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.