Operator / Function | Description |
+ | add |
– | subtract |
* | multiply |
/ | divide |
^ | raise to the power |
< | less than |
<= | less than or equal to |
<> | not equal to |
= | equal to |
> | greater than |
>= | greater than or equal to |
@abs(x), abs(x) | absolute value |
@acos(x) | arc cosine (real results in radians) |
@after(arg1) | Creates a dummy variable equal to 1 if the observation is after or on the date given by arg1. arg1 should be enclosed in quotes. |
and | logical and |
@asin(x) | arc sine (real results in radians) |
@atan(x) | arc tangent (results in radians) |
@before(arg1) | Creates a dummy variable equal to 1 if the observation is before the date given by arg1. arg1 should be enclosed in quotes. |
@beta(a,b) | beta integral |
@betainc(x,a,b[,n]) | incomplete beta integral |
@betaincder(x,a,b,s) | derivative of the incomplete beta integral |
@betaincinv(p,a,b) | inverse of the incomplete beta integral |
@betalog(a,b) | natural logarithm of the beta integral |
@between(x, val1, val2) | Creates a dummy variable equal to 1 for observations where the series is greater than or equal to val1 and less than or equal to val2. |
@binom(n,x) | binomial coefficient |
@binomlog(n,x) | natural logarithm of the binomial coefficient |
@bounds(x,y,z) | returns x if x is between y and z, and the boundary values y and z otherwise |
@bridge(x) | returns a copy of series x with NAs replaced by the nearest preceding non-NA value. This function is panel aware. |
@cbeta(x,a,b[,n]) | beta cumulative distribution (CDF) |
@cbvnorm(x, y, r) | bivariate cumulative normal distribution (CDF) |
@cbinom(x,n,p) | binomial cumulative distribution (CDF) |
@cchisq(x,v[,n]) | chi-square cumulative distribution (CDF) |
@ceiling(x[,n]) | smallest integer not less than |
@cellid | element |
@cexp(x,m[,n]) | exponential cumulative distribution (CDF) |
@cextreme(x[,n]) | extreme value cumulative distribution (CDF) |
@cfdist(x,v1,v2[,n]) | F-distribution cumulative distribution (CDF) |
@cgamma(x,b,r[,n]) | gamma cumulative distribution (CDF) |
@cged(x,r[,n]) | generalized error cumulative distribution (CDF) |
@chisq(x,v[,n]) | chi-square p-value |
@claplace(x[,n]) | Laplace cumulative distribution (CDF) |
@clogistic(x[,n]) | logistic cumulative distribution (CDF) |
@cloglog(x) | complementary log-log function |
@clognorm(x,m,s[,n]) | log normal cumulative distribution (CDF) |
@cnegbin(x,n,p) | negative binomial cumulative distribution (CDF) |
@cnorm(x[,n]) | normal cumulative distribution (CDF) |
@columns(g) | number of columns. |
@cor(x,y[,s]) | correlation |
@cos(x) | cosine (argument in radians) |
@cov(x,y[,s]) | population covariance |
@covp(x,y[,s]) | population covariance |
@covs(x,y[,s]) | sample covariance |
@cpareto(x,a,k[,n]) | Pareto cumulative distribution (CDF) |
@cpoisson(x,m) | Poisson cumulative distribution (CDF) |
@crossid | observations in range |
@ctdist(x,v) | Student’s t-distribution cumulative distribution (CDF) |
@cumbmax(x[,s]) | backwards cumulative maximum |
@cumbmean(x[,s]) | backwards cumulative mean |
@cumbmin(x[,s]) | backwards cumulative minimum |
@cumbnas(x[,s]) | backwards cumulative nmber of NA observations |
@cumbobs(x[,s]) | backwards cumulative nmber of non-NA observations |
@cumbprod(x[,s]) | backwards cumulative product |
@cumbstdev(x[,s]) | backwards cumulative sample standard deviation |
@cumbstdevp(x[,s]) | backwards cumulative population standard deviation |
@cumbstdevs(x[,s]) | backwards cumulative sample standard deviation |
@cumbsum(x[,s]) | backwards cumulative sum |
@cumbsumsq(x[,s]) | backwards cumulative sum-of-squares |
@cumbvar(x[,s]) | backwards cumulative population variance |
@cumbvarp(x[,s]) | backwards cumulative population variance |
@cumbvars(x[,s]) | backwards cumulative sample variance |
@cumdn(x,d[,y,s]) | cumulative negative (below threshold) changes |
@cumdp(x,d[,y,s]) | cumulative positive (above threshold) changes |
@cumdz(x,d[,y,s]) | cumulative zero (at threshold) changes |
@cummax(x[,s]) | cumulative maximum |
