stata软件教程之详细的使用教程 软件促销活动

北京天演融智软件有限公司

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.

Stata是一款完整的、集成的统计软件包，提供您需要的一切数据分析、数据管理和图形。


完整的数据管理功能
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。


使用Mata进行矩阵编程
Mata是一个成熟的编程语言，可编译您所输入的任何字节，并进行优化和准确执行。
尽管您不需要使用Stata进行编程，但是它作为一个快速完成矩阵的编程语言，是Stata功能中不可或缺的一部分。Mata既是一个操作矩阵的互动环境，也是一个完整开发环境，可以生产编译和优化代码。它还包含了一些特殊功能来处理面板数据、执行真实或复制的矩阵运算，提供完整的支持面向对象的编程，并完全兼容Stata。


跨平台兼容
Stata可在Windows，Mac和Linux/Unix电脑上运行，但是license不需要区分电脑系统。也就是说，如果您有一台Mac系统的电脑和一台Windows系统的电脑，您不需要2个license来运行Stata。您可以安装在任意支持的系统中安装Stata软件。Stata数据集、程序以及其他的数据无需翻译就可以跨平台的共享。您还可以从其他的统计软件、电子报表和数据库中轻松而快速的导入数据。


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不管您是需要购买单款软件，还是制定整个实验室的购买方案，都可以提供。

Multiple-group IRT models in Stata
IRT models explore the relationship between a latent (unobserved) trait and items that measure aspects of the trait. This often arises in standardized testing where the trait of interest is ability, such as mathematical ability. A set of items (test questions) is designed, and the responses measure this unobserved trait. Researchers in education, psychology, and health frequently fit IRT models.
Stata’s irt commands fit 1-, 2-, and 3-parameter logistic models. They also fit graded response, nominal response, partial credit, and rating scale models, and any combination of them. And after fitting a model, irtgraph graphs item-characteristic curves, test characteristic curves, item information functions, and test information functions.
New in Stata 16, the irt commands allow comparisons across groups. Take any of the existing irt commands, add a group(varname) option, and fit the corresponding multiple-group model. For instance, type
. irt 2pl item1-item10, group(female)
and fit a two-group 2PL model.
Group-specific means and variances of the latent trait will be estimated. Group-specific difficulty and discrimination parameters can also be estimated for one or more items. With constraints, you can specify exactly which parameters are allowed to vary and which parameters are constrained to be equal across groups.
You can even use likelihood-ratio tests to compare models with and without constraints to perform an IRT model-based test of differential item functioning.

Panel-data ERMs
Extended regression models (ERMs) were a big new feature last release. The ERM commands fit models that account for three common problems that arise in observational data—endogenous covariates, sample selection, and treatment—either alone or in combination.
In Stata 16, we introduce the xteregress, xteintreg, xteprobit, and xteoprobit commands for fitting panel-data ERMs. This means ERMs can now account for the three problems we mentioned above and for within-panel correlation. These new commands fit random-effects linear, interval, probit, and ordered probit regression models. They allow random effects in one or all equations, and they allow random effects to be correlated across equations.
Researchers from all disciplines who work with observational (nonexperimental) data are interested in ERMs and will be excited about the new panel-data versions of these commands. However, different disciplines talk about these models differently.
Above, we referred to the problems ERMs solve as endogenous covariates, sample selection, treatment, and within-panel correlation. While this terminology is common in some disciplines such as economics, other disciplines may use other terms.
• Instead of panel-data and within-panel correlation, researchers may ask for models for multilevel (two-level) data that account for within-group correlation.
• Instead of endogenous covariates, researchers may ask for methods of dealing with unobserved confounding or unmeasured confounding.
• Instead of sample selection, researchers may be concerned about trials with informative dropout, nonignorable nonresponse, or outcomes missing not at random (MNAR).
• Instead of treatment, researchers may ask about methods for causal inference or estimating average treatment effects (ATEs).
The important message is that all disciplines are interested in ERMs, but they often speak different languages.