此菜单上的大多数功能是2.0版本的新增功能，可让您进行调整而不必推出并重新启动CATS过程。与版本1相比，它们还使用户可以更好的控制程序的许多方面。
例如，您可以更改模型（包括确定性变量结构，季节性的包含和滞后结构），控制样本范围（通过设置开始和结束日期，使用样本“虚拟”系列或直接排除）具体观察结果），显示模型摘要或设置以下任意首选项：
这是I（1）菜单
那些使用CATS 1.0的人会很熟悉这些选项。这些构成了CATS 2.0中I（1）分析功能的核心功能，其中包括确定和设置协整等级以及测试关于Pi矩阵元素结构的假设。
这是I（2）菜单
I（2）分析是CATS 2.0中的新功能。菜单操作使您可以设置模型的等级，对Tau和Beta矩阵的测试限制等。
这是图形菜单
如您所见，“图形”菜单提供了对多种图形的快速访问。除了提供比CATS 1.0中更多的图形外，版本2还允许用户自定义图形的外观。“自定义图形”操作在默认设置和您通过“设置”操作访问的自定义设置之间切换：
自动测试菜单
顾名思义，“自动测试”菜单提供对几个重要测试的访问，其中大多数CATS可以在几乎不需要用户输入的情况下执行。这是菜单：
其中大多数都是不言自明的。CATS mining操作是一项主要的新功能。该名称与“数据挖掘”一词的名称有所不同，表示用于分析数据的自动化例程。在这种情况下，CATS mining例程可以根据用户指定的协整等级自动识别并限制协整空间。除了自动过程选择的关系外，您还可以选择提供要使用的用户定义关系。该设置操作让你控制CATS mining过程的许多方面。
然后，“加载模型”操作允许您将先前会话中保存的CATS模型加载到内存中（版本2中新增了保存和重新加载模型的功能）。
Misc菜单
Misc菜单（miscellaneous的缩写）提供了十个更有用的功能：
前两个显示当前模型的短期和MA表示。
下一项是“结构化MA模型”，这是一个重要的新功能，使您可以定义结构模型并绘制所得的脉冲响应函数。
其他操作允许您将生成的数据保存到各种类型的文件（或在电子表格样式的窗口中显示数据），保存模型以供以后的会话中使用，等等。
CATS: Version 2.0!
Version 2.0 is a major update to CATS that introduces significant new econometrics capabilities, a re-designed and expanded user interface, and a new, significantly expandedUser's Manual.
New Econometrics Features
Bartlett small-sample correction of the tests for the cointegrating rank and hypotheses on Beta.
A new “CATSmining” automated model-selection procedure.
Estimation and hypothesis testing of the I(2) model, including testing hypotheses on the multi-cointegrating relations and the I(1) relations among the system variables
Estimation of structural moving average models.
System reduction tests for lag length determination.
Missing observations in data allowed.
Updated recursive estimation routine includes new tests for eigenvalue fluctuation, constancy of the cointegrating space and the log-likelihood function.
Allows for “backwards” recursion for investigating parameter constancy over the beginning of the sample.
For most model specifications, CATS now reports the correct critical values and p-values for the rank test. For other models, you can simulate the critical values using a built-in procedure.
Includes a procedure for estimation and identification of structural moving average models.
New Interface Features
All-new user interface, with separate menus for various categories of operations, including I(1) analysis, I(2) analysis, graphics, and automated tests.
All model settings, including the deterministic terms and lag structure, are menu-controlled, so you can now change the underlying VAR model without quitting and re-starting CATS.
All procedure settings, such as maximum number of iterations and convergence criteria for the switching algorithms, screen output format, and more, can be set via a "Preferences" dialog box.
The estimated model can now be exported as a RATS “MODEL” making it much easier to compute forecasts and impulse responses.
The graphs created by CATS can be customized.
Output can be exported in tex or csv formats.
Restrictions can be saved and re-loaded, making it easier to replicate analyses or continue your work at a later time.
CATS offers the option of running in a true batch mode that does not require user interaction to generate basic output. This allows it to be used in loop.
Other Features
These features carry over from Version 1.0:
Batch” tests for long-run exclusion, weak exogeneity, and stationarity on all model variables (now available from the cats menu). Also includes a test for unit vectors in alpha, which corresponds to testing if the cumulated disturbances of any of the variables do not enter the common trends.
Support for partial systems, models with structural breaks, and various forms of dummy variables.
Multivariate and univariate tests of the estimated residuals.
Recursive estimation for assessing constancy of the estimated model parameters, including tests for constancy of the estimated eigenvalues, the cointegrating space, the log-likelihood function, the parameters of an identified system, and the adequacy of one-step-ahead predictions.
Options for testing hypothesis on the long-run relations in Beta as well as on the adjustment coefficients in Alpha.
Choice of normalization for each cointegrating vector (CATS 2 simplifies this by suggesting default choices).
Estimation of the parameters of the moving average model, e.g. the long-run impact matrix C and the loadings to the common trends (with asymptotic t-values).
A large variety of preset graphics illustrating various key aspects of the estimated model.
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