@cummean(x[,s]) | cumulative mean |
@cummin(x[,s]) | cumulative minimum |
@cumnas(x[,s]) | cumulative nmber of NA observations |
@cumobs(x[,s]) | cumulative nmber of non-NA observations |
@cumprod(x[,s]) | cumulative product |
@cumstdev(x[,s]) | cumulative sample standard deviation |
@cumstdevp(x[,s]) | cumulative population standard deviation |
@cumstdevs(x[,s]) | cumulative sample standard deviation |
@cumsum(x[,s]) | cumulative sum |
@cumsumsq(x[,s]) | cumulative sum-of-squares |
@cumvar(x[,s]) | cumulative population variance |
@cumvarp(x[,s]) | cumulative population variance |
@cumvars(x[,s]) | cumulative sample variance |
@cunif(x,a,b) | uniform cumulative distribution (CDF) |
@cweib(x,m,a[,n]) | Weibull cumulative distribution (CDF) |
d(x) | first difference |
d(x,n) | n-th order difference |
d(x,n,s) | n-th order difference with a seasonal difference at  |
@date | element |
@dateadd(d, offset[,u]) | calculates date number offsets |
@dateceil(d1, u[, step] | calculates the last possible date number for a date with a given time unit |
@datediff(d1,d2[,u]) | difference between two dates |
@datefloor(d1, u[, step]) | calculates a date floor |
@dateceil(d1, u[, step] | calculates the next date number for a date with a given time unit |
@datepart(d1, u) | calculates the date part of a number |
@datestr(d[, fmt]) | converts date to string |
@dateval(str1[, fmt]) | converts string to date |
@day | day of month |
@dbeta(x,a,b) | beta density function |
@dbinom(x,n,p) | binomial density function |
@dbname | returns a string containing the current default database name |
@dbvnorm(x,y,r) | bivariate normal density function |
@dchisq(x,v) | chi-square density function |
@dcumdn(x,d[,y,s]) | addition to cumulative negative (below threshold) changes |
@dcumdp(x,d[,y,s]) | addition to cumulative positive (above threshold) changes |
@dcumdz(x,d[,y,s]) | addition cumulative zero (at threshold) changes |
@demean(x[, s]) | returns a copy of series x translated to have a mean of zero |
@demeanby(x, g[, s]) | returns a copy of series x translated to have a mean of zero within each group of observations defined by series g. |
@detrend | returns the residuals of an OLS regression on series x versus an implicit time trend. This function is panel aware. |
@dexp(x,m) | exponential density function |
@dextreme(x) | extreme value density function |
@dfdist(x,v1,v2) | F-distribution density function |
@dgamma(x,b,r) | gamma density function |
@dged(x,r) | generalized error density function |
@digamma(x), @psi(x) | first derivative of the log gamma function |
@diwish([v,]s) | Inverse Wishart density with sym covariance |
@diwishc([v,]s) | Inverse Wishart density with sym Choesky of covariance |
@diwishi([v,]s) | Inverse Wishart density with sym inverse covariance |
@diwishic([v,]s) | Inverse Wishart density with sym Cholesky of inverse covariance |
@dlaplace(xb) | Laplace density function |
dlog(x) | first difference of the logarithm |
dlog(x,n) | n-th order difference of the logarithm |
dlog(x,n,s) | n-th order difference of the logarithm with a seasonal difference at  |
@dlogistic(x) | logistic density function |
@dlognorm(x,m,s) | log normal density function |
@dmvnorm([v,]s) | multivariate normal density with sym covariance |
@dmvnormc([v,]s) | multivariate normal density with sym Choesky of covariance |
@dmvnormi([v,]s)i | multivariate normal density with sym inverse covariance |
@dmvnormic([v,]s) | multivariate normal density with sym Cholesky of inverse covariance |
@dnegbin(x,n,p) | negative binomial density function |
@dnorm(x) | normal density function |
@dpareto(x,a,k) | Pareto density function |
@dpoisson(x,m) | Poisson density function |
@dtdist(x,v) | Student’s t-distribution density function |
@dtoo(str) | converts string to observation number |
@dunif(x,a,b) | uniform density function |
@dupselem(x1[, x2,..., s]) | duplicates enumeration |
@dupsid(x1[, x2,..., s]) | duplicates identifiers |
@dupsobs(x1[, x2,..., s]) | duplicates observations |
