From 2375589e2aee9b698a25abfe540473ad6ef47703 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Mon, 12 May 2025 23:49:37 +0200
Subject: [PATCH 01/21] Spelling.

---
 R/DFM.R    | 8 ++++----
 man/DFM.Rd | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/R/DFM.R b/R/DFM.R
index 3582d86..3b06ece 100644
--- a/R/DFM.R
+++ b/R/DFM.R
@@ -12,13 +12,13 @@
 #' with time-invariant system matrices and classical assumptions, while permitting missing data.
 #'
 #' @param X a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values.
-#' @param r integer. number of factors.
-#' @param p integer. number of lags in factor VAR.
+#' @param r integer. Number of factors.
+#' @param p integer. Number of lags in factor VAR.
 #' @param \dots (optional) arguments to \code{\link{tsnarmimp}}.
 #' @param idio.ar1 logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generaly not needed if \code{n} is large (>30). See theoretical vignette for details.
 #' @param quarterly.vars character. Names of quarterly variables in \code{X} (if any). Monthly variables should be to the left of the quarterly variables in the data matrix and quarterly observations should be provided every 3rd period.
-#' @param rQ character. restrictions on the state (transition) covariance matrix (Q).
-#' @param rR character. restrictions on the observation (measurement) covariance matrix (R).
+#' @param rQ character. Restrictions on the state (transition) covariance matrix (Q).
+#' @param rR character. Restrictions on the observation (measurement) covariance matrix (R).
 #' @param em.method character. The implementation of the Expectation Maximization Algorithm used. The options are:
 #'    \tabular{llll}{
 #' \code{"auto"} \tab\tab Automatic selection: \code{"BM"} if \code{anyNA(X)}, else \code{"DGR"}. \cr\cr
diff --git a/man/DFM.Rd b/man/DFM.Rd
index 2f4a277..ac311f4 100644
--- a/man/DFM.Rd
+++ b/man/DFM.Rd
@@ -24,9 +24,9 @@ DFM(
 \arguments{
 \item{X}{a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values.}
 
-\item{r}{integer. number of factors.}
+\item{r}{integer. Number of factors.}
 
-\item{p}{integer. number of lags in factor VAR.}
+\item{p}{integer. Number of lags in factor VAR.}
 
 \item{\dots}{(optional) arguments to \code{\link{tsnarmimp}}.}
 
@@ -34,9 +34,9 @@ DFM(
 
 \item{quarterly.vars}{character. Names of quarterly variables in \code{X} (if any). Monthly variables should be to the left of the quarterly variables in the data matrix and quarterly observations should be provided every 3rd period.}
 
-\item{rQ}{character. restrictions on the state (transition) covariance matrix (Q).}
+\item{rQ}{character. Restrictions on the state (transition) covariance matrix (Q).}
 
-\item{rR}{character. restrictions on the observation (measurement) covariance matrix (R).}
+\item{rR}{character. Restrictions on the observation (measurement) covariance matrix (R).}
 
 \item{em.method}{character. The implementation of the Expectation Maximization Algorithm used. The options are:
 \tabular{llll}{

From ba1092cc72f14688ff89150cb8b65fe62927df91 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Mon, 12 May 2025 23:49:49 +0200
Subject: [PATCH 02/21] More clarity.

---
 R/dfms.R            | 2 +-
 man/dfms-package.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/dfms.R b/R/dfms.R
index db5dfc6..b024c4c 100644
--- a/R/dfms.R
+++ b/R/dfms.R
@@ -4,7 +4,7 @@
 #'
 #' *dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). The package has the following contents:
 #'
-#' **Information Criteria**
+#' **Information Criteria to Determine the Number of Factors**
 #'
 #'  \code{\link[=ICr]{ICr()}}\cr
 #'
diff --git a/man/dfms-package.Rd b/man/dfms-package.Rd
index fafe4ff..4146117 100644
--- a/man/dfms-package.Rd
+++ b/man/dfms-package.Rd
@@ -8,7 +8,7 @@
 \description{
 \emph{dfms} provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). The package has the following contents:
 
-\strong{Information Criteria}
+\strong{Information Criteria to Determine the Number of Factors}
 
 \code{\link[=ICr]{ICr()}}\cr
 \itemize{

From d34a104ea93d0f74e2afe28a4092a865e9f46592 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 00:14:09 +0200
Subject: [PATCH 03/21] Minor fixes.

---
 R/my_RcppExports.R | 4 ++--
 man/SKF.Rd         | 2 +-
 man/SKFS.Rd        | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/R/my_RcppExports.R b/R/my_RcppExports.R
index 7080c2d..1087352 100644
--- a/R/my_RcppExports.R
+++ b/R/my_RcppExports.R
@@ -4,7 +4,7 @@ Estep <- function(X, A, C, Q, R, F_0, P_0) {
 
