The following functions and CALL subroutines are useful for analyzing and simulating time series data:

Function | Purpose |
---|---|

ARMASIM | simulates an autoregressive moving average (ARMA) series |

CONVEXIT | computes convexity of a noncontingent cash flow |

COVLAG | computes autocovariance estimates for a vector time series |

DIF | computes the difference between a value and a lagged value |

DURATION | computes modified duration of a noncontingent cash flow |

FORWARD | computes forward rates |

LAG | computes lagged values |

PV | computes the present value |

RATES | converts interest rates from one base to another |

SPOT | computes spot rates |

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

CALL Subroutine | Purpose |
---|---|

ARMACOV | computes an autocovariance sequence for an ARMA model |

ARMALIK | computes the log likelihood and residuals for an ARMA model |

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

FARMAFIT | estimates the parameters of an ARFIMA () model |

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

FARMASIM | generates an ARFIMA () process |

FDIF | computes a fractionally differenced process |

KALCVF | 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. |

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

KALDFF | 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. |

KALDFS | 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 |

TSBAYSEA | performs Bayesian seasonal adjustment modeling |

TSDECOMP | analyzes nonstationary time series by using smoothness priors modeling |

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

TSMLOMAR | analyzes nonstationary or locally stationary multivariate time series by using a method that minimizes the AIC |

TSMULMAR | estimates vector autoregressive (VAR) processes by minimizing the AIC |

TSPEARS | analyzes periodic autoregressive (AR) models by minimizing the AIC |

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

TSROOT | 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 | analyzes time series that are nonstationary in the covariance function |

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

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

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

VARMASIM | generates a VARMA () time series |

VNORMAL | generates multivariate normal random series |

VTSROOT | computes the characteristic roots for a VARMA () model |