@during(arg1) | Creates a dummy variable equal to 1 if the observation lies between the dates given by the date pair contained in arg1, and 0 otherwise. arg1 should be given in quotes. |
@dweib(x,m,a) | Weibull density function |
@dwish([v,]s) | Wishart density with sym covariance |
@dwishc([v,]s) | Wishart density with sym Choesky of covariance |
@dwishi([v,]s) | Wishart density with sym inverse covariance |
@dwishic([v,]s) | Wishart density with sym Cholesky of inverse covariance |
@elem(x,"v") | element |
@enddate | observations in range |
@env(str) | returns a string containing the value of the Windows environment variable str. |
@eqna(str1, str2) | test for equality of strings |
@eqna(x,y) | equal to |
@erf(x) | error function |
@erfc(x) | complementary error function |
@event(arg1[, basis]) | Proportion of a one-off event that lies in each observation. |
@exp(x), exp(x) | exponential,  |
@expm1(x) | for x near zero,  |
@fact(x) | factorial,  |
@factlog(x) | natural logarithm of the factorial,  |
@fdist(x,v1,v2) | F-distribution p-value |
@fileexist(str) | returns a 0 or 1 depending on whether the filename specified by str exists on disk. |
@first(x) | returns the value of the first non-missing value in the series for the current sample. |
@firstsby(arg1, arg2[, s]) | First non-missing value in arg1 for each arg2 group. |
@floor(x[,n]) | largest integer not greater than. |
@folderexist(str) | returns a binary scalar, equal to 1 if the folder specified by str exists on disk, and 0 otherwise. |
@fracdiff | fractional difference operator. |
@fv(r,n,x[,fv,t]) | future value. |
@gamma(x) | (complete) gamma function |
@gammader(x) | first derivative of the gamma function |
@gammainc(x,a[,n]) | incomplete gamma function |
@gammaincder(x,a,n) | derivative of the incomplete gamma function |
@gammaincinv(p,a) | inverse of the incomplete gamma function |
@gammalog(x) | logarithm of the gamma function |
@getnextname(str) | returns a string containing the next available variable name in the workfile, starting with str. e.g. entering “result” will return “result01” unless there is already a RESULT01, in which case it will return “result02”. |
@gmean(x[,s]) | geometric mean |
@groupid(arg1[, arg2, ..., argn, s]) | group index for the groups defined by arg1 to argn. |
@hmean(x[.s]) | harmonic mean |
@holiday(h[, b][, flag...]) | Proportion of an annual event that lies in each observation. |
@holidayset(h[, b][, flag...]) | Proportion of annual events that lies in each observation. |
@hour | returns the hour of each observation as an integer. |
@hourf | returns the hour of each observation as a floating point number. |
@iff(s,x,y) | recode by condition |
@ifirst(x) | returns the workfile index of the first non-missing value in the series for the current sample. |
@ilast(x) | returns the workfile index of the last non-missing value in the series for the current sample. |
@imax(x) | returns the workfile index of the maximum value in the series for the current sample. |
@imin(x) | returns the workfile index of the minimum value in the series for the current sample. |
@inlist(x, “list”) | Creates a dummy variable equal to 1 for observations where the series is equal to one of the values specified in list, and 0 otherwise. list should be a quoted, space delimited list of values. This function works on both numerical and alpha series. |
@inner(x,y[,s]) | inner product |
@insert(str1, str2, n]) | string insertion |
@instr(str1, str2[, n]) | string location |
@intercept(x[,s]) | intercept of OLS regression versus implicit time trend |
@inv(x) | reciprocal,  |
@isempty(str) | tests for blank strings |
@isna(x) | equal to NA |
@ispanel | returns indicator for whether the workfile is panel structured. |
@isperiod(x) | period dummy |
@kurt(x[,s]) | kurtosis |
@kurtsby(arg1, arg2[,arg3, arg4..., argn, s]) | kurtosis of arg1 observations for each arg2 to argn defined group. |
@lag(x[, n]) | n-th order lag (equivalent to “X(‑4”) for the series X) |
@last(x) | returns the value of the last non-missing value in the series for the current sample. |
