lag_spot = L1.spot
create a new variable of annual lags of sun spots
tsreport
report time series aspects of a dataset
xtdescribe
report panel aspects of a dataset
xtsum hours
summarize hours worked, decomposing
standard deviation into between and
within components
arima spot, ar(1/2)
estimate an auto-regressive model with 2 lags
xtreg ln_w c.age##c.age ttl_exp, fe vce(robust)
estimate a fixed-effects model with robust standard errors
xtline ln_wage if id <= 22, tlabel(#3)
plot panel data as a line plot
svydescribe
report survey data details
svy: mean age, over(sex)
estimate a population mean for each subpopulation
svy: tabulate sex heartatk
report two-way table with tests of independence
svy, subpop(rural): mean age
estimate a population mean for rural areas
tsset time, yearly
declare sunspot data to be yearly time series
TIME SERIES webuse sunspot, clear PANEL / LONGITUDINAL webuse nlswork, clear
SURVEY DATA webuse nhanes2b, clear
svyset psuid [pweight = finalwgt], strata(stratid)
declare survey design for a dataset
svy: reg zinc c.age##c.age female weight rural
estimate a regression using survey weights
stset studytime, failure(died)
declare survey design for a dataset
SURVIVAL ANALYSIS webuse drugtr, clear
stsum
summarize survival-time data
stcox drug age
estimate a cox proportional hazard model
tscollap
carryforward
tsspell
compact time series into means, sums and end-of-period values
carry non-missing values forward from one obs. to the next
identify spells or runs in time series
USEFUL ADD-INS
pwmean mpg, over(rep78) pveffects mcompare(tukey)
estimate pairwise comparisons of means with equal
variances include multiple comparison adjustment
webuse systolic, clearanova systolic drug
analysis of variance and covariance
ttest mpg, by(foreign)
estimate t test on equality of means for mpg by foreign
tabulate foreign rep78, chi2 exact expected
tabulate foreign and repair record and return chi2
and Fisher’s exact statistic alongside the expected values
prtest foreign == 0.5
one-sample test of proportions
ksmirnov mpg, by(foreign) exact
Kolmogorov-Smirnov equality-of-distributions test
ranksum mpg, by(foreign) exact
equality tests on unmatched data (independent samples)
By declaring data type, you enable Stata to apply data munging and analysis functions specific to certain data types
TIME SERIES OPERATORS
L. lag x t-1
L2. 2-period lag x t-2
F. lead x t+1
F2. 2-period lead x t+2
D. difference x t
-x t-1
D2. difference of difference xt
-xt−1
-(xt−1
-xt−2
)
S. seasonal difference x t
-xt-1
S2. lag-2 (seasonal difference) xt
−xt−2
logit foreign headroom mpg, or
estimate logistic regression and
report odds ratios
regress price mpg weight, robust
estimate ordinary least squares (OLS) model
on mpg weight and foreign, apply robust standard errors
probit foreign turn price, vce(robust)
estimate probit regression with
robust standard errors
rreg price mpg weight, genwt(reg_wt)
estimate robust regression to eliminate outliers
regress price mpg weight if foreign == 0, cluster(rep78)
regress price only on domestic cars, cluster standard errors
bootstrap, reps(100): regress mpg /*
*/ weight gear foreign
estimate regression with bootstrapping
jackknife r(mean), double: sum mpg
jackknife standard error of sample mean
Examples use auto.dta (sysuse auto, clear)
unless otherwise noted
Data Analysis
For more info see Stata’s reference manual (stata.com)
Cheat Sheetwith Stata 14.1
Summarize Data
Statistical Tests
Estimation with Categorical & Factor Variables
Tim Essam (tessam@usaid.gov) • Laura Hughes (lhughes@usaid.gov) inspired by RStudio’s awesome Cheat Sheets (rstudio.com/resources/cheatsheets) geocenter.github.io/StataTraining updated March 2016
CC BY NCDisclaimer: we are not affiliated with Stata. But we like it.