 #' (Fast) Stationary Kalman Filter
 #'
-#' @description A simple and fast C++ implementation of the Kalman Filter for stationary data with time-invariant system matrices and missing data.
+#' @description A simple and fast C++ implementation of the Kalman Filter for stationary data (or random walks - data should be mean zero and without a trend) with time-invariant system matrices and missing data.
 #' @param X numeric data matrix (\eqn{T \times n}{T x n}).
 #' @param A transition matrix (\eqn{rp \times rp}{rp x rp}).
 #' @param C observation matrix (\eqn{n \times rp}{n x rp}).
@@ -103,7 +103,7 @@ FIS <- function(A, F, F_pred, P, P_pred, F_0 = NULL, P_0 = NULL) {
 #' @inheritParams SKF
 #'
 #' @returns All results from \code{\link{SKF}} and \code{\link{FIS}}, and additionally
-#' a \eqn{rp \times rp \times T}{rp x rp x T} matrix \code{PPm_smooth}, which is equal to the estimate of \eqn{Cov(F^smooth_t, F^smooth_{t-1} | T)}{Cov(F_smooth(t), F_smooth(t-1) | T)} and needed for EM iterations.
+#' a \eqn{rp \times rp \times T}{rp x rp x T} matrix \code{PPm_smooth}, which is equal to the estimate of \eqn{Cov(F^{smooth}_t, F^{smooth}_{t-1} | T)}{Cov(F_smooth(t), F_smooth(t-1) | T)} and needed for EM iterations.
 #' See 'Property 6.3: The Lag-One Covariance Smoother' in Shumway & Stoffer (2017).
 #'
 #'
diff --git a/man/SKF.Rd b/man/SKF.Rd
index c824cbe..3e67744 100644
--- a/man/SKF.Rd
+++ b/man/SKF.Rd
@@ -32,7 +32,7 @@ Predicted and filtered state vectors and covariances.
 \item{\code{loglik}}{value of the log likelihood. }
 }
 \description{
-A simple and fast C++ implementation of the Kalman Filter for stationary data with time-invariant system matrices and missing data.
+A simple and fast C++ implementation of the Kalman Filter for stationary data (or random walks - data should be mean zero and without a trend) with time-invariant system matrices and missing data.
 }
 \details{
 The underlying state space model is:
diff --git a/man/SKFS.Rd b/man/SKFS.Rd
index 0a6bdce..1f203aa 100644
--- a/man/SKFS.Rd
+++ b/man/SKFS.Rd
@@ -25,7 +25,7 @@ SKFS(X, A, C, Q, R, F_0, P_0, loglik = FALSE)
 }
 \value{
 All results from \code{\link{SKF}} and \code{\link{FIS}}, and additionally
-a \eqn{rp \times rp \times T}{rp x rp x T} matrix \code{PPm_smooth}, which is equal to the estimate of \eqn{Cov(F^smooth_t, F^smooth_{t-1} | T)}{Cov(F_smooth(t), F_smooth(t-1) | T)} and needed for EM iterations.
+a \eqn{rp \times rp \times T}{rp x rp x T} matrix \code{PPm_smooth}, which is equal to the estimate of \eqn{Cov(F^{smooth}_t, F^{smooth}_{t-1} | T)}{Cov(F_smooth(t), F_smooth(t-1) | T)} and needed for EM iterations.
 See 'Property 6.3: The Lag-One Covariance Smoother' in Shumway & Stoffer (2017).
 }
 \description{

From d86f3b2e76abe9d84ec04cd23875bc291261ea94 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 00:16:50 +0200
Subject: [PATCH 04/21] Include mixed frequency example.

---
 vignettes/introduction.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index a94d777..b2741c9 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -165,7 +165,7 @@ BM14 use the approximation of Mariano and Murasawa (2003) to construct a monthly
 
 In the absence of such adjustments to the algorithm, or other methods of interpolating quarterly data, a very simple way to increase the weight of these series in the estimation is to duplicate them in the dataset. Such duplication can be mechanical (e.g. duplicate quarterly series 2 times in monthly dataset), but should ideally be based on considerations about the quality of the signal stemming from different quarterly series (i.e. informative series should be duplicated more).  -->
 
-```{r, eval = FALSE, include=FALSE}
+```{r}
 # Quarterly series from BM14
 head(BM14_Q, 3)
 # Pre-processing the data

From 022c581db61ea8dc0a9a8685f8cde6bae3f25558 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 00:29:52 +0200
Subject: [PATCH 05/21] Better.

---
 vignettes/introduction.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index b2741c9..a2bf47f 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -169,7 +169,7 @@ In the absence of such adjustments to the algorithm, or other methods of interpo
 # 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[, BM14_Models$log_trans[BM14_Models$freq == "Q"]] %<>% log()
 BM14_Q_diff <- diff(BM14_Q)
 # Merging to monthly data
 BM14_diff <- cbind(BM14_M_diff, BM14_Q_diff)

From f56b22eeb6b85cc1bcca015bc4e864eb00e264ae Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 00:47:01 +0200
Subject: [PATCH 06/21] Greater parsimony.

---
 R/dfms.R            | 2 +-
 man/dfms-package.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/dfms.R b/R/dfms.R
index b024c4c..f6aecf2 100644
--- a/R/dfms.R
+++ b/R/dfms.R
@@ -2,7 +2,7 @@
 #'
 #' @description
 #'
-#' *dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). The package has the following contents:
+#' *dfms* provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). Contents:
 #'
 #' **Information Criteria to Determine the Number of Factors**
 #'
diff --git a/man/dfms-package.Rd b/man/dfms-package.Rd
index 4146117..94cbee3 100644
--- a/man/dfms-package.Rd
+++ b/man/dfms-package.Rd
@@ -6,7 +6,7 @@
 \alias{dfms}
 \title{Dynamic Factor Models}
 \description{
-\emph{dfms} provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). The package has the following contents:
+\emph{dfms} provides efficient estimation of Dynamic Factor Models via the EM Algorithm --- following Doz, Giannone & Reichlin (2011, 2012) and Banbura & Modugno (2014). Contents:
 
 \strong{Information Criteria to Determine the Number of Factors}
 

From 3c724f8c0741ee2fb39981ffb321326147c35cf3 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 08:45:41 +0200
Subject: [PATCH 07/21] Minors.