@lastsby(arg1, arg2[, s]) | Last non-missing value in arg1 for each arg2 group. |
@left(str, n) | returns the left part of a string |
@len(str) | length of a string |
@length(str) | length of a string |
@log(x), log(x) | natural logarithm,  |
@log10(x) | base-10 logarithm,  |
@log1mexp(x) | for negative x near zero,  |
@log1p(x) | for x near zero,  |
@log2pi |  |
@logcnorm | @log(@cnorm(x)) |
@logit(x) | logistic transform |
@logx(x,b) | base-b logarithm,  |
@lower(str) | lowercases a string |
@ltrim(str) | removes spaces from a string |
@mae(x, y) | the mean of the absolute value of the difference between X and Y. |
@makedate(arg1[,arg2[,arg3]], fmt]) | converts number to date |
@map(x[, mapname]) | mapped value |
@mape(x,y) | 100 multiplied by the mean of the absolute difference between X and Y, divided by Y. |
@mav(x,n) | n-period backward moving average. NAs are not propagated. |
@mavc(x,n) | n-period centered moving average. NAs are not propagated. |
@max(x[,s]) | maximum |
@maxes(x,n) | n-maximum values, returned largest to smallest in a vector object. |
@maxsby((arg1, arg2[, arg3, arg4..., argn, s) | maximum value of arg1 observations for each arg2 to argn group. |
@mcor(x,y,n) | n-period backwards moving correlation. NAs are not propagated. |
@mcov(x,y,n) | n-period backwards population moving covariance. NAs are not propagated. |
@mcovp(x,y,n) | n-period backwards moving population covariance. NAs are not propagated. |
@mcovs(x,y,n) | n-period backwards moving sample covariance. NAs are not propagated. |
@mean(x[,s]) | mean |
@meansby(arg1, arg2[,arg3, arg4..., argn, s]) | mean of arg1 observations for each arg2 to argn defined group. |
@median(x[,s]) | median |
@mediansby(arg1, arg2[, arg3, arg4..., argn, s]) | median of arg1 observations for each arg2 to argn defined group |
@mid(str, n1[, n2]) | returns the middle part of a string |
@min(x[,s]) | minimum |
@minner(x,y,n) | n-period backwards inner product of X and Y. NAs are not propagated. |
@mins(x,n) | n-minimum values, returned smallest to largest in a vector object. |
@minsby(arg1, arg2[,arg3, arg4..., argn, s]) | minimum value of arg1 observations for each arg2 to argn defined group. |
@minute | returns the minute of each observation as an integer. |
@mkurt(x,n) | n-period backwards kurtosis. NAs are not propagated. |
@mmax(x,n) | n-period backwards moving maximum. NAs are not propagated. |
@mmedian(x,n) | n-period backward moving median. NAs are not propagated. |
@mmin(x,n) | n-period backwards moving minimum. NAs are not propagated. |
@mnas(x,n) | n-period backwards number of NA observations. NAs are not propagated. |
@mobs(x,n) | n-period backwards number of non-NA observations. NAs are not propagated. |
@mod(x,y) | floating point remainder |
@month | month |
@movav(x,n) | n-period backward moving average. NAs are propagated. |
@movavc(x,n) | n-period centered moving average. NAs are propagated. |
@movcor(x,y,n) | n-period backwards moving correlation. NAs are propagated. |
@movcov(x,y,n) | n-period backwards moving population covariance. NAs are propagated. |
@movcovp(x,y,n) | n-period backwards moving population covariance. NAs are propagated. |
@movcovs(x,y,n) | n-period backwards moving sample covariance. NAs are propagated. |
@movinner(x,y,n) | n-period backwards inner product of X and Y. NAs are propagated. |
@movkurt(x,n) | n-period backwards kurtosis. NAs are propagated. |
@movmax(x,n) | n-period backwards moving maximum. NAs are propagated. |
@movmin(x,n) | n-period backwards moving minimum. NAs are propagated. |
@movnas(x,n) | n-period backwards nmber of NA observations |
@movobs(x,n) | n-period backwards nmber of non-NA observations |
@movskew(x,n) | n-period backwards skewness. NAs are propagated. |
@movstdev(x,n) | n-period backwards moving sample standard deviation. NAs are propagated. |
@movstdevp(x,n) | n-period backwards moving population standard deviation. NAs are propagated. |
@movstdevs(x,n) | n-period backwards moving sample standard deviation. NAs are propagated. |
