introduction.R
In dfms: Dynamic Factor Models

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  cache = TRUE,
  fig.width = 7,
  fig.height = 5,
  comment = "#>"
)

opt <- options(max.print = 70)

## ----setup, message=FALSE-----------------------------------------------------
library(dfms)
library(xts)

## -----------------------------------------------------------------------------
# Using the monthly series from BM14
dim(BM14_M)
range(index(BM14_M))
head(colnames(BM14_M))
plot(scale(BM14_M), lwd = 1)

## -----------------------------------------------------------------------------
head(BM14_Models, 3)

# Using only monthly data
BM14_Models_M <- subset(BM14_Models, freq == "M")

## -----------------------------------------------------------------------------
library(magrittr)
# log-transforming and first-differencing the data
BM14_M[, BM14_Models_M$log_trans] %<>% log()
BM14_M_diff = diff(BM14_M)
plot(scale(BM14_M_diff), lwd = 1)

## -----------------------------------------------------------------------------
ic = ICr(BM14_M_diff)
print(ic)
plot(ic)

## -----------------------------------------------------------------------------
screeplot(ic)

## -----------------------------------------------------------------------------
# Using vars::VARselect() with 4 principal components to estimate the VAR lag order
vars::VARselect(ic$F_pca[, 1:4])

## -----------------------------------------------------------------------------
# Estimating the model with 4 factors and 3 lags using BM14's EM algorithm
model1 = DFM(BM14_M_diff, r = 4, p = 3)
print(model1)
plot(model1)

## -----------------------------------------------------------------------------
dfm_summary <- summary(model1)
print(dfm_summary) # Large model with > 40 series: defaults to compact = 2

# Can request more detailed printouts
# print(dfm_summary, compact = 1)
# print(dfm_summary, compact = 0) 

## -----------------------------------------------------------------------------
plot(resid(model1, orig.format = TRUE))
plot(fitted(model1, orig.format = TRUE))

## -----------------------------------------------------------------------------
plot(model1, method = "all", type = "individual")

## -----------------------------------------------------------------------------
# Default: all estimates in long format
head(as.data.frame(model1, time = index(BM14_M_diff)))

## -----------------------------------------------------------------------------
# 12-period ahead DFM forecast
fc = predict(model1, h = 12)
print(fc)

## -----------------------------------------------------------------------------
# Setting an appropriate plot range to see the forecast
plot(fc, xlim = c(320, 370))
# Predicting with Two-Step estimates
# plot(predict(model1, h = 12, method = "2s"), xlim = c(320, 370))

## -----------------------------------------------------------------------------
# Factor forecasts in wide format
head(as.data.frame(fc, pivot = "wide"))

## ----eval = FALSE, include=FALSE----------------------------------------------
#  # Quarterly series from BM14
#  head(BM14_Q, 3)
#  # Pre-processing the data
#  BM14_Q[, BM14_Models[BM14_Models$freq == "Q", ]$log_trans] %<>% log()
#  BM14_Q_diff = diff(BM14_Q)
#  # Merging to monthly data and duplicating 2 times
#  BM14_diff = cbind(BM14_M_diff, BM14_Q_diff, BM14_Q_diff, BM14_Q_diff)
#  
#  # Estimating the model with 5 factors and 3 lags using BM14's EM algorithm
#  model2 = DFM(BM14_diff, r = 5, p = 3)
#  print(model2)
#  plot(model2)

## ----include=FALSE------------------------------------------------------------
options(opt)