Review references

.Rbuildignore

@@ -5,3 +5,7 @@
 ^doc$
 ^Meta$
 ^\.github$
+^CRAN-SUBMISSION$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$

.gitignore

@@ -4,3 +4,4 @@ inst/doc
 .Rprofile
 /doc/
 /Meta/
+docs

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 1.0.0
+Date: 2026-02-17 20:27:31 UTC
+SHA: 9ff062df4af4e5572f66abfeca5e27fa2f7b3886

DESCRIPTION

@@ -1,8 +1,10 @@
 Package: csdm
 Title: Cross-Sectional Dependence Models
-Version: 1.0.0
+Version: 1.0.1
 Depends: R (>= 4.0.0)
-Imports: MASS
+Imports: MASS,
+    Rdpack
+RdMacros: Rdpack
 Suggests:
     testthat (>= 3.0.0),
     knitr,
@@ -14,7 +16,10 @@ Authors@R:
     person(given = c("Joao", "Claudio"), 
           family = "Macosso",
           email = "joaoclaudiomacosso@gmail.com",
-          role = c("aut", "cre"))
+          role = c("aut", "cre"),
+          comment = c(ORCID = "0009-0006-5051-9312"))
+URL: https://github.com/Macosso/csdm
+BugReports: https://github.com/Macosso/csdm/issues
 Description: Provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics.
 License: GPL-3
 Encoding: UTF-8

NAMESPACE

@@ -22,6 +22,7 @@ export(get_residuals)
 export(pooled_vcov)
 export(prepare_cd_input)
 export(sandwich_vcov)
+importFrom(Rdpack,reprompt)
 importFrom(stats,aggregate)
 importFrom(stats,cor)
 importFrom(stats,model.matrix.lm)

NEWS.md

@@ -11,3 +11,16 @@
 ### Inference and diagnostics
 - Cross-sectional dependence (CD) tests
 - Summary and printing methods
+
+
+# csdm 1.0.1
+
+## Documentation and References enhancement
+
+### References
+- Added references for the implemented estimators and methods, including key papers and textbooks in the field
+
+### Documentation
+- Improved documentation for all functions, including detailed descriptions of the estimators, their assumptions, and
+- Ensured consistency in the documentation style across different documents
+- Added link for reporting bugs

