Description Usage Arguments Value References See Also Examples

The main function for estimating a mixed-frequency BVAR.

1 | ```
estimate_mfbvar(mfbvar_prior = NULL, prior, variance = "iw", ...)
``` |

`mfbvar_prior` |
a |

`prior` |
either |

`variance` |
form of the error variance-covariance matrix: |

`...` |
additional arguments to |

An object of class `mfbvar`

, `mfbvar_<prior>`

and `mfbvar_<prior>_<variance>`

containing posterior quantities as well as the prior object. For all choices of `prior`

and `variance`

, the returned object contains:

`Pi` |
Array of dynamic coefficient matrices; |

`Z` |
Array of monthly processes; |

`Z_fcst` |
Array of monthly forecasts; |

If `prior = "ss"`

, it also includes:

`psi`

Matrix of steady-state parameter vectors;

`psi[r,]`

is the`r`

th draw`roots`

The maximum eigenvalue of the lag polynomial (if

`check_roots = TRUE`

)

If `prior = "ssng"`

, it also includes:

`psi`

Matrix of steady-state parameter vectors;

`psi[r,]`

is the`r`

th draw`roots`

The maximum eigenvalue of the lag polynomial (if

`check_roots = TRUE`

)`lambda_psi`

Vector of draws of the global hyperparameter in the normal-Gamma prior

`phi_psi`

Vector of draws of the auxiliary hyperparameter in the normal-Gamma prior

`omega_psi`

Matrix of draws of the prior variances of psi;

`omega_psi[r, ]`

is the`r`

th draw, where`diag(omega_psi[r, ])`

is used as the prior covariance matrix for psi

If `variance = "iw"`

or `variance = "diffuse"`

, it also includes:

`Sigma`

Array of error covariance matrices;

`Sigma[,, r]`

is the`r`

th draw

If `variance = "csv"`

, it also includes:

`Sigma`

Array of error covariance matrices;

`Sigma[,, r]`

is the`r`

th draw`phi`

Vector of AR(1) parameters for the log-volatility regression;

`phi[r]`

is the`r`

th draw`sigma`

Vector of error standard deviations for the log-volatility regression;

`sigma[r]`

is the`r`

th draw`f`

Matrix of log-volatilities;

`f[r, ]`

is the`r`

th draw

If `variance = "fsv"`

, it also includes:

`facload`

Array of factor loadings;

`facload[,, r]`

is the`r`

th draw`latent`

Array of latent log-volatilities;

`latent[,, r]`

is the`r`

th draw`mu`

Matrix of means of the log-volatilities;

`mu[, r]`

is the`r`

th draw`phi`

Matrix of AR(1) parameters for the log-volatilities;

`phi[, r]`

is the`r`

th draw`sigma`

Matrix of innovation variances for the log-volatilities;

`sigma[, r]`

is the`r`

th draw

Ankargren, S., Unosson, M., & Yang, Y. (2020) A Flexible Mixed-Frequency Bayesian Vector Autoregression with a Steady-State Prior. *Journal of Time Series Econometrics*, 12(2), doi: 10.1515/jtse-2018-0034.

Ankargren, S., & Jonéus, P. (2020) Simulation Smoothing for Nowcasting with Large Mixed-Frequency VARs. *Econometrics and Statistics*, doi: 10.1016/j.ecosta.2020.05.007.

Ankargren, S., & Jonéus, P. (2019) Estimating Large Mixed-Frequency Bayesian VAR Models. arXiv:1912.02231, https://arxiv.org/abs/1912.02231.

Kastner, G., & Huber, F. (2020) Sparse Bayesian Vector Autoregressions in Huge Dimensions. *Journal of Forecasting*, 39, 1142–1165. doi: 10.1002/for.2680.

Schorfheide, F., & Song, D. (2015) Real-Time Forecasting With a Mixed-Frequency VAR. *Journal of Business & Economic Statistics*, 33(3), 366–380. doi: 10.1080/07350015.2014.954707

`set_prior`

, `update_prior`

, `predict.mfbvar`

, `plot.mfbvar_minn`

,
`plot.mfbvar_ss`

, `varplot`

, `summary.mfbvar`

1 2 | ```
prior_obj <- set_prior(Y = mf_usa, n_lags = 4, n_reps = 20)
mod_minn <- estimate_mfbvar(prior_obj, prior = "minn")
``` |

```
```

mfbvar documentation built on Feb. 10, 2021, 5:12 p.m.

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