04-22-2012 03:40 AM
I have t-values for mean returns and actual values of mean returns fo r20 countries, and have calculated averages of the country returns. Is it possible based on the t-values I have to calculate t-values also for the average returns, or do I have to code it earlier?
04-23-2012 03:30 AM
Just the t-statistic I get from testing whether the returns are statistically different from zero. Using:
proc means mean t - approach. So I'm just wondering if I can take an average of the t-statistics as well, or is it statistically impossible?
04-23-2012 03:48 AM
I think you should firstly test your data (return) to see whether it conform NORMAL distribution, because t is based on NORMAL distribution, and also should use non-parameter method to test it again to make the result is reliable .
04-23-2012 09:22 AM
I've done normality testing using proc univariate normal, and the Shapiro-Wilk values are on 99% of the cases over 0.90. Most are somewhere close to 0.98. I think this is enough to know that it's close to normal, right? Am I able to get the t-statistic averages now somehow?
04-25-2012 01:53 AM
By the way, These return is time serial data ?
Normal Test required these data (i.e. observations) is independent for each other .
So I am not quite sure if it is right to do so. Maybe you need some statistician ,especially for the time serial analysis .
04-25-2012 09:07 AM
Ksharp is trying to get you on the right track here. If this is time series data, getting a whole bunch of t values, say one for each time point, and then averaging them falls into the category of a very bad idea. First, the values are not independent and have some sort of serial correlation. Second, how could you possibly calculate the correct degrees of freedom to come up with a p value or standardized effect size? I think you need to look at the ETS suite, especially PROC PANEL, if this describes your dataset.
Try this for a start--look at PROC MIXED for a repeated measures analysis, with country and time as CLASS variables. Your model should include the interaction between these to get separate estimates by country and time. I don't know what the repeated nature of the time variable is like, but an AR(1) structure probably fits if your data are equally spaced in time. It may be heterogeneous by country, so that is something else you will have to investigate. In any case, the Type III tests are "averaged t-tests" in this context. Specific t values can be obtained with an LSMEANS statement.