Mention that Factors are assumed to follow a stationary VAR process of order p + add Eli as reviewer.

Author: SebKrantz

DESCRIPTION

@@ -3,17 +3,19 @@ Version: 0.3.0
 Title: Dynamic Factor Models
 Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "[email protected]"),
              person("Rytis", "Bagdziunas", role = "aut"),
-             person("Santtu", "Tikka", role = "rev"))
+             person("Santtu", "Tikka", role = "rev"),
+             person("Eli", "Holmes", role = "rev"))
 Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
-  or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the 
-  econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 
-  2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated 
-  Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) 
-  <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
-  allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and 
-  the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports 
-  interpretation and visualization of the model as well as forecasting. Information criteria to choose the number 
-  of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
+  or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR 
+  process of order p. The estimation options follow advances in the econometric literature: either running the Kalman 
+  Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) 
+  <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following 
+  Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and 
+  Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy 
+  use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. 
+  A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. 
+  Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) 
+  <doi:10.1111/1468-0262.00273>.
 URL: https://sebkrantz.github.io/dfms/
 BugReports: https://github.com/SebKrantz/dfms/issues
 Depends: R (>= 3.5.0)

README.md

@@ -17,7 +17,8 @@
 <!--
 The package is fully functional though, and you are very welcome to install it using `remotes::install_github("SebKrantz/dfms")` and give feedback. -->
 
-*dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm. Estimation can be done in 3 different ways following:
+*dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm. Factors are assumed to follow a stationary VAR 
+  process of order `p`. Estimation can be done in 3 different ways following:
 
 - Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. *Journal of Econometrics, 164*(1), 188-205. <doi:10.1016/j.jeconom.2011.02.012> 
 
@@ -27,7 +28,7 @@ The package is fully functional though, and you are very welcome to install it u
 
 The default is `em.method = "auto"`, which chooses `"BM"` following Banbura & Modugno (2014) with missing data or mixed frequency, and `"DGR"` following Doz, Giannone & Reichlin (2012) otherwise. Using `em.method = "none"` generates Two-Step estimates following Doz, Giannone & Reichlin (2011). This is extremely efficient on bigger datasets. PCA and Two-Step estimates are also reported in EM-estimation. All methods support missing data, but `em.method = "DGR"` does not model them in EM iterations.
 
-The package is stable, but functionality may expand in the future. In particular, mixed-frequency estimation with autoregressive errors is planned for the near future, and generation of the 'news' may be added in the further future. 
+The package is currently stable, but functionality may expand in the future. In particular, mixed-frequency estimation with autoregressive errors is planned for the near future, and generation of the 'news' may be added in the further future. 
 
 
 ### Comparison with Other R Packages
@@ -56,7 +57,7 @@ install.packages('dfms', repos = c('https://sebkrantz.r-universe.dev', 'https://
 library(dfms)
 
 # Fit DFM with 6 factors and 3 lags in the transition equation
-mod = DFM(diff(BM14_M), r = 6, p = 3) 
+mod <- DFM(diff(BM14_M), r = 6, p = 3) 
 ```
 
 ```
@@ -158,7 +159,7 @@ as.data.frame(mod) |> head()
 
 ```r
 # Forecasting 20 periods ahead
-fc = predict(mod, h = 20)
+fc <- predict(mod, h = 20)
 
 # 'dfm_forecast' methods
 plot(fc)