The China+India_n is an Excel file containing the data.

The Summary statistics is a R file. 
It can be used to get the the mean, SD, Skewness, and Kurtosis. 
And the test of mean, SD, Skewness, Kurtosis and normality.


The Cointegration test is an Eview file.
Under the null hypothesis of no cointegration, the residuals  will be I(1). 
The general approach is to obtain residuals and then test whether residuals are I(1) by performing an auxiliary regression, for each cross-section. 
Pedroni describes various methods of constructing statistics for testing for null hypothesis of no cointegration . 
There are two alternative hypotheses: the homogenous alternative,  for all, and the heterogeneous alternative, for all. 
The Pedroni panel cointegration statistic  is constructed from the residuals. 


The Multiple break and BDS test is an Eview file.
Bai and Perron is a generalization of the Quandt-Andrews test in which we test for equality of the  across multiple regimes. 
The distributions of these test statistics depends on numbers of regressors, and numbers of breaks.
The first four lines in the result table summarize the results for different approaches to determining the number of breaks. 
The Sequential result is obtained by performing tests from 1 to the maximum number until we cannot reject the null; 
the Significant result chooses the largest statistically significant breakpoint. 
The UDmax and WDmax results show the number of breakpoints as determined by application of the unweighted and weighted maximized statistics. 
The maximized statistics both indicate the presence of a single break.
The remaining lines show the individual test statistics (original, scaled, weighted) along with the critical values for the scaled statistics. 
In each case, the statistics far exceed the critical value so that we reject the null of no breaks. 
The last two lines of output show the test results for double maximum statistics. 

The BDS test is a test for time based dependence in a series.
It can be used for testing against a variety of possible deviations from independence including linear dependence, non-linear dependence, or chaos.
The test can be applied to a series of estimated residuals to check whether the residuals are independent and identically distributed (iid). 
To perform the test, we first choose a distance. We then consider a pair of points. 
If the observations of the series truly are iid, then for any pair of points, the probability of the distance between these points being less than or equal to epsilon will be constant.
We can also consider sets consisting of multiple pairs of points. One way we can choose sets of pairs is to move through the consecutive observations of the sample in order. 
The result denote the joint probability of every pair of points in the set satisfying the epsilon condition by the probability .
The BDS test proceeds by noting that under the assumption of independence, this probability will simply be the product of the individual probabilities for each pair.
The value used in calculating should be specified. The meaning of this value varies based on the choice of method. 
The default value of 0.7 provides a good starting point for the default method when testing shorter dimensions. 
The program will calculate the BDS test statistic for all dimensions from 2 to the specified value, using the same value of  or each dimension. 
It may be better to vary  with the correlation dimension to maximize the power of the test.
To request bootstrapped p-values, simply check the Use bootstrap box, then specify the number of repetitions in the field below. 
A greater number of repetitions will provide a more accurate estimate of the p-values, but the procedure will take longer to perform.
When bootstrapped p-values are requested, the program first calculates the test statistic for the data in the order in which it appears in the sample. 
The program then carries out a set of repetitions where for each repetition a set of observations is randomly drawn with replacement from the original data. 
Also note that the set of observations will be of the same size as the original data. For each repetition, The program recalculates the BDS test statistic for the randomly drawn data, 
then compares the statistic to that obtained from the original data. 



The Linear causality test is an Eview file.
There are four different situations for the causality relationships between X and Y in (1): (a) unidirectional causality from  X to  Y , 
(b) unidirectional causality from Y to X, (c) existence of feedback relations between  Y  and  X, and (d) Y  and  X are not rejected to be independent.
To check what is the situation, one may first obtain the residual covariance matrix ? from the full model without imposing any restriction on the parameters, 
and compute the residual covariance matrix for the restricted model with the restriction on the parameters imposed by the null hypothesis. 
Thereafter, one could use the F-test or the likelihood ratio statistic (T-C)(log|?_0 |-log|?|)  check what is the situation .


The Nonlinear Causality is a C file.
To test whether there is any nonlinear causality relationship between two vectors of stationary panel time series, 
X and Y, one has to apply the VAR or VECM model  to identify their linear causal relationships and obtain their corresponding residuals `_(y,t) and `_(x,t). 
Thereafter, one has to apply a nonlinear Granger causality test to the residual series `_(y,t) and `_(x,t). 
By removing linear predictive power with a linear VAR model, any remaining incremental predictivepower of one residual series for another can be considered nonlinear predictive power.
Correlation-integral estimators of the joint probabilities in equation  are used to test the condition in equation in the paper. 
The program could use to assign the kernel equals 1 when two conformable vectors and are within the maximum-norm distance e of each other and 0 otherwise. 
Using the joint probability estimators in equation, then the strict Granger noncausality condition in equation can be tested. 
