计量代码

/*

============================================================= =============

* Program: Econometrics TA - Stata Demo

* Description: Basic Stata code

* Version:

* 1.Sep/2012 by G.M.Zeng

* 2.Sep/2015 by Wanru Xiong

* 3.Sep/2017 by Chen Zihao

*

============================================================= ============= */

/*****************0.Gate keeping code******************************/ clear all/*Remove everything from the memory, refresh.

cap log close/*Close any open log file.

set more off/*Show the result all at once.

**set more on /*Show the result section by section.

cd "C:/课件/17秋计量/Stata_0923"

/*更改当前路径为。。。Specify working directory (cd: change directory)

/*Manually: File --> change working directory

**pwd

/*显示当前路径Displays the path of the current working directory.

log using demo.txt,replace/*打开demo.txt文件，覆盖原来日志

/*****************HELP!******************************************/ help log /*查看命令的详细说明

/**********1.Import & save data*******************************/

/*--Import.1--*/

use wage.dta, clear/*清空内存数据，打开wage.dta的数据Import .dta format (Stata data format)*/

insheet using wage.csv,clear/*加载.csv format*/

import excel using wage.xlsx, first clear /*Import .xlsx format*/

browse/*打开数据看数据*/

edit/*Edit data.

/*--Import.2--*/

/*Or copy paste in Data Editor.

/*Advance: merge / append

/*--Save.1--*/

save wage_1.dta,replace/*Save data, replace the original file. /*--Save.2--*/

/*Or copy paste

/***outfile

/***************2.Desribe the data*********************************/

/*--basic info.1--*/

describe/*每一个变量的含义View what is in the storage

d wage/*View a certain var; d is an abbreviation of "describe"*/

/*--basic info.2--*/

codebook/*View basic info of each var

codebook wage/*View basic info of a certain var

/*--basic info.3--*/

sum wage/*Summary statistics of a variable (Notice:missing value excluded)

sum wage, detail

Percentiles 显示了从1%到99%的分位数的取值。第二列是最小和最大的5个数。第三列从上到下：obs观测值数目、mean平均数、std.dev标准差、variance方差。skewness偏度: 偏度的绝对值越小，表明该数据的正态对称性越好。kurtosis峰度: 峰度值越大表明该数据的正态峰越明显。

return list/*View what are in the storage

scalar wage_avg = r(mean)/*赋值Get mean wage, save it as a scalar

scalar wage_max = r(max)/*Get max wage, save it as a scalar

dis wage_avg/*显示值Display the scalar

dis wage_max

/*? Exercise: Show the sample size and store it as a scalar called "sample_size"

/*--basic info.4--*/

tabstat educ, stat(mean min max)/*Show certain statistics of a var, more flexible than sum

tabstat educ, stat(mean min max) by(urban)/*Show certain statistics of a var by group

help tabstat

/*? Exercise: Show the 25th/50th/75th percentile of wage

sum wage,detail

return list

dis r(p25)

dis r(p50)

dis r(p75)

/****Tabulate****/

tabulate feduc/*Tabulate all the values of a var.

tab feduc, miss/*Including missing value

tab south urban/*Cross tabulation

tab south urban,row/*Show row percentage

tab south urban,chi2/*Chi-square test

tab south urban, summ(wage) means /*by south/urban group, calculate mean wage

table south urban, contents(mean wage) /*by south/urban group, calculate mean wage

table south urban, contents(mean wage median wage) /*by south/urban group, calculate mean wage and median wage*/

/*? Exercise: by married/urban/black, calculate the mean and s.d. of wage

mean：平均数count/n：观测值数目sum：加总

max/min：最大值/最小值range：极差sd：标准差cv：变异系

数semean：平均标准误差skewness：偏度var：方差

kurtosis：峰度median/p50：中位数p#：#%百分位数

table married urban black,contents(mean wage sd wage)

/****Correlation****/

corr educ wage/* 相关系数Correlation coefficient of variables

corr educ wage, cov/*协方差方差Covariance of variables

return list/*View what are in the storage

corr educ meduc feduc/*Correlation of more than 2 var

/******************3.Manage the data**********************************/ /****Have a look at the obs****/

list in 1/10/*View obs 1 to 10

list in 1/10,table/* 前十个，下图只是节选View obs 1 to 10, list in table list wage if wage==wage_max/*?View the obs with max wage

sort hours

list hours in -5/l/*View the 5 obs with the most hours

/*? Exercise: Show the observation with the highest IQ

sum IQ

return list

scalar highest_IQ= r(max)

dis highest_IQ

/****Sort and order****/

order feduc meduc/* 将变量把feduce meduc 放在前边Order the vars

browse

sort wage /*Order the value of a var

browse

/****Generate a scalar****/

scalar pi = 3.1415926/*Generate a scalar

/****Generate var and label****/

/*--generate--*/

gen age_sq = age^2/*在表格中加入新的变量Generate a var

label var age_sq "age square" /*Add label to a var. (Label it!)