@movsum(x,n) | n-period backward moving sum. NAs are propagated. |
@movsumsq(x,n) | n-period backwards sum-of-squares. NAs are propagated. |
@movvar(x,n) | n-period backwards moving population variance. NAs are propagated. |
@movvarp(x,n) | n-period backwards moving population variance. NAs are propagated. |
@movvars(x,n) | n-period backwards moving sample variance. NAs are propagated. |
@mse(x,y) | the mean of the squared difference between X and Y. |
@mskew(x,n) | n-period backwards skewness. NAs are not propagated. |
@mstdev(x,n) | n-period backwards moving sample standard deviation. NAs are not propagated. |
@mstdevp(x,n) | n-period backwards moving population standard deviation. NAs are not propagated. |
@mstdevs(x,n) | n-period backwards moving sample standard deviation. NAs are not propagated. |
@msum(x,n) | n-period backward moving sum. NAs are not propagated. |
@msumsq(x,n) | n-period backwards sum-of-squares. NAs are not propagated. |
@mvar(x,n) | n-period backwards moving population variance. NAs are not propagated. |
@mvarp(x,n) | n-period backwards moving population variance. NAs are not propagated. |
@mvars(x,n) | n-period backwards moving sample variance. NAs are not propagated. |
@nan(x,y) | recode NAs in X to Y |
@nas(x[,s]) | number of NAs |
@nasby(arg1, arg2[,arg3, arg4..., argn, s]) | number of arg1 NA values for each arg2 to argn defined group. |
@neqna(str1, str2) | tests for inequality of strings |
@neqna(x,y) | not equal to |
@now | returns current time |
@nper(r,x,pv[,fv,t]) | annuity number of periods |
nrnd | normal random number generator |
@obs(x[,s]) | number of observations |
@obsby(arg1, arg2[,arg3, arg4..., argn, s]) | number of non-NA arg1 observations for each arg2 to argn defined group. |
@obsid | cross-section observation number |
@obsrange | observations in range |
@obssmpl | observations in sample |
or | logical or |
@otod(n) | converts observation number to string |
@otods(n) | converts sample observation number to string |
@pagecount | returns a scalar containing the number of pages in the current workfile. |
@pageexist(str) | returns a 0 or 1 depending on whether the page specified by str exists in the current workfile. |
@pagefreq | returns a string containing the current page’s frequency. |
@pageids | returns a string containing a space delimited list of the id series for the current workfile page. |
@pagelist | returns a space delimited string containing the names of all the pages in the current active workfile. |
@pagename | returns a string containing the current default page name. |
@pagesmpl | returns a string containing the current sample for the active page. |
@pc(x) | one-period percentage change (in percent) |
@pca(x) | one-period percentage change—annualized (in percent) |
@pch(x) | one-period percentage change (in decimal) |
@pcha(x) | one-period percentage change—annualized (in decimal) |
@pchy(x) | one-year percentage change (in decimal) |
@pctiles(x[, ties, s]) | percentile |
@pcy(x) | one-year percentage change (in percent) |
@periodtodate(x, p) | cumulative sum of series x within each period, where each period is defined by a contiguous block of identical values in series p |
@pi |  |
@pmax(x,y) | returns the pairwise max of x and y (for more than pairs, you may still use @rmax) |
@pmin(x,y) | returns the pairwise min of x and y (for more than pairs, you may still use @rmin |
@pmt(r,n,pv[,fv,t]) | payment amount |
@pow(x,a) | power,  |
@powm1(x,a) | for x near 1,  |
@pow1pm1(x,a) | for x near zero,  |
@prod(x[,s]) | product |
@psi(x) | see @digamma |
@pv(r,n,x[,fv,t]) | present value |
@qbeta(s,a,b) | beta quantile function (inverse CDF) |
@qbinom(s,n,p) | binomial quantile function (inverse CDF) |
@qchisq(p,v) | chi-square quantile function (inverse CDF) |
@qexp(p,m) | exponential quantile function (inverse CDF) |
@qextreme(p) | extreme value quantile function (inverse CDF) |
@qfdist(p,v1,v2) | F-distribution quantile function (inverse CDF) |
@qgamma(p,b,r) | gamma quantile function (inverse CDF) |