---
 vignettes/introduction.Rmd | 38 ++++++++++++++++++--------------------
 1 file changed, 18 insertions(+), 20 deletions(-)

diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index a2bf47f..b640124 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -88,15 +88,15 @@ Estimation can then simply be done using the `DFM()` function with parameters `r
 
 ```{r}
 # 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)
+model_m <- DFM(BM14_M_diff, r = 4, p = 3)
+print(model_m)
+plot(model_m)
 ```
 
 The model can be investigated using `summary()`, which returns an object of class 'dfm_summary' containing the system matrices and summary statistics of the factors and the residuals in the measurement equation, as well as the R-Squared of the factor model for individual series. The print method automatically adjusts the amount of information printed to the data size. For large databases with more than 40 series, no series-level statistics are printed. 
 
 ```{r}
-dfm_summary <- summary(model1)
+dfm_summary <- summary(model_m)
 print(dfm_summary) # Large model with > 40 series: defaults to compact = 2
 
 # Can request more detailed printouts
@@ -107,8 +107,8 @@ print(dfm_summary) # Large model with > 40 series: defaults to compact = 2
 Apart from the model summary, the *dfm* methods `residuals()` and `fitted()` return observation residuals and fitted values from the model. The default format is a plain matrix, but the functions also have an argument to return data in the original (input) format.
 
 ```{r}
-plot(resid(model1, orig.format = TRUE))
-plot(fitted(model1, orig.format = TRUE))
+plot(resid(model_m, orig.format = TRUE))
+plot(fitted(model_m, orig.format = TRUE))
 ```
 
 
@@ -116,25 +116,25 @@ plot(fitted(model1, orig.format = TRUE))
 Another way to examine the factor model visually is to plot the Quasi-Maximum-Likelihood (QML) factor estimates against PCA and Two-Step estimates following Doz, Giannone and Reichlin (2011)^[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.], where the Kalman Filter and Smoother is run only once. Both estimates are also computed by `DFM()` during EM estimation and can also be visualized with `plot.dfm`.
 
 ```{r}
-plot(model1, method = "all", type = "individual")
+plot(model_m, method = "all", type = "individual")
 ```
 
-The plot with the various estimates shows that the QML estimates are more volatile in the initial periods where there are many missing series, but less volatile in the latter periods. In general, QML estimates may now always be superior across the entire data range to Two-Step and PCA estimates. Often, Two-Step estimates also provide similar forecasting performance, and are much faster to estimate using `DFM(BM14_M_diff, r = 4, p = 3, em.method = "none")`.  
+The plot with the various estimates shows that the QML estimates are more volatile in the initial periods where there are many missing series, but less volatile in the latter periods. In general, QML estimates may not always be superior across the entire data range to Two-Step and PCA estimates. Often, Two-Step estimates also provide similar forecasting performance, and are much faster to estimate using `DFM(BM14_M_diff, r = 4, p = 3, em.method = "none")`.  
 
 The factor estimates themselves can be extracted in a data frame using `as.data.frame()`, which also provides various options regarding the estimates retained and the format of the frame. It is also possible to add a time variable from the original data (the default is a sequence of integers).
 
 ```{r}
 # Default: all estimates in long format
-head(as.data.frame(model1, time = index(BM14_M_diff)))
+head(as.data.frame(model_m, time = index(BM14_M_diff)))
 ```
 
 ## Forecasting
 
-DFM forecasts can be obtained with the `predict()` method, which dynamically forecasts the factors using the transition equation (default 10 periods), and then also predicts data forecasts using the observation equation. Objects are of class 'dfm_forecast'
+DFM forecasts can be obtained with the `predict()` method, which dynamically forecasts the factors using the transition equation (default 10 periods), and then also predicts data forecasts using the observation equation. Objects are of class 'dfm_forecast'.
 
 ```{r}
 # 12-period ahead DFM forecast
-fc <- predict(model1, h = 12)
+fc <- predict(model_m, h = 12)
 print(fc)
 ```
 
@@ -142,12 +142,10 @@ These forecasts can also be visualized using a plot method. By default the entir
 ```{r}
 # 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))
 ```
+By default, `predict()` uses the QML factor estimates (if available). We can however also predict with PCA or TwoStep estimates using, e.g., `predict(model_m, h = 12, method = "2s")`. 
 
-
-The forecasts can also be retrieved in data frame using `as.data.frame()`. Again the method has various arguments to control the forecasts retained (factors, data or both, default factors), and the format of the frame. 
+The forecasts can be retrieved in data frame using `as.data.frame()`. Again the method has various arguments to control the output (factors, data, or both --- default factors) and the format of the frame. 
 