R/csdm.R

@@ -1,12 +1,13 @@
 # csdm.R
 
-#' Panel Model Estimation with Cross Section Dependence
+#' Panel Model Estimation with Cross-Sectional Dependence
 #'
-#' Estimate panel data models that allow for cross-sectional dependence and
-#' heterogeneous slopes. The interface supports Mean Group (MG), Common
-#' Correlated Effects (CCE), Dynamic CCE (DCCE), and Cross-Sectionally
-#' Augmented ARDL (CS-ARDL) estimators with consistent handling of
-#' cross-sectional averages, dynamic structure, and robust inference.
+#' Estimate heterogeneous panel data models with optional cross-sectional
+#' augmentation and dynamic structure. The interface supports Mean Group (MG),
+#' Common Correlated Effects (CCE), Dynamic CCE (DCCE), and
+#' Cross-Sectionally Augmented ARDL (CS-ARDL) estimators with a consistent
+#' specification workflow for cross-sectional averages, lag structure, and
+#' variance-covariance estimation.
 #'
 #' @param formula Model formula of the form \code{y ~ x1 + x2}.
 #' @param data A \code{data.frame} (or \code{plm::pdata.frame}) containing the
@@ -29,59 +30,120 @@
 #'
 #' @return An object of class \code{csdm_fit} containing estimated coefficients,
 #'   residuals, variance-covariance estimates, model metadata, and diagnostics.
-#'   Use \code{summary()}, \code{coef()}, \code{residuals()}, \code{vcov()}, and
-#'   \code{cd_test()} to access standard outputs.
+#'   Use [summary()], [coef()], [residuals()], [vcov()], and
+#'   [cd_test()] to access standard outputs.
 #'
 #' @details
-#' ## Model equations
-#'
-#' \describe{
-#'   \item{MG (Pesaran and Smith, 1995)}{
-#'     \deqn{y_{it} = x_{it}^\top \beta_i + u_{it}}
-#'   }
-#'   \item{CCE (Pesaran, 2006)}{
-#'     \deqn{y_{it} = x_{it}^\top \beta_i + \lambda_i^\top F_t + u_{it}}
-#'   }
-#'   \item{DCCE (Chudik and Pesaran, 2015)}{
-#'     \deqn{\Delta y_{it} = \Delta x_{it}^\top \beta_i + \lambda_i^\top \Delta F_t + u_{it}}
-#'   }
-#'   \item{CS-ARDL (Chudik and Pesaran, 2015)}{
-#'     \deqn{y_{it} = \phi_i y_{it-1} + x_{it}^\top \theta_i + \lambda_i^\top F_t + u_{it}}
-#'   }
-#' }
-#'
-#' ## Estimation, identification, and assumptions
-#'
-#' \describe{
-#'   \item{MG}{Unit-by-unit estimation with heterogeneous slopes. The reported
-#'   coefficients are cross-sectional averages of unit estimates. Requires
-#'   sufficient time series per unit and weak serial dependence in errors.}
-#'   \item{CCE}{Augments regressions with cross-sectional averages (CSA) to proxy
-#'   unobserved common factors. Identification relies on large N and T, weak
-#'   dependence in idiosyncratic errors after CSA, and weak exogeneity of
-#'   regressors.}
-#'   \item{DCCE}{Extends CCE to dynamic settings with lagged dependent variables
-#'   and CSA lags. Identification relies on weak exogeneity, adequate time length
-#'   for dynamic lags, and a stable factor structure.}
-#'   \item{CS-ARDL}{Specifies dynamic distributed lags with CSA terms. Estimation
-#'   follows ARDL-style dynamics in each unit and aggregates to panel averages.
-#'   Assumes weak exogeneity and sufficient time length for lag structure.}
-#' }
+#' Let \eqn{i = 1, \ldots, N} index cross-sectional units and
+#' \eqn{t = 1, \ldots, T} index time. A baseline heterogeneous panel model is
 #'
-#' @references
-#' Pesaran, M.H. and Smith, R. (1995). "Estimating long-run relationships from
-#' dynamic heterogeneous panels." Journal of Econometrics, 68(1), 79-113.
+#' \deqn{y_{it} = \alpha_i + \beta_i^T x_{it} + u_{it}.}
+#'
+#' Here \eqn{\alpha_i} is a unit-specific intercept, \eqn{x_{it}} is a vector
+#' of regressors, \eqn{\beta_i} is a vector of unit-specific slopes, and
+#' \eqn{u_{it}} is an error term that may exhibit cross-sectional dependence.
+#'
+#' Cross-sectional averages are specified through [csdm_csa()] and dynamic or
+#' long-run structure is specified through [csdm_lr()]. This keeps the model
+#' interface consistent across estimators while allowing the degree of
+#' cross-sectional augmentation and lag structure to vary by application.
+#'
+#' \strong{Implemented estimators}
+#'
+#' \strong{MG (Pesaran and Smith, 1995)}
+#'
+#' The Mean Group estimator fits separate regressions for each unit and averages
+#' the resulting coefficients:
+#'
+#' \deqn{\hat{\beta}_{MG} = \frac{1}{N}\sum_{i=1}^N \hat{\beta}_i.}
+#'
+#' This estimator accommodates slope heterogeneity but does not explicitly model
+#' cross-sectional dependence.
+#'
+#' \strong{CCE (Pesaran, 2006)}
+#'
+#' Regressions are augmented with cross-sectional averages to proxy unobserved
+#' common factors:
+#'
+#' \deqn{y_{it} = \alpha_i + \beta_i^T x_{it} + \gamma_i^T \bar{z}_{t} + v_{it}.}
+#'
+#' A common choice is
+#'
+#' \deqn{\bar{z}_t = (\bar{y}_t, \bar{x}_t),}
+#'
+#' with
+#'
+#' \deqn{\bar{x}_t = \frac{1}{N}\sum_{i=1}^N x_{it}, \qquad
+#' \bar{y}_t = \frac{1}{N}\sum_{i=1}^N y_{it}.}
+#'
+#' More generally, \eqn{\bar{z}_t} collects the cross-sectional averages
+#' specified in \code{csa}.
+#'
+#' \strong{DCCE (Chudik and Pesaran, 2015)}
 #'
-#' Pesaran, M.H. (2006). "Estimation and inference in large heterogeneous panels
-#' with multifactor error structure." Econometrica, 74(4), 967-1012.
+#' Dynamic CCE extends CCE by allowing lagged dependent variables and lagged
+#' cross-sectional averages:
+#'
+#' \deqn{y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p}
+#' + \beta_i^T x_{it}
+#' + \sum_{q=0}^{Q} \delta_{iq}^T \bar{z}_{t-q}
+#' + e_{it}.}
+#'
+#' In the package implementation, lagged dependent variables and distributed
+#' lags of regressors are controlled through \code{lr}, while contemporaneous
+#' and lagged cross-sectional averages are controlled through \code{csa}.
+#'
+#' \strong{CS-ARDL (Chudik and Pesaran, 2015)}
+#'
+#' In the package implementation, \code{model = "cs_ardl"} is obtained by first
+#' estimating a cross-sectionally augmented ARDL-style regression in levels,
+#' using the same dynamic specification as \code{model = "dcce"}, and then
+#' transforming the unit-specific coefficients into adjustment and long-run
+#' parameters.
+#'
+#' The underlying unit-level regression is of the form
+#'
+#' \deqn{y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p}
+#' + \sum_{q=0}^{Q} \beta_{iq}^T x_{i,t-q}
+#' + \sum_{s=0}^{S} \omega_{is}^T \bar{z}_{t-s}
+#' + e_{it}.}
+#'
+#' From this dynamic specification, the package recovers the implied
+#' error-correction form
+#'
+#' \deqn{\Delta y_{it} =
+#' \alpha_i +
+#' \varphi_i \left(y_{i,t-1} - \theta_i^T x_{i,t-1}\right)
+#' + \sum_{j=1}^{P-1} \lambda_{ij} \Delta y_{i,t-j}
+#' + \sum_{j=0}^{Q-1} \psi_{ij}^T \Delta x_{i,t-j}
+#' + \sum_{s=0}^{S} \tilde{\omega}_{is}^T \bar{z}_{t-s}
+#' + e_{it},}
+#'
+#' where \eqn{\varphi_i} is the adjustment coefficient and \eqn{\theta_i} is
+#' the implied long-run relationship. In the current implementation, these
+#' quantities are computed from the estimated lag polynomials rather than from a
+#' direct ECM regression.
+#'
+#' \strong{Identification and assumptions}
+#'
+#' MG requires sufficient time-series variation within each unit.
+#'
+#' CCE relies on cross-sectional averages acting as proxies for latent common
+#' factors, together with adequate cross-sectional and time dimensions.
+#'
+#' DCCE additionally requires enough time periods to support lagged dependent
+#' variables, distributed lags, and lagged cross-sectional averages.
+#'
+#' CS-ARDL requires sufficient time length for the distributed-lag structure and
+#' is intended for applications where both short-run dynamics and long-run
+#' relationships are of interest in the presence of common factors.
+#'
+#' @references
+#' \insertRef{PesaranSmith1995}{csdm}
 #'
-#' Chudik, A. and Pesaran, M.H. (2015). "Common correlated effects estimation of
-#' heterogeneous dynamic panel data models with weakly exogenous regressors."
-#' Journal of Econometrics, 188(2), 393-420.
+#' \insertRef{Pesaran2006}{csdm}
 #'
-#' Chudik, A. and Pesaran, M.H. (2015). "Large panel data models with
-#' cross-sectional dependence: A survey." Annals of Economics and Finance, 16(1),
-#' 53-78.
+#' \insertRef{ChudikPesaran2015a}{csdm}
 #'
 #' @examples
 #' library(csdm)

