I'm looking to calculate rolling window pairwise correlations, something in SAS that replicates the following python function. Please let me know if you need data and more examples ? Proc IML doesn't do the trick either : proc iml;
use work.ret_trans;
read all var{date} into myDates;
read all var _NUM_ into X[colname=NumerNames];
do x=18 to nrow(myDates);
dt=myDates[x];
lagdt = intnx('month',dt,-18,'end');
read all var _NUM_ where(date <= dt & date > lagdt ) into trailing18;
idx = setdif(1:ncol(trailing18), 1);
pc = corr(trailing18);
if x=18 then create work.PairWise from pc ;
else edit work.PairWise ;
append from pc;
end;
quit; pandas.rolling_corr pandas.rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None, center=False, pairwise=None, how=None) Moving sample correlation. Parameters: Returns: arg1 : Series, DataFrame, or ndarray arg2 : Series, DataFrame, or ndarray, optional if not supplied then will default to arg1 and produce pairwise output window : int Size of the moving window. This is the number of observations used for calculating the statistic. min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). freq : string or DateOffset object, optional (default None) Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object. center : boolean, default False Set the labels at the center of the window. how : string, default ‘None’ Method for down- or re-sampling pairwise : bool, default False If False then only matching columns between arg1 and arg2 will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used. y : type depends on inputs DataFrame / DataFrame -> DataFrame (matches on columns) or Panel (pairwise) DataFrame / Series -> Computes result for each column Series / Series -> Series
... View more