 ```{r}
 # Factor forecasts in wide format
@@ -156,7 +154,7 @@ head(as.data.frame(fc, pivot = "wide"))
 
 ## Estimation with Mixed Frequency
 
-Since v0.3.0 *dfms* allows monthly and quarterly mixed frequency estimation following Mariano & Murasawa (2003) and Banbura & Modugno (2014). Quarterly variables should be to the right of the monthly variables in the data matrix and need to be indicated using the `quarterly.vars` argument. Quarterly observations should be provided every 3rd period.
+Since v0.3.0, *dfms* allows monthly and quarterly mixed frequency estimation following Mariano & Murasawa (2003) and Banbura & Modugno (2014). Quarterly variables should be to the right of the monthly variables in the data matrix and need to be indicated using the `quarterly.vars` argument. Quarterly observations should be provided every 3rd period (months 3, 6, 9, and 12). Below, I estimate the mixed frequency DFM, adding a factor to capture any idiosynchratic dynamics in the quarterly series. 
 
 <!-- 
 The baseline algorithm of BM14, being designed for arbitrary patterns of missing data, allows lower frequency series to be included in the estimation. The problem here is that series at lower frequency contain many missing values, which effectively lets these series have less weight in the estimation.
@@ -172,12 +170,12 @@ head(BM14_Q, 3)
 BM14_Q[, BM14_Models$log_trans[BM14_Models$freq == "Q"]] %<>% log()
 BM14_Q_diff <- diff(BM14_Q)
 # Merging to monthly data
-BM14_diff <- cbind(BM14_M_diff, BM14_Q_diff)
+BM14_diff <- merge(BM14_M_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, quarterly.vars = colnames(BM14_Q))
-print(model2)
-plot(model2)
+model_mq <- DFM(BM14_diff, r = 5, p = 3, quarterly.vars = colnames(BM14_Q))
+print(model_mq)
+plot(model_mq)
 ```
 
 <!--

From 821e3152093847db28e71a2d0ccd8f852c1cfe9b Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 09:00:55 +0200
Subject: [PATCH 08/21] Add conclusion and outlook section.

---
 vignettes/introduction.Rmd | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index b640124..d709375 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -189,6 +189,14 @@ If performance is not critical, I also refer the user to the [*nowcasting*](<htt
 
 *dfms* also exports a matrix inverse and pseudo-inverse from the Armadillo C++ library through the functions `ainv()` and `apinv()`. These are often faster than `solve()`, and somewhat more robust in near-singularity cases. 
 
+## Conclusion and Outlook
+
+*dfms* provides a simple but robust and powerful implementation of dynamic factors models in R. For more information about the model consult the [theoretical vignette](https://raw.githubusercontent.com/SebKrantz/dfms/main/vignettes/dynamic_factor_models_paper.pdf). 
+
+Other implementations more geared to economic nowcasting applications are provided in R packages [*nowcasting*](<https://github.com/nmecsys/nowcasting>) and [*nowcastDFM*](<https://github.com/dhopp1/nowcastDFM>). More general forms of autoregressive state space models can be fit using [*MARSS*](<https://CRAN.R-project.org/package=MARSS>). For large-scale nowcasting models, the [`DynamicFactorMQ`](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQ.html) class in the *statsmodels* Python library provides a robust and performant implementation.
+
+In the future, a simple `news()` function following Banbura and Modugno (2014) may be added to *dfms* to evaluate the impact of new observations on model predictions. In general, my time on this package is very limited, but the original Matlab codes of Banbura and Modugno (2014) are [in the repo](https://github.com/SebKrantz/dfms/tree/main/misc/BM2014). Thus, impatient users are also very welcome to do advances and submit PRs.
+
 ## References
 
 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.

From 43d4d8f9a6a7ff6a244b455d25429dc124fb60b0 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 11:16:21 +0200
Subject: [PATCH 09/21] Minors.

---
 _pkgdown.yml               | 6 +++---
 vignettes/introduction.Rmd | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index dce20c7..244b8fb 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -93,10 +93,10 @@ external-articles:
 
 articles:
   - title: Introduction to dfms
-    desc: Provides a walk-through of all main features
+    desc: Introduces the package, including a walk-through of all main features.
     contents:
     - introduction
-  - title: Dynamic Factor Models - A Very Short Introduction
-    desc: Provides a short theoretical overview of dynamics factor models as used in the economics literature
+  - title: Dynamic Factor Models: A Very Short Introduction
+    desc: Provides a short overview of dynamics factor models as used in the economics literature.
     contents:
     - dynamic_factor_models
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index d709375..57c7e9f 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -143,6 +143,7 @@ These forecasts can also be visualized using a plot method. By default the entir
 # Setting an appropriate plot range to see the forecast
 plot(fc, xlim = c(320, 370))
 ```
+
 By default, `predict()` uses the QML factor estimates (if available). We can however also predict with PCA or TwoStep estimates using, e.g., `predict(model_m, h = 12, method = "2s")`. 
 
 The forecasts can be retrieved in data frame using `as.data.frame()`. Again the method has various arguments to control the output (factors, data, or both --- default factors) and the format of the frame. 

From 18355c57cfda83f7e3eb29cf8c4a457eba5062eb Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 11:17:13 +0200
Subject: [PATCH 10/21] Further minors.

---
 _pkgdown.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 244b8fb..c518869 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -87,7 +87,7 @@ reference:
 
 external-articles:
   - name: dynamic_factor_models
-    title: Dynamic Factor Models - A Very Short Introduction
+    title: Dynamic Factor Models: A Very Short Introduction
     description: " "
     href: https://raw.githubusercontent.com/SebKrantz/dfms/main/vignettes/dynamic_factor_models_paper.pdf
 

From cf6e4151c12ddff1a3acaa8e1b35d66460335901 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Tue, 13 May 2025 11:22:42 +0200
Subject: [PATCH 11/21] Need dash here.

