Language Reference

Time Series Functions

ARMACOV call: computes an autocovariance sequence for an autoregressive moving average (ARMA) model
ARMALIK call: computes the log likelihood and residuals for an ARMA model
ARMASIM function: simulates an ARMA series
CONVEXIT function: computes convexity of a noncontingent cash flow
COVLAG function: computes autocovariance estimates for a vector time series
DIF function: computes the difference between a value and a lagged value
DURATION function: computes modified duration of a noncontingent cash flow
FARMACOV call: computes the autocovariance function for an autoregressive fractionally integrated moving average (ARFIMA) model of the form ARFIMA( $p,d,q$ )
FARMAFIT call: estimates the parameters of an ARFIMA( $p,d,q$ ) model
FARMALIK call: computes the log-likelihood function of an ARFIMA( $p,d,q$ ) model
FARMASIM call: generates an ARFIMA( $p,d,q$ ) process
FDIF call: computes a fractionally differenced process
FORWARD function: computes forward rates
KALCVF call: computes the one-step prediction $z_{t+1|t}$ and the filtered estimate $z_{t|t}$ , in addition to their covariance matrices. The call uses forward recursions, and you can also use it to obtain k-step estimates.
KALCVS call: uses backward recursions to compute the smoothed estimate $z_{t|T}$ and its covariance matrix, $P_{t|T}$ , where T is the number of observations in the complete data set
KALDFF call: computes the one-step forecast of state vectors in a state space model (SSM) by using the diffuse Kalman filter. The call estimates the conditional expectation of $z_ t$ , and it also estimates the initial random vector, $\delta$ , and its covariance matrix.
KALDFS call: computes the smoothed state vector and its mean squares error matrix from the one-step forecast and mean squares error matrix computed by the KALDFF subroutine.
LAG function: computes lagged values
PV function: computes the present value
RATES function: converts interest rates from one base to another
SPOT function: computes spot rates
TSBAYSEA call: performs Bayesian seasonal adjustment modeling
TSDECOMP call: analyzes nonstationary time series by using smoothness priors modeling
TSMLOCAR call: analyzes nonstationary or locally stationary time series by using a method that minimizes Akaike’s information criterion (AIC)
TSMLOMAR call: analyzes nonstationary or locally stationary multivariate time series by using a method that minimizes Akaike’s information criterion (AIC)
TSMULMAR call: estimates vector autoregressive (VAR) processes by minimizing the AIC
TSPEARS call: analyzes periodic autoregressive (AR) models by minimizing the AIC
TSPRED call: provides predicted values of univariate and multivariate ARMA processes when the ARMA coefficients are given
TSROOT call: computes AR and moving average (MA) coefficients from the characteristic roots of the model, or computes the characteristic roots of the model from the AR and MA coefficients
TSTVCAR call: analyzes time series that are nonstationary in the covariance function
TSUNIMAR call: determines the order of an AR process by minimizing the AIC, and estimates the AR coefficients
VARMACOV call: computes the theoretical cross-covariance matrices for a stationary vector autoregressive moving average (VARMA( $p,q$ )) model
VARMALIK call: computes the log-likelihood function for a VARMA( $p,q$ ) model
VARMASIM call: generates VARMA( $p,q$ ) time series
VNORMAL call: generates multivariate normal random series
VTSROOT call: computes the characteristic roots for a VARMA( $p,q$ ) model
YIELD function: computes yield-to-maturity of a cash-flow stream

You can also call functions in Base SAS software such as those documented in the section Financial Functions.