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inkl. 16 % USt

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CATS bietet eine Vielzahl von Tools zum Analysieren Ihrer Daten sowie zum Auswählen und Testen eines Kointegrationsmodells. Das Programm ist fast vollständig menü- und dialoggesteuert. Sie führen zunächst ein kurzes RATS-Programm durch, um Ihre Daten zu definieren und CATS zu laden. Dadurch werden der RATS-Menüleiste mehrere CATS-Menüs hinzugefügt. Nun führen Sie Ihre Analyse durch, indem Sie Vorgänge aus diesen Menüs auswählen.

Das CATS 2.0-Paket enthält das CATS-Verfahren auf CD und ein vollständig überarbeitetes 200-seitiges Handbuch, das die Ökonometrie des kointegrierten VAR-Modells und die Interpretation der Ausgabe beschreibt. Alle Funktionen des Programms werden anhand eines Beispiels veranschaulicht. Das Handbuch enthält auch einen technischen Anhang, der die Mathematik von CATS beschreibt. Beispieldaten und Setup-Dateien für die veranschaulichenden Beispiele sind ebenfalls enthalten.

Beachten Sie, dass Sie eine Kopie der RATS-Software benötigen, um CATS verwenden zu können. CATS 2.0 funktioniert mit Version 6.2 oder höher von RATS.

 
 

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.