---
 _pkgdown.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index c518869..ee385e6 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -87,7 +87,7 @@ reference:
 
 external-articles:
   - name: dynamic_factor_models
-    title: Dynamic Factor Models: A Very Short Introduction
+    title: Dynamic Factor Models - A Very Short Introduction
     description: " "
     href: https://raw.githubusercontent.com/SebKrantz/dfms/main/vignettes/dynamic_factor_models_paper.pdf
 
@@ -96,7 +96,7 @@ articles:
     desc: Introduces the package, including a walk-through of all main features.
     contents:
     - introduction
-  - title: Dynamic Factor Models: A Very Short Introduction
+  - title: Dynamic Factor Models - A Very Short Introduction
     desc: Provides a short overview of dynamics factor models as used in the economics literature.
     contents:
     - dynamic_factor_models

From f0661bed03604bab92933bdf18e4c0e97fdfa8b9 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sat, 17 May 2025 20:10:31 +0200
Subject: [PATCH 12/21] Minors.

---
 R/DFM.R    | 3 ++-
 man/DFM.Rd | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/R/DFM.R b/R/DFM.R
index 3b06ece..cae214f 100644
--- a/R/DFM.R
+++ b/R/DFM.R
@@ -11,7 +11,7 @@
 #' Efficient estimation of a Dynamic Factor Model via the EM Algorithm - on stationary data
 #' with time-invariant system matrices and classical assumptions, while permitting missing data.
 #'
-#' @param X a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values.
+#' @param X a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values. \emph{Note} that data is internally standardized (scaled and centered) before estimation.
 #' @param r integer. Number of factors.
 #' @param p integer. Number of lags in factor VAR.
 #' @param \dots (optional) arguments to \code{\link{tsnarmimp}}.
@@ -104,6 +104,7 @@
 #'  \item{\code{converged}}{single logical valued indicating whether the EM algorithm converged (within \code{max.iter} iterations subject to \code{tol}).}
 #'  \item{\code{anyNA}}{single logical valued indicating whether there were any (internal) missing values in the data (determined after removal of rows with too many missing values). If \code{FALSE}, \code{X_imp} is simply the original data in matrix form, and does not have the \code{"missing"} attribute attached.}
 #'  \item{\code{rm.rows}}{vector of any cases (rows) that were removed beforehand (subject to \code{max.missing} and \code{na.rm.method}). If no cases were removed the slot is \code{NULL}. }
+#'  \item{\code{quarterly.vars}}{names of the quarterly variables (if any).}
 #'  \item{\code{em.method}}{The EM method used.}
 #'  \item{\code{call}}{call object obtained from \code{match.call()}.}
 #'
diff --git a/man/DFM.Rd b/man/DFM.Rd
index ac311f4..c7207f1 100644
--- a/man/DFM.Rd
+++ b/man/DFM.Rd
@@ -22,7 +22,7 @@ DFM(
 )
 }
 \arguments{
-\item{X}{a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values.}
+\item{X}{a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values. \emph{Note} that data is internally standardized (scaled and centered) before estimation.}
 
 \item{r}{integer. Number of factors.}
 
@@ -85,6 +85,7 @@ A list-like object of class 'dfm' with the following elements:
 \item{\code{converged}}{single logical valued indicating whether the EM algorithm converged (within \code{max.iter} iterations subject to \code{tol}).}
 \item{\code{anyNA}}{single logical valued indicating whether there were any (internal) missing values in the data (determined after removal of rows with too many missing values). If \code{FALSE}, \code{X_imp} is simply the original data in matrix form, and does not have the \code{"missing"} attribute attached.}
 \item{\code{rm.rows}}{vector of any cases (rows) that were removed beforehand (subject to \code{max.missing} and \code{na.rm.method}). If no cases were removed the slot is \code{NULL}. }
+\item{\code{quarterly.vars}}{names of the quarterly variables (if any).}
 \item{\code{em.method}}{The EM method used.}
 \item{\code{call}}{call object obtained from \code{match.call()}.}
 }

From 8f8fbb6dd9c7e5de0c88e4e591813e724d85bb64 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sat, 17 May 2025 20:10:53 +0200
Subject: [PATCH 13/21] Add coef and logLik methods.

---
 R/methods.R | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/R/methods.R b/R/methods.R
index 78a1722..c3dc044 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -73,6 +73,13 @@ print.dfm <- function(x, digits = 4L, ...) {
   return(invisible(x))
 }
 
+#' @export
+coef.dfm <- function(object, ...) list(A = object$A, C = object$C)
+coefficients.dfm <- coef.dfm
+
+#' @export
+logLik.dfm <- function(object, ...) object$loglik[length(object$loglik)]
+
 #' @rdname summary.dfm
 #' @param method character. The factor estimates to use: one of \code{"qml"}, \code{"2s"} or \code{"pca"}.
 #' @param \dots not used.

From c6d08d7b648a4f232a7a19f9c6c7f38fc75f8526 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sat, 17 May 2025 20:10:53 +0200
Subject: [PATCH 14/21] Add coef and logLik methods.