display _b[length] display _se[length]
return coefficient estimate or standard error for mpg
from most recent regression model
margins, dydx(length)
return the estimated marginal effect for mpg
margins, eyex(length)
return the estimated elasticity for price
predict yhat if e(sample)
create predictions for sample on which model was fit
predict double resid, residuals
calculate residuals based on last fit model
test mpg = 0
test linear hypotheses that mpg estimate equals zero
lincom headroom - length
test linear combination of estimates (headroom = length)
regress price headroom length Used in all postestimation examples
more details at http://www.stata.com/manuals14/u25.pdf
pwcorr price mpg weight, star(0.05)
return all pairwise correlation coefficients with sig. levels
correlate mpg price
return correlation or covariance matrix
mean price mpg
estimates of means, including standard errors
proportion rep78 foreign
estimates of proportions, including standard errors for
categories identified in varlist
ratio
estimates of ratio, including standard errors
total price
estimates of totals, including standard errors
ci mpg price, level(99)
compute standard errors and confidence intervals
stem mpg
return stem-and-leaf display of mpg
summarize price mpg, detail
calculate a variety of univariate summary statistics
frequently used commands are
highlighted in yellow
univar price mpg, boxplot
calculate univariate summary, with box-and-whiskers plot
ssc install univar
returns e-class information when post option is used
Type help regress postestimation plots
for additional diagnostic plots
hettest test for heteroskedasticityestat
vif report variance inflation factor
ovtest test for omitted variable bias
dfbeta(length)
calculate measure of influence
rvfplot, yline(0)
plot residuals
against fitted
values
plot all partial-
regression leverage
plots in one graph
avplots
Residuals
Fitted values
price
mpg
price
rep78
price
headroom
price
weight
not appropriate with robust standard errorsDiagnostics2
Postestimation3
Estimate Models1
commands that use a fitted model
stores results as -class
r
e
r
e
r eResults are stored as either -class or -class. See Programming Cheat Sheet
r
e
r
r
r
r
r
r
e
e
e
e
0
100
200 Number of sunspots
19501850 1900
4
2
0
4
2
0
1970 1980 1990
id 1 id 2
id 3 id 4
4
2
0
wage relative to inflation
Blinder-Oaxaca decomposition
ADDITIONAL MODELS
xtline plot
tsline plot
instrumental variablesivregress ivreg2
principal components analysispca
factor analysisfactor
count outcomespoisson • nbreg
censored datatobit
difference-in-differencediff
built-in Stata
command
regression discontinuityrd
dynamic panel estimatorxtabond xtabond2
propensity score matchingpsmatch2
synthetic control analysissynth
oaxaca
user-written
ssc install ivreg2](https://image.slidesharecdn.com/statacheatsheetanalysis-160319031024/85/Stata-cheat-sheet-analysis-1-320.jpg)
Stata cheat sheet analysis
- 1. OPERATOR EXAMPLE specify rep78 variable to be an indicator variablei. regress price i.rep78specify indicators ib. set the third category of rep78 to be the base categoryregress price ib(3).rep78specify base indicator fvset command to change base fvset base frequent rep78 set the base to most frequently occurring category for rep78 c. treat mpg as a continuous variable and specify an interaction between foreign and mpg regress price i.foreign#c.mpg i.foreigntreat variable as continuous # create a squared mpg term to be used in regressionregress price mpg c.mpg#c.mpgspecify interactions o. set rep78 as an indicator; omit observations with rep78 == 2regress price io(2).rep78omit a variable or indicator ## regress price c.mpg##c.mpg create all possible interactions with mpg (mpg and mpg2 )specify factorial interactions DESCRIPTION CATEGORICAL VARIABLES identify a group to which an observations belongs INDICATOR VARIABLES denote whether something is true or false T F CONTINUOUS VARIABLES measure something Declare Data tsline spot plot time series of sunspots xtset id year declare national longitudinal data to be a panel generate lag_spot = L1.spot create a new variable of annual lags of sun spots tsreport report time series aspects of a dataset xtdescribe report panel aspects of a dataset xtsum hours summarize hours worked, decomposing standard deviation into between and within components arima spot, ar(1/2) estimate an auto-regressive model with 2 lags xtreg ln_w c.age##c.age ttl_exp, fe vce(robust) estimate a fixed-effects model with robust standard errors xtline ln_wage if id <= 22, tlabel(#3) plot panel data as a line plot svydescribe report survey data details svy: mean age, over(sex) estimate a population mean for each subpopulation svy: tabulate sex heartatk report two-way table with tests of independence svy, subpop(rural): mean age estimate a population mean for rural areas tsset time, yearly declare sunspot data to be yearly time series TIME SERIES webuse sunspot, clear PANEL / LONGITUDINAL webuse nlswork, clear SURVEY DATA webuse nhanes2b, clear svyset psuid [pweight = finalwgt], strata(stratid) declare survey design for a dataset svy: reg zinc c.age##c.age female weight rural estimate a regression using survey weights stset studytime, failure(died) declare survey design for a dataset SURVIVAL ANALYSIS webuse drugtr, clear stsum summarize