label define urbanlabel 1 "Urban" 0 "Rural"/*Label the value of a var. Define first then attach the label*/

label values urban urbanlabel

browse/*Notice the color

gen random1 = runiform()/*Generate a random var from U(0,1)

gen random2 = rnormal(0)/*Generate a random var from N(0,1)

gen wage_k = wage/1000

label var wage_k "wage (1000yuan)"

gen obsID = _n /*Generate ID for each obs

/*Show that correlation coefficient is invariant to the unit of x or y

corr educ wage

corr educ wage_k

/*--egen--*/

/*Compare

gen urban_sum1 = sum(urban)/*累计求和*/

egen urban_sum2 = sum(urban)/*全部之和*/

count if urban==1

egen IQ_rank = rank(IQ) /*Generate rank. Option: unique

**help egen/*"egen" is powerful

bysort educ: egen wage_byeduc = mean(wage)/*Calculate mean wage within each educ group*/

tab wage_byeduc

/****Rename****/

rename random normal /*把random改名为normal*/

rename _all =_17 /*加后缀*/

/****tostring****/

tostring married,replace /*Convert numeric variables to string variables

destring married,replace /*Convert string variables to numeric variables

/****Keep & drop****/

**keep wage hours/*Keep certain variables

drop urban_* /*Drop every var with prefix "urban_"

drop if wage==wage_max /*Drop a certain observation

/*******************************4.Some tests***********************/ /*--ttest--*/

/*Mean-comparison tests

ttest educ == 6 /*One-sample mean-comparison test. Default ci 95% /*H0: (pop) mean of educ is 6.

ttest educ == 6, level(90) /*ci 90%

ttest educ,by(urban)/*Two-group mean-comparison test.

/*H0: mean of urban educ is equal to that of rural. Assumption: homoskedasticity ttest educ,by(urban) unequal

/*H0: mean of urban educ is equal to that of rural. Assumption: heteroskedasticity

ttest feduc = meduc /*Two-var mean-comparision test

/*H0: mean of father's educ is equal to mean of mother's educ.

/***********************5.Graph*************************************/ hist wage /*Histogram*/

hist wage, bin(50)/*Specify num of bins50总共分50组

hist wage, normal/*Add a normal density to the graph

kdensity wage/*Univariate kernel density estimation

kdensity wage,normal

graph twoway scatter wage IQ /*Scatter plot

scatter wage IQ || lfitci wage IQ /*

graph twoway scatter wage IQ || lfitci wage IQ /*Add lfitci to scatter plot graph twoway lfitci wage IQ || scatter wage IQ /*Add scatter plot to lfitci twoway (scatter wage IQ)(lfit wage IQ) /*Add linear prediction lines

twoway (scatter wage IQ)(lfitci wage IQ) /*Add confidential intervals

twoway (scatter wage IQ)(qfit wage IQ) /*Add quadratic prediction lines

/*Manually: Graphics -->

/*Tips: Do it manually then save the code

/***********6.Regression***********************************/

/****Simple regression****/

reg wage educ /*Regress wage(y) on educ(x)

ereturn list/*View what are in the storage. "ereturn" here.

dis e(N)/*Display number of obs

dis e(r2)/*Display R^2

dis e(df_m)/*Display degree of freedom

dis _b[educ] /*Display coefficient of educ

dis _b[_con] /*Display constant term

scalar b0 = _b[_con]

scalar b1 = _b[educ]

/***************The End************************************/

exit, clear

/**************第一次作业*********************************/

scatter GPA ACT, title("Fig.1 Relationship between GPA and ACT") note("Data Source: Sample from Koop and Tobias")

twoway lfit GPA ACT, title("Fig.2 Relationship between GPA and ACT") note("Data Source: Sample from Koop and Tobias")

corr GPA ACT

reg GPA ACT

est store reg1

outreg2[reg1] using table.xls

/*interpretation: ACT增加1分，GPA平均增加0.102

predict GPAhat

predict uhat,r

gen logGPA=log(GPA)

reg logGPA ACT if student>2

est store reg2

outreg2[reg2] using table2.xls

/*interpretation:ACT增加1分，GPA平均增加5.5%

/**************第二次作业*********************************/

clear all

set mem 1g

set more off

cd "C:\课件\17秋计量\"

use HP ,clear

/*/a

tab hp ,m

/*/b

sum floating,detail

return list

scalar x1=r(p75)

/*/c

hist hp ,norm w(1000) freq title("Distribution of house price") note("Data Source: 2015 statistical yearbook")

graph export c.png , replace

/*/d

scatter hp pteacher || lfit hp pteacher, title("House price and primary school teachers") note("Data Source: 2015 statistical yearbook")

/*/e

reg hp pteacher gdp pop floating third

scalar b1=_b[pteacher]

/*/f

reg pteacher gdp pop floating third if hp~=.

predict uhat, res

reg hp uhat

scalar b2=_b[uhat]

dis b1

dis b2