---
 NAMESPACE   | 2 ++
 R/methods.R | 7 +++++++
 2 files changed, 9 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index cf878dc..ac182c6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,7 +2,9 @@
 
 S3method(as.data.frame,dfm)
 S3method(as.data.frame,dfm_forecast)
+S3method(coef,dfm)
 S3method(fitted,dfm)
+S3method(logLik,dfm)
 S3method(plot,ICr)
 S3method(plot,dfm)
 S3method(plot,dfm_forecast)
diff --git a/R/methods.R b/R/methods.R
index 78a1722..c3dc044 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -73,6 +73,13 @@ print.dfm <- function(x, digits = 4L, ...) {
   return(invisible(x))
 }
 
+#' @export
+coef.dfm <- function(object, ...) list(A = object$A, C = object$C)
+coefficients.dfm <- coef.dfm
+
+#' @export
+logLik.dfm <- function(object, ...) object$loglik[length(object$loglik)]
+
 #' @rdname summary.dfm
 #' @param method character. The factor estimates to use: one of \code{"qml"}, \code{"2s"} or \code{"pca"}.
 #' @param \dots not used.

From 30cc1ced5805ae8bf3440c72aeed10a30f78e41c Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:13:45 +0200
Subject: [PATCH 15/21] Additional details.

---
 R/DFM.R    | 6 ++++--
 man/DFM.Rd | 5 +++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/R/DFM.R b/R/DFM.R
index cae214f..25f3a59 100644
--- a/R/DFM.R
+++ b/R/DFM.R
@@ -14,7 +14,7 @@
 #' @param X a \code{T x n} numeric data matrix or frame of stationary time series. May contain missing values. \emph{Note} that data is internally standardized (scaled and centered) before estimation.
 #' @param r integer. Number of factors.
 #' @param p integer. Number of lags in factor VAR.
-#' @param \dots (optional) arguments to \code{\link{tsnarmimp}}.
+#' @param \dots (optional) arguments to \code{\link{tsnarmimp}}. The default settings impute internal missing values with a cubic spline and the edges with the median and a 3-period moving average.
 #' @param idio.ar1 logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generaly not needed if \code{n} is large (>30). See theoretical vignette for details.
 #' @param quarterly.vars character. Names of quarterly variables in \code{X} (if any). Monthly variables should be to the left of the quarterly variables in the data matrix and quarterly observations should be provided every 3rd period.
 #' @param rQ character. Restrictions on the state (transition) covariance matrix (Q).
@@ -77,9 +77,11 @@
 #'  \eqn{\textbf{R}}{R} \tab\tab \eqn{n \times n}{n x n} observation covariance matrix. It is diagonal by assumption 2 and identical to \eqn{\textbf{R}}{R} as stated in the dynamic form.\cr\cr
 #' }
 #  that \eqn{E[\textbf{f}_t|\textbf{F}_{t-1}] = E[\textbf{f}_t|\textbf{f}_{t-1}] = \textbf{A}_1 \textbf{f}_{t-1}}{E[f(t)|F(t-1)] = E[f(t)|f(t-1)] = A1 f(t-1)} (all relationships between lagged factors are captured in \eqn{\textbf{A}_1}{A1}).\cr\cr
+#' The filter is initialized with PCA estimates on the imputed dataset---see \code{\link{SKFS}} for a complete code example.
+#'
 #'
 #' @returns A list-like object of class 'dfm' with the following elements:
-#'  \item{\code{X_imp}}{\eqn{T \times n}{T x n} matrix with the imputed and standardized (scaled and centered) data - with attributes attached allowing reconstruction of the original data:
+#'  \item{\code{X_imp}}{\eqn{T \times n}{T x n} matrix with the imputed and standardized (scaled and centered) data---after applying \code{\link{tsnarmimp}}. It has attributes attached allowing for reconstruction of the original data:
 #'  \tabular{llll}{
 #'      \code{"stats"} \tab\tab is a \eqn{n \times 5}{n x 5} matrix of summary statistics of class \code{"qsu"} (see \code{\link[collapse]{qsu}}).\cr\cr
 #'      \code{"missing"} \tab\tab is a \eqn{T \times n}{T x n} logical matrix indicating missing or infinite values in the original data (which are imputed in \code{X_imp}).\cr\cr
diff --git a/man/DFM.Rd b/man/DFM.Rd
index c7207f1..2ea2aaf 100644
--- a/man/DFM.Rd
+++ b/man/DFM.Rd
@@ -28,7 +28,7 @@ DFM(
 
 \item{p}{integer. Number of lags in factor VAR.}
 
-\item{\dots}{(optional) arguments to \code{\link{tsnarmimp}}.}
+\item{\dots}{(optional) arguments to \code{\link{tsnarmimp}}. The default settings impute internal missing values with a cubic spline and the edges with the median and a 3-period moving average.}
 
 \item{idio.ar1}{logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generaly not needed if \code{n} is large (>30). See theoretical vignette for details.}
 
