stata：第四章预测、拟合优度和建模应⽤stata学习计量经济学原理
第四章预测、拟合优度和建模
本章代码
考研英语自我介绍* tup
version 11.1
capture log clo
t more off
*open log
log using chap04_food, replace text
*open data
u food, clear
* add obrvation
edit
t obs 41
replace income=20in41
* estimate regression
quietly regress food_exp income
predict yhat
predict ehat, residuals
predict f, stdf
* compute t-critical value
严寒的拼音
scalar define tc = invttail(38,.025)
voice
di "t critical value 97.5 percentile = " tc
gen lb = yhat - tc*f
gen ub = yhat + tc*f
list income lb yhat ub in41
drop in41
* R2
pwcorr food_exp income yhat
* effect of scaling
* create $ income and regress
gen inc_dollar = income*100
reg food_exp income
reg food_exp inc_dollar
log clo
* Chapter 4.3.3 linear-log model
log using chap04_linlog, replace text
*open data
u food, clear
* log of income
gen lincome = ln(income)
* linear-log regression
reg food_exp lincome
predict lyhat
predict lyhat
predict lehat, resid
* slope = beta2/x
summarize income
return list
scalar xbar = r(mean)
lincom lincome/xbar
lincom lincome/10
lincom lincome/20
lincom lincome/30
* fitted value plot
twoway (scatter food_exp income, sort)///
(line lyhat income, sort lwidth(medthick)),///
xtitle(Income) ytitle(Food Expenditure) ylabel(0(100)600)/// title(Linear Log Model)
graph save linlog, replace
* linear relationship
quietly reg food_exp income
predict yhat
predict ehat, resid
* linear and linear-log fitted lines
twoway (scatter food_exp income, sort)///
(line lyhat income, sort lwidth(medthick))///
(line yhat income, sort lpattern(dash) lwidth(medthick)),/// xtitle(Income) ytitle(Food Expenditure) ylabel(0(100)600)/// title(Linear Log Model)
graph save linlog2, replace
* plot linear-log model residuals
盐都教育
twoway (scatter lehat income, sort),///
xtitle(Income) ytitle(Residuals)///
title(Linear Log Model Residuals)
graph save linlog_residual, replace
* analyze residuals from original equation
histogram ehat, percent title(Linear Model Residuals)
graph save olhat_hist, replace
* Jarque-Bera test of error normality
summarize ehat, detail
return list
scalar jb =(r(N)/6)*((r(skewness)^2)+((r(kurtosis)-3)^2)/4) di "Jarque-Bera Statistic = " jb
scalar chic = invchi2tail(2,.05)
di "Chi-square(2) 95th percentile = " chic
scalar pvalue = chi2tail(2,jb)
di "Jarque-Bera p-value = " pvalue
log clo
* Polynomial model Chapter 4.4
*open new log
log using chap04_wheat, replace text
*open data and examine
u wa_wheat, clear
describe
summarize
gen yield= greenough
label variable yield"wheat yield greenough shire"
* plot data
twoway (scatter yield time, sort msymbol(circle)),/// xtitle(Time) ylabel(0(.5)2.5) ytitle(Yield)///
title(Wheat Yield)
graph save wawheat, replace
* regression
reg yield time
predict yhat
predict ehat, residuals
* plot fitted lines and data
twoway (scatter yield time, sort)///
(line yhat time, sort lwidth(medthick)),///
xtitle(Time) ytitle(Yield) ylabel(0(.5)2.5)///
title(Wheat Yield Fitted Linear Model)
graph save wheat_fit, replace检讨的格式
* plot residuals
twoway (scatter ehat time, sort),///
xtitle(Time) ytitle(Residuals) yline(0)///
title(Wheat Linear Model Residuals)
graph save wheat_ehat, replace
rvpplot time, recast(bar) yline(0)
graph save wheat_ehat_bar, replace
* Chapter 4.4.2 Cubic equation for yield
* create scaled cubic variable
generate time0=time/100
list yield time0 in1/5
summarize time0
