## CATS IN RATS COINTEGRATION ANALYSIS OF TIME SERIES PDF

##### May 26, 2020 | by admin

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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Allows you to choose between two formulations for specifying restrictions on and. Analysis of the model when r D 2. In the cointegration literature, this problem is often emphasized by noting that it is the space spanned cointegrattion and not itself which is uniquely determined.

Hence a model for analyzing X t must allow for I. Before anything is timw, you are asked to normalize CQ 2 column-wise ; and B and B 1 row-wise as shown in gures 3.

An I 1 Analysis 3. If you add more xed relations it is not strictly necessary to generate the set anew since non-identifying structures will be ruled out anyway.

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The dialog has 6 menu items: Allowing the data to speak freely: In this case we see that until end ofthe test statistic for both forms is larger than one, but overall, the model does quite well in this test. As shown in section 1.

Analhsis often-overlooked aspect of econometric research is ensuring that results are reported accurately. If you don’t set these, CATS will use the largest sample possible given the data analyzed.

Under the assumption that all roots of the characteristic polynomial 1. We begin by testing if all roots are unit roots r D 0 which is clearly rejected. After specifying the rank, you are presented with a dialog displaying the transposed eigenvectors cor- responding to the r largest solutions to the eigenvalue problem given by A. Corresponding to the identi cation scheme for the permanent shocks, we choose to normalize the two rst permanent shocks on b2 and ppp.

Roots of the Companion Matrix will, for any selected value of rcreate a plot of the eigenvalues of the companion matrix which is given by ” A1 Ak AD 3. This model allows for the cointegrating relations to be trend-stationary and have non-zero intercepts.

You select which graphs you wish to see from the Re- cursive Graphics dialog gure 4. Estimate With Non-Identifying Restrictions Estimating non-identifying restrictions will invalidate the calculation of t-values on: Cointegraiton, the rank test and plot of the vats matrix roots still suggest that there are two eeries relations and three common trends in the data. Weak exogeneity is a hypothesis about the rows of when the parameters of interest are the 0 long-run parameters and.

Actually, when testing for the cointegrating rank, it is the latter that 0 0 is being tested for stationarity. All recursively estimated statistics, parameter estimates, and con dence bands.

An example is given in paragraph 5. The loadings O 1 to the rst cointegrating vector R1-form. The time paths of the transformed eigenvalues, O i D log.

Multi-Cointegrating Relations plots the multi-cointegrating relations. The RATS Editor Our interactive RATS Editor environment allows you to quickly implement econometric analysis tasks, and makes it easy to try different model specifications or techniques without having to rerun entire programs.

Domestic and foreign effects on prices in an open economy: Estimation of restricted I. In this case, you can get CATS to check if the restrictions are generically identifying by calculating the rank conditions given by 1.

The manual also includes a technical appendix describing the mathematics of CATS. Here you specify the discretization of the unit interval T and number of repli- cations N for simulation of the rank test statistic for the I. By a single click on the mouse you can test for variable exclusion and stationarity as well as weak exogeneity. In this case, it is a good idea to start with this model speci cation and then test the signi cance of the trends.

The labels will be used in the output le and graph headers, so descriptive labels are a good help to distinguish between different analyses. Settings for the recursive estimation.

From all linear restrictions imposing at least one over-identifying restriction on one vector, we form the set C1 consisting of all design matrices Hi for which the hypothesis Hi: From there you can either accept the identi cation, or go back to the rst dialog to change it. By graphical inspection analysiis key properties of the variables, misspeci cation tests of the estimated residuals and parameter constancy.

## CATS in RATS. Cointegration Analysis of Time Series

rast Hence, H and H impose 1 2 two over-identifying restrictions on each -vector which is why the degrees of freedom is 4 and kn 6 as one might think. The empirical support for u 1t to be stationary has generally been very weak, see inter alia Rogoff Once the rank has been determined, further tests like the stationarity of a known relation, or a given variable, can be performed using the usual 2 -distribution.

The multivariate LM tests for no con- ditional heteroskedasticity are rejected, but Rahbek et al. As an illustration, we will input a relation based on the real fats rate parity b2t dp2t D b1t dpc1t discussed in paragraph 3. The test is performed for each i D 1;: Open the dialog for imposing the zero-restrictions as described above.

### In the STCs, how do I execute CATS procedures in RATS?

The US price index lp2t for the period We normalize on dpc1 and dp2 and get the following output from which it is seen that the hypothesis can be accepted with a p-value of 0: In the alternative formulation, H is a. As mentioned in the beginning of this chapter, the pppt series coointegration I. Rank which will produce the following output: Thus, X t will often contain both I.