@@ -60,7 +60,7 @@ This yields significant performance gains over the iterative methods. Final syst
 }
 \value{
 A list-like object of class 'dfm' with the following elements:
-\item{\code{X_imp}}{\eqn{T \times n}{T x n} matrix with the imputed and standardized (scaled and centered) data - with attributes attached allowing reconstruction of the original data:
+\item{\code{X_imp}}{\eqn{T \times n}{T x n} matrix with the imputed and standardized (scaled and centered) data---after applying \code{\link{tsnarmimp}}. It has attributes attached allowing for reconstruction of the original data:
 \tabular{llll}{
 \code{"stats"} \tab\tab is a \eqn{n \times 5}{n x 5} matrix of summary statistics of class \code{"qsu"} (see \code{\link[collapse]{qsu}}).\cr\cr
 \code{"missing"} \tab\tab is a \eqn{T \times n}{T x n} logical matrix indicating missing or infinite values in the original data (which are imputed in \code{X_imp}).\cr\cr
@@ -134,6 +134,7 @@ where
 \eqn{\textbf{Q}}{Q} \tab\tab \eqn{rp \times rp}{rp x rp} state covariance matrix. The top \eqn{r \times r}{r x r} part gives the contemporaneous relationships, the rest are zeros by assumption 4.\cr\cr
 \eqn{\textbf{R}}{R} \tab\tab \eqn{n \times n}{n x n} observation covariance matrix. It is diagonal by assumption 2 and identical to \eqn{\textbf{R}}{R} as stated in the dynamic form.\cr\cr
 }
+The filter is initialized with PCA estimates on the imputed dataset---see \code{\link{SKFS}} for a complete code example.
 }
 \examples{
 library(magrittr)

From 6a3cedf443115f36a336567b139a4fe64cd0bf7f Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:14:07 +0200
Subject: [PATCH 16/21] Also export coefficients.dfm.

---
 NAMESPACE   | 1 +
 R/methods.R | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index ac182c6..41f0f80 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -3,6 +3,7 @@
 S3method(as.data.frame,dfm)
 S3method(as.data.frame,dfm_forecast)
 S3method(coef,dfm)
+S3method(coefficients,dfm)
 S3method(fitted,dfm)
 S3method(logLik,dfm)
 S3method(plot,ICr)
diff --git a/R/methods.R b/R/methods.R
index c3dc044..37e58d7 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -75,6 +75,8 @@ print.dfm <- function(x, digits = 4L, ...) {
 
 #' @export
 coef.dfm <- function(object, ...) list(A = object$A, C = object$C)
+
+#' @export
 coefficients.dfm <- coef.dfm
 
 #' @export

From 9439daaee323c4a928460a35bbd8d36cbd7d7110 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:14:23 +0200
Subject: [PATCH 17/21] Ignore docs.

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 25e33b2..5dd5866 100644
--- a/.gitignore
+++ b/.gitignore
@@ -53,3 +53,4 @@ vignettes/*.pdf
 .Rproj.user
 # docs
 inst/doc
+docs

From 1e5923c92c868f2f8d5100d2fe8b1d34cfe38e1c Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:39:32 +0200
Subject: [PATCH 18/21] Spelling.

---
 R/DFM.R    | 2 +-
 man/DFM.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/DFM.R b/R/DFM.R
index 25f3a59..ab148cc 100644
--- a/R/DFM.R
+++ b/R/DFM.R
@@ -15,7 +15,7 @@
 #' @param r integer. Number of factors.
 #' @param p integer. Number of lags in factor VAR.
 #' @param \dots (optional) arguments to \code{\link{tsnarmimp}}. The default settings impute internal missing values with a cubic spline and the edges with the median and a 3-period moving average.
-#' @param idio.ar1 logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generaly not needed if \code{n} is large (>30). See theoretical vignette for details.
+#' @param idio.ar1 logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generally not needed if \code{n} is large (>30). See theoretical vignette for details.
 #' @param quarterly.vars character. Names of quarterly variables in \code{X} (if any). Monthly variables should be to the left of the quarterly variables in the data matrix and quarterly observations should be provided every 3rd period.
 #' @param rQ character. Restrictions on the state (transition) covariance matrix (Q).
 #' @param rR character. Restrictions on the observation (measurement) covariance matrix (R).
diff --git a/man/DFM.Rd b/man/DFM.Rd
index 2ea2aaf..f316666 100644
--- a/man/DFM.Rd
+++ b/man/DFM.Rd
@@ -30,7 +30,7 @@ DFM(
 
 \item{\dots}{(optional) arguments to \code{\link{tsnarmimp}}. The default settings impute internal missing values with a cubic spline and the edges with the median and a 3-period moving average.}
 
-\item{idio.ar1}{logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generaly not needed if \code{n} is large (>30). See theoretical vignette for details.}
+\item{idio.ar1}{logical. Model observation errors as AR(1) processes: \eqn{e_t = \rho e_{t-1} + v_t}{e(t) = rho e(t-1) + v(t)}. \emph{Note} that this substantially increases computation time, and is generally not needed if \code{n} is large (>30). See theoretical vignette for details.}
 
 \item{quarterly.vars}{character. Names of quarterly variables in \code{X} (if any). Monthly variables should be to the left of the quarterly variables in the data matrix and quarterly observations should be provided every 3rd period.}
 

From c2ef6fbe8a55929a1ba82274a28bb83a9db0fa25 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:49:48 +0200
Subject: [PATCH 19/21] Ignore HTML README.

---
 .Rbuildignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.Rbuildignore b/.Rbuildignore
index abced86..f9af947 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,6 +1,7 @@
 ^dfms\.Rproj$
 ^\.Rproj\.user$
 misc
+^README\.html$
 .RData
 LICENSE
 ^\.github$

From 1778744a960d8a462290bbf5cab1cd53cd601560 Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 00:54:19 +0200
Subject: [PATCH 20/21] Remove coefficients.

