Dynamic Factor Model Input
gen_dfm produces the input for the estimation of a dynamic factor model (DFM).
gen_dfm(x, p = 2, n = 1, iterations = 50000, burnin = 5000)
x |
a time-series object of stationary endogenous variables. |
p |
an integer vector of the lag order of the measurement equation. See 'Details'. |
n |
an integer vector of the number of factors. See 'Details'. |
iterations |
an integer of MCMC draws excluding burn-in draws (defaults to 50000). |
burnin |
an integer of MCMC draws used to initialize the sampler (defaults to 5000). These draws do not enter the computation of posterior moments, forecasts etc. |
The function produces the variable matrices of dynamic factor models (DFM) with measurement equation
x_t = λ f_t + u_t,
where x_t is an M \times 1 vector of observed variables, f_t is an N \times 1 vector of unobserved factors and λ is the corresponding M \times N matrix of factor loadings. u_t is an M \times 1 error term.
The transition equation is
f_t = ∑_{i=1}^{p} A_i f_{t - i} + v_t,
where A_i is an N \times N coefficient matrix and v_t is an N \times 1 error term.
If integer vectors are provided as arguments p or n, the function will
produce a distinct model for all possible combinations of those specifications.
An object of class 'dfmodel', which contains the following elements:
data |
A list of data objects, which can be used for posterior simulation. Element
|
model |
A list of model specifications. |
Chan, J., Koop, G., Poirier, D. J., & Tobias, J. L. (2019). Bayesian Econometric Methods (2nd ed.). Cambridge: University Press.
Lütkepohl, H. (2007). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.
# Load data
data("bem_dfmdata")
# Generate model data
model <- gen_dfm(x = bem_dfmdata, p = 1, n = 1,
iterations = 5000, burnin = 1000)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.