@qged(p,r) | generalized error quantile function (inverse CDF) |
@qlaplace(x) | Laplace quantile function (inverse CDF) |
@qlogistic(p) | logistic quantile function (inverse CDF) |
@qlognorm(p,m,s) | log normal quantile function (inverse CDF) |
@qnegbin(s,n,p) | negative binomial quantile function (inverse CDF) |
@qnorm(p) | normal quantile function (inverse CDF) |
@qpareto(p,a,k) | Pareto quantile function (inverse CDF) |
@qpoisson(p,m) | Poisson quantile function (inverse CDF) |
@qtdist(p,v) | Student’s t-distribution quantile function (inverse CDF) |
@quantile(x,q[,m,s]) | quantile |
@quantilesby(arg1, arg2[,arg3, arg4..., argn], q[, s]) | q-quantiles of arg1 observations for each arg2 to argn defined group. |
@quarter | quarter of the year in a dated workfile |
@qunif(p,a,b) | uniform quantile function (inverse CDF) |
@qweib(p,m,a) | Weibull quantile function (inverse CDF) |
@ranks(x[,o,t,s]) | rank |
@rate(n,x,pv[,fv,t]) | interest |
@rbeta(a,b) | beta random number generator |
@rbinom(a,b) | binomial random number generator |
@rchisq(v) | chi-square random number generator |
@recode(s,x,y) | recode by condition |
@replace(str1, str2, str3[,n]) | replaces a string with another |
@rexp(m) | exponential random number generator |
@rextreme(p) | extreme value random number generator |
@rfdist(v1,v2) | F-distribution random number generator |
@rfirst(g) | row-wise first non-NA value. |
@rgamma(b,r) | gamma random number generator |
@rged(r) | generalised error random number generator |
@right(str,n) | returns the right parts of a string |
@rifirst(g) | row-wise first non-NA index. |
@rilast(g) | row-wise last non-NA index. |
@rimax(g) | row-wise maximum index. |
@rimin(g) | row-wise minimum index. |
@riwish([v,]s) | Inverse Wishart random number generator with sym covariance |
@riwishc([v,]s) | Inverse Wishart random number generator with sym Choesky of covariance |
@riwishi([v,]s)i | Inverse Wishart random number generator with sym inverse covariance |
@riwishic([v,]s) | Inverse Wishart andom number generator with sym Cholesky of inverse covariance |
@rlaplace | Laplace random number generator |
@rlast(g) | row-wise last non-NA value. |
@rlogistic | logistic random number generator |
@rlognorm(m,s) | log normal random number generator |
@rmax(g) | row-wise maximum value. |
@rmean(g) | row-wise mean. |
@rmedian(g) | row-wise median |
@rmin(g) | row-wise minimum value. |
@rmvnorm([v,]s) | multivariate normal random number generator with sym covariance |
@rmvnormc([v,]s) | multivariate normal random number generator with sym Choesky of covariance |
@rmvnormi([v,]s)i | multivariate normal random number generator with sym inverse covariance |
@rmvnormic([v,]s) | multivariate normal random number generator with sym Cholesky of inverse covariance |
@rnas(g) | row-wise number of NAs. |
@rnegbin(n,p) | negative binomial random number generator |
@rnorm | normal random number generator |
@robs(g) | row-wise number of non-NAs. |
@round(x[,n]) | round to the nearest integer |
@rpareto(a,k) | Pareto random number generator |
@rpoisson(m) | Poisson random number generator |
@rprod(g) | row-wise product (one should be careful of overflow) |
@rquantile(g,q) | row-wise quantile, where q is the quantile. The Cleveland quantile definition is used. |
@rstdev(g) | row-wise sample standard deviation. |
@rstdevp(g) | row-wise population standard deviation. |
@rstdevs(g) | row-wise sample standard deviation. |
@rsum(g) | row-wise sum. |
@rsumsq(g) | row-wise sum-of-squares. |
@rtdist(v) | Student’s t-distribution random number generator |
@rtrim(str) | removes spaces from a string |
@runif(a,b) | uniform random number generator |
@rvalcount(g,v) | row-wise count of observations matching v. |
@rvar(g) | row-wise population variance. |
@rvarp(g) | row-wise population variance |
@rvars(g) | row-wise sample variance. |
@rweib(m,a) | Weibull random number generator |
@rwish([v,]s) | Wishart random number generator with sym covariance |
@rwishc([v,]s) | Wishart random number generator with sym Choesky of covariance |
