Posted on January 11, 2019

CATS IN RATS COINTEGRATION ANALYSIS OF TIME SERIES PDF

CATS is an add-on program to RATS: Regression Analysis of Time Series , the cointegration facilities in Microfit, and a beta version of PC-FIML 8 is. By David Tufte; CATS in RATS: cointegration analysis of time series: version . CATS in RATS: Cointegration Analysis of Time Series. Front Cover. Henrik Hansen, Katarina Juselius. Estima, – Cointegration – 87 pages.

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EconPapers: CATS in RATS: cointegration analysis of time series: version

These options are described below. Actual and tted values top leftstandardized residuals bottom leftautocorrelations top rightand histogram bottom right. The correction factor is based on the ‘basic model’. In the screen output shown below, we report the normalized long-run impact matrix C, Q the contemporaneous impact matrix CQ and the cumulated impact after n periods.

Either as D H ‘, which is the default formulation, or as R 0 D 0: When comparing the plots, note that the scale on the vertical axis may be different, as it is determined by the largest value in each dats. The hypotheses we consider are linear hypotheses about the cointegration relations or the loadings.

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Restrictions on Subsets of Beta imposes restrictions on subsets of as described in section 1.

CATS in RATS: cointegration analysis of time series: version 1.01

The Automated Tests menu. In cointegrztion to include the dummy variables in the model, you need to make a some changes to the set-up le and restart CATS. An I 1 Analysis The lines executing the new model should look like this: The dialog for de ning the restriction design matrix H given by equation 3.

See also the descriptions of R and S1. Further details in paragraph 3.

A Restricted Linear Trend. Hence, except for the inclusion of 1Wt or 12 Wt in DtU ; the weakly exogenous variables are treated almost like the endogenous variables except they are not dependent variables of an equation. Details of the different versions of the -switching algorithm can be found in paragraph B. If and have full rank rJohansen shows that the I. Often, a stationary variable might a priori play an important role in a hypothetical cointegration relation; for instance, an in ation rate.

CATS in RATS – Search

Preferences you do not need to source cats. Other types of dummies such as impulses or shifts, as well as exogenous variables, are not covered. This corresponds to the result in section 3. In each row, r is constant and s2 denoting the number of I. Check Rank Conditions calculates the rank conditions as de ned by equation 1. Preferences opens the User Settings dialog described in paragraph 2.

We shall return to the transformation when we discuss analysis of I. However, we note that a likelihood ratio test for signi cance of C. Sets ana,ysis maximal number of iterations used in the – and -switching algorithms for estimating the I. aeries

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Testing Weak Exogeneity for the Long-Run Parameters We have not discussed hypotheses about the loadings in this section, but nevertheless, we end the section with a brief discussion of restrictions onweak exogeneity and partial systems. R can be used together with one of the options S1 or S2 to specify an I.

Dt can also contain stationary stochastic variables that are weakly exogenous, or that can be excluded from the cointegrating space. When you have speci ed which restrictions to load, the dialog shown in gure 3. Dialog for specifying normalization of the transformation matrix B. To determine the indices. The coef cients are related to those of model 1. The implication of 1ls12t being I.

CATS in RATS. Cointegration Analysis of Time Series

Here we accept the suggested normalization on b1b2and ppp and get the following screen output: The output from the MA procedure is as follows: Formally, these hypotheses are expressed by 0 H1: Set Rank of Pi and indicating the desired rank r. Steps 1 and 2 coingegration be familiar to most RATS users: Allows you to choose between two formulations for specifying restrictions on and.

In our case, nine models are found and reported to the output window. It should, however, be noted that the asymptotic distributions can be rather bad approximations to the nite sample dis- tributions, and should therefore be used with caution.