---
 NAMESPACE   | 1 -
 R/methods.R | 3 ---
 2 files changed, 4 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 41f0f80..ac182c6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -3,7 +3,6 @@
 S3method(as.data.frame,dfm)
 S3method(as.data.frame,dfm_forecast)
 S3method(coef,dfm)
-S3method(coefficients,dfm)
 S3method(fitted,dfm)
 S3method(logLik,dfm)
 S3method(plot,ICr)
diff --git a/R/methods.R b/R/methods.R
index 37e58d7..971aa0c 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -76,9 +76,6 @@ print.dfm <- function(x, digits = 4L, ...) {
 #' @export
 coef.dfm <- function(object, ...) list(A = object$A, C = object$C)
 
-#' @export
-coefficients.dfm <- coef.dfm
-
 #' @export
 logLik.dfm <- function(object, ...) object$loglik[length(object$loglik)]
 

From 29b9beb238ff39d528657da81884d2012946191d Mon Sep 17 00:00:00 2001
From: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>
Date: Sun, 18 May 2025 01:07:15 +0200
Subject: [PATCH 21/21] Add coef() and logLik() to summary Rd page.

---
 R/methods.R        |  8 +++++---
 man/summary.dfm.Rd | 12 +++++++++---
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/R/methods.R b/R/methods.R
index 971aa0c..30ebfe2 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -43,8 +43,8 @@
 #'
 #' @title DFM Summary Methods
 #'
-#' @description Summary and print methods for class 'dfm'. \code{print.dfm} just prints basic model information and the factor transition matrix \eqn{\textbf{A}}{A},
-#' \code{summary.dfm} returns all system matrices and additional residual and goodness of fit statistics - with a print method allowing full or compact printout.
+#' @description Summary and print methods for class 'dfm'. \code{print.dfm} just prints basic model information and the factor transition matrix \eqn{\textbf{A}}{A}, \code{coef.dfm} returns \eqn{\textbf{A}}{A} and \eqn{\textbf{C}}{C} in a plain list, whereas
+#' \code{summary.dfm} returns all system matrices and additional residual and goodness of fit statistics---with a print method allowing full or compact printout.
 #'
 #' @param x,object an object class 'dfm'.
 #' @param digits integer. The number of digits to print out.
@@ -73,16 +73,18 @@ print.dfm <- function(x, digits = 4L, ...) {
   return(invisible(x))
 }
 
+#' @rdname summary.dfm
 #' @export
 coef.dfm <- function(object, ...) list(A = object$A, C = object$C)
 
+#' @rdname summary.dfm
 #' @export
 logLik.dfm <- function(object, ...) object$loglik[length(object$loglik)]
 
 #' @rdname summary.dfm
 #' @param method character. The factor estimates to use: one of \code{"qml"}, \code{"2s"} or \code{"pca"}.
 #' @param \dots not used.
-#' @return Summary information following a dynamic factor model estimation.
+#' @return Summary information following a dynamic factor model estimation. \code{coef()} returns \eqn{\textbf{A}}{A} and \eqn{\textbf{C}}{C}.
 #' @importFrom stats cov
 #' @importFrom collapse pwcov
 #' @export
diff --git a/man/summary.dfm.Rd b/man/summary.dfm.Rd
index 9d1be87..8e88ac9 100644
--- a/man/summary.dfm.Rd
+++ b/man/summary.dfm.Rd
@@ -4,10 +4,16 @@
 \alias{summary.dfm}
 \alias{print.dfm}
 \alias{print.dfm_summary}
+\alias{coef.dfm}
+\alias{logLik.dfm}
 \title{DFM Summary Methods}
 \usage{
 \method{print}{dfm}(x, digits = 4L, ...)
 
+\method{coef}{dfm}(object, ...)
+
+\method{logLik}{dfm}(object, ...)
+
 \method{summary}{dfm}(object, method = switch(object$em.method, none = "2s", "qml"), ...)
 
 \method{print}{dfm_summary}(x, digits = 4L, compact = sum(x$info["n"] > 15, x$info["n"] > 40), ...)
@@ -24,11 +30,11 @@
 \item{compact}{integer. Display a more compact printout: \code{0} prints everything, \code{1} omits the observation matrix \eqn{\textbf{C}}{C} and residual covariance matrix \code{cov(resid(model))}, and \code{2} omits all disaggregated information on the input data. Sensible default are chosen for different sizes of the input dataset so as to limit large printouts.}
 }
 \value{
-Summary information following a dynamic factor model estimation.
+Summary information following a dynamic factor model estimation. \code{coef()} returns \eqn{\textbf{A}}{A} and \eqn{\textbf{C}}{C}.
 }
 \description{
-Summary and print methods for class 'dfm'. \code{print.dfm} just prints basic model information and the factor transition matrix \eqn{\textbf{A}}{A},
-\code{summary.dfm} returns all system matrices and additional residual and goodness of fit statistics - with a print method allowing full or compact printout.
+Summary and print methods for class 'dfm'. \code{print.dfm} just prints basic model information and the factor transition matrix \eqn{\textbf{A}}{A}, \code{coef.dfm} returns \eqn{\textbf{A}}{A} and \eqn{\textbf{C}}{C} in a plain list, whereas
+\code{summary.dfm} returns all system matrices and additional residual and goodness of fit statistics---with a print method allowing full or compact printout.
 }
 \examples{
 mod = DFM(diff(BM14_Q), 2, 3)