@rwishi([v,]s)i | Wishart random number generator with sym inverse covariance |
@rwishic([v,]s) | Wishart andom number generator with sym Cholesky of inverse covariance |
@seas(x) | seasonal dummy |
@second | returns the second of each observation as an integer. |
@sign(x) | returns -1, 0, 1 depending on the sign of the corresponding element of x |
@sin(x) | sine (argument in radians) |
@skew(x[,s]) | skewness |
@skewsby(arg1, arg2[,arg3, arg4..., argn, s) | skewness of arg1 observations for each arg2 to argn defined group. |
@smape(x,y[,s]) | symmetric mean absolute percentage error |
@sqrt(x), sqr(x) | square root |
@stdev(x[,s]) | standard deviation |
@stdevp(x[,s]) | population standard deviation |
@stdevs(x[,s]) | sample standard deviation |
@stdevpsby(arg1, arg2[,arg3, arg4..., argn, s]) | standard deviation of arg1 observations for each arg2 to argn defined group (division by  ). |
@stdevsby(arg1, arg2[,arg3, arg4..., argn, s]) | sample standard deviation of arg1 observations for each arg2 to argn defined group (division by  ). |
@stdevssby((arg1, arg2[,arg3, arg4..., argn, s]) | sample standard deviation of arg1 observations for each arg2 to argn defined group (division by  ). |
@stdize(x[, s]) | standardize series using sample variance. |
@stdizep(x[,s]) | standardize series using population variance. |
@str(d,[fmt]) | converts a number to a string |
@strdate(fmt) | string corresponding to each element in workfile |
@strdate(x) | string dates |
@strlen(str) | length of a string |
@strnow(fmt) | returns current time as a string |
@sum(x[,s]) | sum |
@sumsby(arg1, arg2[, s]) | sum of arg1 observations for each arg2 to argn defined group. |
@sumsq(x[,s]) | sum-of-squares |
@sumsqsby(arg1, arg2[, s]) | sum of squares of arg1 observations for each arg2 to argn defined group. |
@tablenames(str) | returns a space delimited string containing the names of the table names of a foreign file. |
@tan(x) | tangent (argument in radians) |
@tdist(x,v) | t-distribution p-value |
@trend | time trend |
@trend([x]) | time trend |
@trendc | calendar time trend |
@trendc([x]) | calendar time trend |
@trendcoef(x[,s]) | coefficient from OLS regression on a trend |
@trmean(x,p[, s]) | trimmed mean |
@trigamma(x) | second derivative of the log gamma function |
@trim(str) | removes spaces from a string |
@unmap(x, mapname) | unmapped numeric value |
@unmaptxt(x, mapname) | unmapped text value |
@upper(str) | uppercases a string |
@val(str[, fmt]) | converts a string to a number |
@var(x[,s]) | population variance |
@varp(x[,s]) | population variance |
@varpsby(arg1, arg2[,arg3, arg4..., argn, s]) | variance of arg1 observations for each arg2 to argn defined group (division by  ). |
@vars(x[,s]) | sample variance |
@varsby(arg1, arg2[,arg3, arg4..., argn, s]) | variance of arg1 observations for each arg2 to argn defined group (division by  ). |
@varssby(arg1, arg2[,arg3, arg4..., argn, s]) | sample variance of arg1 observations for each arg2 to argn defined group (division by  ). |
@vernum | returns a scalar containing the EViews version number. |
@verstr | returns a string containing the EViews version string (EViews Enterprise Edition). |
@wdir(str) | returns a string list containing a list of all of the files in the directory str. |
@weekday | day of the week |
@wfname | returns a string containing the current default workfile name. |
@wfpath | returns a string containing the current default workfile path. |
@wquery("database", "search_expression", "attribute_list") | returns a string list containing the attribute_list of all objects in database that match the query given by search_expression . |
@year | year |
@xgetstr(string) | returns a string from the external application. |
@xgetnum(string) | returns a scalar from the external application. |
@xverstr | returns the external application version number as a string. |
@xvernum | returns the external application version number as a number. |
@ytd(x, p) | returns the cumulative sum of a series within the year. |