dfm: Estimates a dynamic factor model based on Doz, Gianone &...

In guilbran/dynfactoR: Dynamic factor model estimation for nowcasting

Description

Estimates a dynamic factor model based on Doz, Gianone & Reichlin (2011)

Usage

dfm(X, r, p, q, max_iter = 100, threshold = 1e-04, rQ, rC)

Arguments

X	Data matrix (T x n)
r	Number of static factors
p	Lag order for factors
q	Number of dynamic factors, must be equal to or less than r
max_iter	Maximum number of iterations in EM-algorithm
threshold	Threshold for algorithm convergence
rQ	Restrictions on system state covariance
rC	Restrictions on factor loading matrix

Value

3 types of factor estimates, namely principal component estimate, two step estimate based on PCA and Kalman filtering and QML estimate based on EM-algorithm

Examples

x <- matrix(rnorm(50*10), 50, 10)
W <- as.logical(matrix(rbinom(50*10, 1, 0.1), 50, 10))
x[W] <- NA
dfm(x, 2, 2, 1)

guilbran/dynfactoR documentation built on May 8, 2019, 1:35 a.m.