survival-time data stcox drug age estimate a cox proportional hazard model tscollap carryforward tsspell compact time series into means, sums and end-of-period values carry non-missing values forward from one obs. to the next identify spells or runs in time series USEFUL ADD-INS pwmean mpg, over(rep78) pveffects mcompare(tukey) estimate pairwise comparisons of means with equal variances include multiple comparison adjustment webuse systolic, clearanova systolic drug analysis of variance and covariance ttest mpg, by(foreign) estimate t test on equality of means for mpg by foreign tabulate foreign rep78, chi2 exact expected tabulate foreign and repair record and return chi2 and Fisher’s exact statistic alongside the expected values prtest foreign == 0.5 one-sample test of proportions ksmirnov mpg, by(foreign) exact Kolmogorov-Smirnov equality-of-distributions test ranksum mpg, by(foreign) exact equality tests on unmatched data (independent samples) By declaring data type, you enable Stata to apply data munging and analysis functions specific to certain data types TIME SERIES OPERATORS L. lag x t-1 L2. 2-period lag x t-2 F. lead x t+1 F2. 2-period lead x t+2 D. difference x t -x t-1 D2. difference of difference xt -xt−1 -(xt−1 -xt−2 ) S. seasonal difference x t -xt-1 S2. lag-2 (seasonal difference) xt −xt−2 logit foreign headroom mpg, or estimate logistic regression and report odds ratios regress price mpg weight, robust estimate ordinary least squares (OLS) model on mpg weight and foreign, apply robust standard errors probit foreign turn price, vce(robust) estimate probit regression with robust standard errors rreg price mpg weight, genwt(reg_wt) estimate robust regression to eliminate outliers regress price mpg weight if foreign == 0, cluster(rep78) regress price only on domestic cars, cluster standard errors bootstrap, reps(100): regress mpg /* */ weight gear foreign estimate regression with bootstrapping jackknife r(mean), double: sum mpg jackknife standard error of sample mean Examples use auto.dta (sysuse auto, clear) unless otherwise noted Data Analysis For more info see Stata’s reference manual (stata.com) Cheat Sheetwith Stata 14.1 Summarize Data Statistical Tests Estimation with Categorical & Factor Variables Tim Essam (tessam@usaid.gov) • Laura Hughes (lhughes@usaid.gov) inspired by RStudio’s awesome Cheat Sheets (rstudio.com/resources/cheatsheets) geocenter.github.io/StataTraining updated March 2016 CC BY NCDisclaimer: we are not affiliated with Stata. But we like it. display _b[length] display _se[length] return coefficient estimate or standard error for mpg from most recent regression model margins, dydx(length) return the estimated marginal effect for mpg margins, eyex(length) return the estimated elasticity for price predict yhat if e(sample) create predictions for sample on which model was fit predict double resid, residuals calculate residuals based on last fit model test mpg = 0 test linear hypotheses that mpg estimate equals zero lincom headroom - length test linear combination of estimates (headroom = length) regress price headroom length Used in all postestimation examples more details at http://www.stata.com/manuals14/u25.pdf pwcorr price mpg weight, star(0.05) return all pairwise correlation coefficients with sig. levels correlate mpg price return correlation or covariance matrix mean price mpg estimates of means, including standard errors proportion rep78 foreign estimates of proportions, including standard errors for categories identified in varlist ratio estimates of ratio, including standard errors total price estimates of totals, including standard errors ci mpg price, level(99) compute standard errors and confidence intervals stem mpg return stem-and-leaf display of mpg summarize price mpg, detail calculate a variety of univariate summary statistics frequently used commands are highlighted in yellow univar price mpg, boxplot calculate univariate summary, with box-and-whiskers plot ssc install univar returns e-class information when post option is used Type help regress postestimation plots for additional diagnostic plots hettest test for heteroskedasticityestat vif report variance inflation factor ovtest test for omitted variable bias dfbeta(length) calculate measure of influence rvfplot, yline(0) plot residuals against fitted values plot all partial- regression leverage plots in one graph avplots Residuals Fitted values price mpg price rep78 price headroom price weight not appropriate with robust standard errorsDiagnostics2 Postestimation3 Estimate Models1 commands that use a fitted model stores results as -class r e r e r eResults are stored as either -class or -class. See Programming Cheat Sheet r e r r r r r r e e e e 0 100 200 Number of sunspots 19501850 1900 4 2 0 4 2 0 1970 1980 1990 id 1 id 2 id 3 id 4 4 2 0 wage relative to inflation Blinder-Oaxaca decomposition ADDITIONAL MODELS xtline plot tsline plot instrumental variablesivregress ivreg2 principal components analysispca factor analysisfactor count outcomespoisson • nbreg censored datatobit difference-in-differencediff built-in Stata command regression discontinuityrd dynamic panel estimatorxtabond xtabond2 propensity score matchingpsmatch2 synthetic control analysissynth oaxaca user-written ssc install ivreg2