國外軟體

12月銷售軟體排行

1. MindManager 視覺化思考繪圖軟體
2. EViews 預測分析計量軟體
3. LISREL 線性結構分析軟體

4.

ATLAS.ti 定性量化分析軟體
5.

EndNote 參考書目軟體

6.

Stata 資料管理統計繪圖軟體

7. See5/C5.0  資料探勘軟體
8. HLM 階層分析軟體
9.

Expert Choice  AHP專家決策分析軟體

10. Grapher 3D科學繪圖軟體

 

 

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CATS   時間序列分析軟體

CATS提供多種工具為您的資料分析和選擇與測試協整模型。節目幾乎完全是功能表和對話方塊驅動。首先,運行一個短的大鼠程式定義您的資料和載入的貓過程。這對RATS功能表列,添加了幾個CATS功能表和您通過從這些功能表選擇操作執行您的分析。


CATS Cointegration Analysis: Overview
CATS (Cointegration Analysis of Time Series) is a set of cointegration analysis procedures written by Jonathan G. Dennis, Katarina Juselius, Søren Johansen and Henrik Hansen of the University of Copenhagen for use with our RATS software. CATS was written .

CATS provides a wide variety of tools for analyzing your data and choosing and testing a cointegration model. The program is almost completely menu- and dialog-driven. You begin by running a short RATS program to define your data and load the CATS procedure. This adds several CATS menus to the RATS menu bar, and you perform your analysis by selecting operations from these menus. CATS will prompt you for any needed input.
 

 

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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