Traditional granger causality (1969;1972) VS Toda and Yamamoto(1995)

7/23/2019 Traditional granger causality (1969;1972) VS Toda and Yamamoto(1995)

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Traditional granger causality (1969;1972) VS Toda and Yamamoto(1995)

Traditional granger causality

Granger (1969) and Sims (1972) proposed a technique which is used commonly for causational

relationship which is known as granger causality. Their approach is crucially based on the saying

that the past and present may cause the future but the future cannot cause the past1. In

econometrics the most widely used operational definition of causality is the Granger definition of

causality, which is defined as follows: X is a Granger cause of Y (denoted as X-----Y), if

present y can be predicted with better accuracy by using past values of x rather than by not doing

so, other information being identical(2).

Shortcomings of traditional granger causality

According to Guajarati 1995 there are few shortcomings in granger causality like first one is

model specification problem and number of lags, second one drawback of this approach is

spurious regression (non-stationary problem(Huang, Kao et al. 2004).

Toda and Yamamoto (1995)

Toda and Yamamoto (1995) is concerned superior than the traditional granger causality because

of this approach does cure of above shortcomings of traditional granger causality. For testing

Toda Yamamoto we have no need to bind us that our all variables must be stationary at level or

first difference etc. Toda and Yamamoto, granger causality test which is valid irrespective of

whether a series is I(0), I(1) or I(2), non-cointegreted or cointegreted of any uninformed order

(Wolde-Rufael 2005)

Advantage of Toda and Yamamoto (1995)

This approach makes granger causality much easier because of, in this technique researchers

have no need to test cointegration or convert VAR into ECM.

Procedure of Toda and Yamamoto

1) Check unit root to know order of integration between time series

2)

Run var model in level form3) On the base of AIC and SIC select appropriate lags

4) You must sure it that there must not be serial correlation

13Granger (1980).

2Charemza and Deadman (1992).


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5) Now see cointegration with the help of Johnsons test for consistent results. And dont

get confused whatever your results come.

6) Now run var model with suitable lag lengths.

7) Now check granger causality

8) Now see your results on the point of views steps five, cos when we find cointegration

among series, there must be ganger causality also.

Now perform Toda and Yamamoto using Eviews.

Step 1: check sationarity of data

Step 2: chose optimal lags; I found that 6 is optimal lags length.


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Step3: checking for serial correlation, go to view of above resulted window -------residual

tests, autocorrelation, from the blow table you will decide about autocorrelation

According to AIC and HQ

five is optimal lags , so as

usually we use AIC so ill

chose five as optimal lag onthe base of AIC..

Ye can zoom it if not clear


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Step4: check cointegration, through Johansen's Trace Test and Max. Eigenvalue Test, for

toda yamamota, this doesnt matter either variables are cointegreted or not, but we are

checking for the robustness. Quick ------groups statistic ------Johnson cointegration


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Step 5: Now Ill run again VAR at level, and just include one extra lag for all variables.

Like below and ok

Here I found two Cointegrating

equations. If we dont have

Cointegrating equation Toda

Yamamota still work it is not

necessary for toda yamamota

approach that series are must be

cointegreted ,we only check it for

the double check Ill explain itbelow.


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Step 6: Results of step 5

Here I have add one more lag in

exogenous option , as we knowour optimal lags length was 5 and I

have put 5 lags in lags intervals for

endogenous ,,,,

Again in exogenous variable tab

write your all variables add one

more lag, like our suitable lags were

5 but I wrote -6 with all variables

i.e. one more lag.


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Step 7: view----- lag structure--- granger causality and ok

Now last step for causational

relation, go to view of this

resulted window----lag

structure---granger causality

/block exogeneity test


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Step 8:

Step9: we saw about cointegration and now we also can see that there is granger causality

also exist .for double check I estimated cointegration.

Huang, J.-T., A.-P. Kao, et al. (2004). "The granger causality between economic growth and

income inequality in post-reform china." The International Centre for the Study of East Asian

Development (ICSEAD), Kitakyushu, Japan 4: 34-38.

Toda, H. Y. and T. Yamamoto (1995). "Statistical inference in vector autoregressions withpossibly integrated processes." Journal of econometrics 66(1): 225-250.

Wolde-Rufael, Y. (2005). "Energy demand and economic growth: the African experience."

Journal of Policy Modeling 27(8): 891-903.

Results of granger causality and u can see gdp granger Couse to

co and we took this decision on the base of probability value,,

Same co gdp and life granger Couse to fdi. I.e. if gdp life

expectancy and Corbin dioxide increase fdi also increase.

And interpret other results same

Note: here you also can see that software automatically

calculate df 5,, as our suitable lags were 5..


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Traditional granger causality (1969;1972) VS Toda and Yamamoto(1995)

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