simulate.fake.mixed.frequency.data {bsts} | R Documentation |

Simulate a fake data set that can be used to test mixed frequency code.

SimulateFakeMixedFrequencyData(nweeks, xdim, number.nonzero = xdim, start.date = as.Date("2009-01-03"), sigma.obs = 1.0, sigma.slope = .5, sigma.level = .5, beta.sd = 10)

`nweeks` |
The number of weeks of data to simulate. |

`xdim` |
The dimension of the predictor variables to be simulated. |

`number.nonzero` |
The number nonzero coefficients. Must be
less than or equal to |

`start.date` |
The date of the first simulated week. |

`sigma.obs` |
The residual standard deviation for the fine time scale model. |

`sigma.slope` |
The standard deviation of the slope component of the local linear trend model for the fine time scale data. |

`sigma.level` |
The standard deviation of the level component fo the local linear trend model for the fine time scale data. |

`beta.sd` |
The standard deviation of the regression coefficients to be simulated. |

The simulation begins by simulating a local linear trend model for
`nweeks`

to get the trend component.

Next a `nweeks`

by `xdim`

matrix of predictor variables is
simulated as IID normal(0, 1) deviates, and a `xdim`

-vector of
regression coefficients is simulated as IID normal(0, `beta.sd`

).
The product of the predictor matrix and regression coefficients is
added to the output of the local linear trend model to get
`fine.target`

.

Finally, `fine.target`

is aggregated to the month level to get
`coarse.target`

.

Returns a list with the following components

`coarse.target` |
A |

`fine.target` |
A |

`predictors` |
A |

`true.beta` |
The vector of "true" regression coefficients used to
simulate |

`true.sigma.obs` |
The residual standard deviation that was used to
simulate |

`true.sigma.slope` |
The value of |

`true.sigma.level` |
The value of |

`true.trend` |
The combined contribution of the simulated latent
state on |

`true.state` |
A matrix containin the fine-scale state of the model
being simulated. Columns represent time (weeks). Rows correspond
to regression (a constant 1), the local linear trend level, the
local linear trend slope, the values of |

Steven L. Scott steve.the.bayesian@gmail.com

Harvey (1990), "Forecasting, structural time series, and the Kalman filter", Cambridge University Press.

Durbin and Koopman (2001), "Time series analysis by state space methods", Oxford University Press.

`bsts.mixed`

,
`AddLocalLinearTrend`

,

fake.data <- SimulateFakeMixedFrequencyData(nweeks = 100, xdim = 10) plot(fake.data$coarse.target)

[Package *bsts* version 0.9.6 Index]