* cubic regression
reg yield c.time0#c.time0#c.time0
玉石俱焚什么意思
predict yhat3
predict ehat3, residuals
* slopes
margins, dydx(*) at(time=(0.150.300.45))
* plot fitted lines and data
twoway (scatter yield time, sort)///
(line yhat3 time, sort lwidth(medthick)),///
xtitle(Time) ytitle(Yield) ylabel(0(.5)2.5)///
title(Wheat Yield Fitted Cubic Model)
graph save wheat_cubic_fit, replace
* plot residuals
twoway (scatter ehat3 time, sort),///
xtitle(Time) ytitle(Residuals) yline(0)///
title("Residuals Wheat""Cubic Specification") graph save wheat_cube_ehat, replace
* Chapter 4.5 Log-linear Models
* Wheat growth model
gen lyield = ln(yield)
reg lyield time
log clo
* Wage Equation
*open new log file
*open new log file
log using chap04_lwage, replace text
*open cps4_small data
u cps4_small, clear
* summarize and plot
describe
厘米毫米换算summarize
tabulate educ
twoway (scatter wage educ, msize(small)),///
xtitle(Education) ytitle(Wage)///
title(Wage-Education Scatter)
graph save wage_educ, replace
* create log(wage)and plot
gen lwage = ln(wage)
twoway (scatter lwage educ, msize(small)),///
xtitle(Education) ytitle(ln(Wage))///
title(ln(Wage)-Education Scatter)
graph save lwage_educ, replace
* log-linear regression
* add one obrvation
edit
t obs 1001
replace educ=12in1001
reg lwage educ
predict lwagehat
predict ehat, residuals
predict f, stdf
* calculate sigma-hat^2
ereturn list
scalar sig2 = e(rss)/e(df_r)
di "sigma-hat squared = " sig2
* Analyze resdiduals
histogram ehat, percent title(ln(Wage) Model Residuals) graph save lwage_ehat, replace
summarize ehat, detail
scalar jb =(r(N)/6)*((r(skewness)^2)+((r(kurtosis)-3)^2)/4) di "Jarque-Bera Statistic = " jb
scalar chic = invchi2tail(2,.05)
di "Chi-square(2) 95th percentile = " chic
scalar pvalue = chi2tail(2,jb)
di "Jarque-Bera p-value = " pvalue
rvpplot educ, yline(0)
* compute natural and corrected predictor and plot
gen yhatn = exp(lwagehat)
di "correction factor = " exp(sig2/2)
gen yhatc = yhatn*exp(sig2/2)
twoway (scatter wage educ, sort msize(small))///
(line yhatn educ, sort ///
lwidth(medthick) lpattern(dash))///
(line yhatc educ, sort lwidth(medthick) lpattern(solid)) graph save lwage_predict, replace
*list predicted values
list educ yhatn yhatc in1001
summarize wage if educ==12in1/1000
* R^2
* R^2
correlate wage yhatn yhatc
di "r2g = " r(rho)^2
* prediction interval
scalar tc = invttail(998,.025)
gen lb_lwage = lwagehat - tc*f
gen ub_lwage = lwagehat + tc*f
gen lb_wage = exp(lb_lwage)
gen ub_wage = exp(ub_lwage)
*list and plot
list lb_wage ub_wage in1001
twoway (scatter wage educ, sort msize(small))///
(line yhatn educ, sort lwidth(medthick) lpattern(solid))/// (line ub_wage educ, sort lcolor(forest_green) lwidth(medthick)/// lpattern(dash))///
(line lb_wage educ, sort lcolor(forest_green) lwidth(medthick)/// lpattern(dash))
graph save lwage_interval, replace
log clo
* A log-log model example
log using chap04_loglog, replace text
u newbroiler, clear
describe
summarize
gen lq = ln(q)
gen lp = ln(p)
reg lq lp
predict lqhat
scalar sig2 = e(rss)/e(df_r)
gen qhatc = exp(lqhat)*exp(sig2/2)
twoway (scatter q p, sort msize(small) lwidth(medthick)///
lpattern(solid))///
(line qhatc p, sort lwidth(medthick)),///培训流程图
xtitle(Price of Chicken) ytitle(Quantity of Chicken)///
title(Poultry Demand)
correlate q qhatc
di "r2g = " r(rho)^2
log clo