R/data.R

@@ -1,13 +1,13 @@
-#' Penn World Tables panel (93 countries, 1960–2007)
+#' Penn World Tables panel (93 countries, 1960-2007)
 #'
-#' A panel of 93 countries (unit id) observed annually over 1960–2007 (time/year),
+#' A panel of 93 countries (unit id) observed annually over 1960-2007 (time/year),
 #' with the log-transformed variables used in 
 #' xtdcce2-style examples.
 #'
 #' @format A data frame with 4464 rows and 6 variables:
 #' \describe{
 #'   \item{id}{Unit identifier (country id).}
-#'   \item{year}{Time identifier (year, 1960–2007).}
+#'   \item{year}{Time identifier (year, 1960-2007).}
 #'   \item{log_rgdpo}{Log real GDP (output).}
 #'   \item{log_hc}{Log human capital index.}
 #'   \item{log_ck}{Log capital stock.}

R/utils_cd.R

@@ -53,21 +53,15 @@
 #' }
 #'
 #' @references
-#' Pesaran, M.H. (2015). "Testing weak cross-sectional dependence in large panels."
-#' \emph{Econometric Reviews}, 34(6-10), 1089-1117.
+#' \insertRef{Pesaran2015}{csdm}
 #'
-#' Pesaran, M.H. (2021). "General diagnostic tests for cross-sectional dependence
-#' in panels." \emph{Empirical Economics}, 60, 13-50.
+#' \insertRef{Pesaran2021}{csdm}
 #'
-#' Juodis, A., & Reese, S. (2021). "The incidental parameters problem in testing for
-#' remaining cross-sectional correlation." \emph{Journal of Business and Economic Statistics},
-#' 40(3), 1193-1203.
+#' \insertRef{JuodisReese2021}{csdm}
 #'
-#' Fan, J., Liao, Y., & Yao, J. (2015). "Power Enhancement in High-Dimensional
-#'   Cross-Sectional Tests." \emph{Econometric Reviews}, 34(6-10), 742-779.
+#' \insertRef{FanLiaoYao2015}{csdm}
 #'
-#' Pesaran, M.H., & Xie, Y. (2021). "A bias-corrected CD test for error cross-sectional
-#'   dependence in panel models." \emph{Econometric Reviews}, 41(6), 649-677.
+#' \insertRef{PesaranXie2021}{csdm}
 #'
 #' @examples
 #' # Simulate independent and dependent panels

R/zzz_imports.R

@@ -1,2 +1,3 @@
 #' @importFrom stats aggregate cor model.matrix.lm pt residuals sd setNames symnum terms
+#' @importFrom Rdpack reprompt
 NULL