computes an autocovariance sequence for an autoregressive moving average (ARMA) model

computes the log likelihood and residuals for an ARMA model

simulates an ARMA series

computes convexity of a noncontingent cash flow

computes autocovariance estimates for a vector time series

computes the difference between a value and a lagged value

computes modified duration of a noncontingent cash flow

computes the autocovariance function for an autoregressive fractionally integrated moving average (ARFIMA) model of the form ARFIMA()

estimates the parameters of an ARFIMA() model

computes the log-likelihood function of an ARFIMA() model

generates an ARFIMA() process

computes a fractionally differenced process

computes forward rates

computes the one-step prediction and the filtered estimate , in addition to their covariance matrices. The call uses forward recursions, and you can also use it to obtain -step estimates.

uses backward recursions to compute the smoothed estimate and its covariance matrix, , where is the number of observations in the complete data set

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 , and it also estimates the initial random vector, , and its covariance matrix.

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.

computes lagged values

computes the present value

converts interest rates from one base to another

computes spot rates

performs Bayesian seasonal adjustment modeling

analyzes nonstationary time series by using smoothness priors modeling

analyzes nonstationary or locally stationary time series by using a method that minimizes Akaike’s information criterion (AIC)

analyzes nonstationary or locally stationary multivariate time series by using a method that minimizes Akaike’s information criterion (AIC)

estimates vector autoregressive (VAR) processes by minimizing the AIC

analyzes periodic autoregressive (AR) models by minimizing the AIC

provides predicted values of univariate and multivariate ARMA processes when the ARMA coefficients are given

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

analyzes time series that are nonstationary in the covariance function

determines the order of an AR process by minimizing the AIC, and estimates the AR coefficients

computes the theoretical cross-covariance matrices for a stationary vector autoregressive moving average (VARMA()) model

computes the log-likelihood function for a VARMA() model

generates VARMA() time series

generates multivariate normal random series

computes the characteristic roots for a VARMA() model

computes yield-to-maturity of a cash-flow stream