Command Reference : Operator and Function Reference : Cumulative Statistic Functions

Cumulative Statistic Functions
These functions perform basic running or cumulative statistics for a series and may be used as part of a series expression. The functions are split into two types, those that cumulate forwards and those that cumulate backwards. The forwards cumulating functions return the running values of a statistic from the start of the workfile (or optionally a sample) to the current observation. The backwards cumulating functions return the running values from the end of the workfile (or sample) to the current observation.
By default, EViews will use the entire workfile range when computing the statistics. You may provide the optional sample s as a literal (quoted) sample expression or a named sample.
Missing values, NAs, do not propagate through these functions. Thus the cumulative sums of the numbers 1, 3, 4, NA, 5 are 1, 4, 8, 8, 13.

 Name Function Description @cumsum(x[,s]) cumulative sum cumulative sum of the values in X from the start of the workfile/sample. @cumprod(x[,s]) cumulative product cumulative product of the values in X from the start of the workfile/sample (note this function could be subject to numerical overflows). @cummean(x[,s]) cumulative mean mean of the values in X from the start of the workfile/sample to the current observation. @cumstdev(x[,s]) cumulative standard deviation sample standard deviation of the values in X from the start of the workfile/sample to the current observation. Note this calculation involves division by . @cumstdevp(x[,s]) cumulative population standard deviation population standard deviation of the values in X from the start of the workfile/sample to the current observation. Note this calculation involves division by . @cumstdevs(x[,s]) cumulative sample standard deviation sample standard deviation of the values in X from the start of the workfile/sample. Note this performs the same calculation as @cumstdev. @cumvar(x[,s]) cumulative variance population variance of the values in X from the start of the workfile/sample to the current observation. Note this calculation involves division by . @cumvarp(x[,s]) cumulative population variance population variance of the values in X from the start of the workfile/sample to the current observation. Note this performs the same calculation as @cumvar. @cumvars(x[,s]) cumulative sample variance sample variance of the values in X from the start of the workfile/sample to the current observation. Note this calculation involves division by . @cummax(x[,s]) cumulative maximum maximum of the values in X from the start of the workfile/sample to the current observation. @cummin(x[,s]) cumulative minimum minimum of the values in X from the start of the workfile/sample to the current observation. @cumsumsq(x[,s]) cumulative sum-of-squares sum of squares of the values in X from the start of the workfile/sample to the current observation. @cumobs(x[,s]) cumulative nmber of non-NA observations the number of non-missing observations in X from the start of the workfile/sample to the current observation. @cumnas(x[,s]) cumulative number of NA observations the number of missing observations in X from the start of the workfile/sample to the current observation. @cumquantile(x,q[,s]) cumulative quantile the qth quantile in X computed from the start of the workfile/sample to the current observation. Quantiles are computed using the Cleveland definition. @cummedian(x[,s]) cumulative median the median of X computed from the end of the workfile/sample to the current observation. @cumbsum(x[,s]) backwards cumulative sum cumulative sum of the values in X from the end of the workfile/sample. @cumbprod(x[,s]) backwards cumulative product cumulative product of the values in X from the end of the workfile/sample (note this function could be subject to numerical overflows). @cumbmean(x[,s]) backwards cumulative mean mean of the values in X from the end of the workfile/sample to the current observation. @cumbstdev(x[,s]) backwards cumulative standard deviation sample standard deviation of the values in X from the end of the workfile/sample to the current observation. Note this calculation involves division by . @cumbstdevp(x[,s]) backwards cumulative population standard deviation population standard deviation of the values in X from the end of the workfile/sample to the current observation. Note this calculation involves division by . @cumbstdevs(x[,s]) backwards cumulative sample standard deviation sample standard deviation of the values in X from the end of the workfile/sample. Note this performs the same calculation as @cumstdev. @cumbvar(x[,s]) backwards cumulative variance population variance of the values in X from the end of the workfile/sample to the current observation. Note this calculation involves division by @cumbvarp(x[,s]) backwards cumulative population variance population variance of the values in X from the end of the workfile/sample to the current observation. Note this performs the same calculation as @cumvar. @cumbvars(x[,s]) backwards cumulative sample variance sample variance of the values in X from the end of the workfile/sample to the current observation. Note this calculation involves division by . @cumbmax(x[,s]) backwards cumulative maximum maximum of the values in X from the end of the workfile/sample to the current observation. @cumbmin(x[,s]) backwards cumulative minimum minimum of the values in X from the end of the workfile/sample to the current observation. @cumbsumsq(x[,s]) backwards cumulative sum-of-squares sum of squares of the values in X from the start of the workfile/sample to the current observation. @cumbobs(x[,s]) backwards cumulative nmber of non-NA observations the number of non-missing observations in X from the end of the workfile/sample to the current observation. @cumbnas(x[,s]) backwards cumulative nmber of NA observations the number of missing observations in X from the end of the workfile/sample to the current observation. @cumbquantile(x,q[,s]) backwards cumulative quantile the qth quantile in X computed from the end of the workfile/sample to the current observation. Quantiles are computed using the Cleveland definition. @cumbmedian(x[,s]) backwards cumulative median the median of X computed from the end of the workfile/sample to the current observation.