stata基础培训 正规代理

Stata 是一套提供其使用者数据分析、数据管理以及绘制专业图表的完整及整合性统计软件。它提供许许多多功能，包含线性混合模型、均衡重复反复及多项式普罗比模式。用Stata绘制的统计图形相当精美。
统计功能介绍
Stata使得大量的统计工具用于指尖
标准方法，如
基本表格和总结
案例对照分析
ARIMA
ANOVA 和MANOVA
线性回归
时间序列平滑
广义线性模型（GLM）
聚类分析
对比和比较
功率分析
样本选择
……
高级方法，如
多层模型
生存分析
动态面板数据回归
结构方程建模
二进制，计数和审查结果
ARCH
多重替代法
调查数据
Treatment effects
精确统计
贝叶斯分析
……
Stata的数据管理功能让您控制所有类型的数据。
您可以重组数据，管理变量，并收集各组并重复统计。您可以处理字节，整数，long, float,double和字符串变量（包括BLOB和达到20亿个字符的字符串）。Stata还有一些高级的工具用来管理特殊的数据，如生存/时间数据、时间序列数据、面板/纵向数据、分类数据、多重替代数据和调查数据。


Stata轻松生成出版质量、风格迥异的图形。您可以编写脚本并以可复制的方式生成成百上千个图形，并且可以以EPS或TIF格式输出打印、以PNG格式或SVG格式输出放到网上、或PDF格式输出预览。使用这个图形编辑器可更改图形的任何方面，或添加标题、注释、横线、箭头和文本。

值得信任
技术支持
我们不仅编写统计方法，我们还会进行验证。
您能从Stata estimator rest与其他估计的比较中看到Monte-Carlo模拟的一致性和覆盖率以及我们统计学家们进行的广
泛测试。每一版的Stata软件，我们都通过了各种认证，包括230万行的代码测试，并产生了430万行的结果输出。我们验
证了这430万行代码中的每一个数字和每一段文字。

Nonlinear DSGE models in Stata 15
In Stata 15, we introduced the dsge command for fitting linear DSGE models, which are time-series models used in economics and finance. These models are an alternative to traditional forecasting models. Both attempt to explain aggregate economic phenomena, but DSGE models do this on the basis of models derived from microeconomic theory.
New in Stata 16, the dsgenl command fits nonlinear DSGE models. Most DSGE models are nonlinear, and this means that you no longer need to linearize them by hand. When you enter equations into dsgenl, it linearizes them for you.
After estimating the parameters of your model with dsgenl, you can obtain the transition and policy matrices; determine the model’s steady state; estimate variables’ variances, covariances, and autocovariances implied by the system of equations; and create and graph impulse–response functions.
This is likely to be the favorite feature of macroeconomists and anyone working in a central bank.

Multiple datasets in memory in Stata 16
You can now load multiple datasets into memory. You type
. use people
and people.dta is loaded into memory. Next, you type
. frame create counties
. frame counties: use counties
and you have two datasets in memory. people.dta is in the frame named default, and counties.dta is in the frame named counties. Your current frame is still default. Most Stata commands use the data in the current frame. For example, if you typed
. list
then people.dta will be listed. If you typed
. frame counties: list
then counties.dta will be listed. Or you could make counties the current frame by typing
. frame change counties
and list will now list the counties data.
Navigating frames is easy and so is linking them. Imagine that both datasets have a variable named countycode that identifies counties in the same way. Type
. frlink m:1 countycode, frame(counties)
and each person in the default frame is linked to a county in the counties frame. This means you can now use the frget command to copy variables from the counties frame to the current frame. Or you can use the frval() function to directly access the values of variables in the counties frame. For instance, if we have each individual’s income in the default frame and median county income in the counties frame, we can generate a new variable containing relative income by typing
. generate rel_income = income / frval(counties, median_income)
This is just the beginning. While this example uses only two frames, you can have up to 100 frames in memory at once, and you can have many links among those frames.

http://turntech8843.b2b168.com