Package {csdm}

Title:	Cross-Sectional Dependence Models
Version:	2.0.0
Depends:	R (≥ 4.0.0)
Imports:	Rdpack, generics, tibble
RdMacros:	Rdpack
Suggests:	testthat (≥ 3.0.0), covr, knitr, rmarkdown, spelling, broom, sandwich, plm, lmtest, modelsummary
URL:	https://macosso.github.io/csdm/, 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
LazyData:	true
Config/testthat/edition:	3
VignetteBuilder:	knitr
Language:	en-US
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-09-22 19:11:01 UTC; Joaoc
Author:	Joao Claudio Macosso [aut, cre]
Maintainer:	Joao Claudio Macosso <joaoclaudiomacosso@gmail.com>
Repository:	CRAN
Date/Publication:	2026-09-22 19:40:02 UTC

Penn World Tables panel (93 countries, 1960-2007)

Description

A panel of 93 countries (unit id) observed annually over 1960-2007 (time/year), with the log-transformed variables used in xtdcce2-style examples.

Usage

PWT_60_07

Format

A data frame with 4464 rows and 6 variables:

id

Unit identifier (country id).

year

Time identifier (year, 1960-2007).

log_rgdpo

Log real output.

log_hc

Log human capital index.

log_ck

Log physical capital.

log_ngd

Log population growth plus a 5 percent break-even investment rate.

Source

Penn World Table 8 example data distributed with Stata's xtdcce2. The variable descriptions follow the accompanying xtdcce2 documentation.

Cross-sectional dependence (CD) tests for panel residuals

Description

Computes Pesaran CD, CDw, CDw+, and CD* tests for cross-sectional dependence in panel residuals. The implementation supports residual matrices or fitted csdm_fit objects and provides consistent handling of unbalanced panels.

Usage

cd_test(object, ...)

## Default S3 method:
cd_test(
  object,
  type = c("CD", "CDw", "CDw+", "CDstar", "all"),
  n_pc = 4L,
  seed = NULL,
  min_overlap = 2L,
  na.action = c("pairwise", "drop.incomplete.times"),
  ...
)

## S3 method for class 'csdm_fit'
cd_test(
  object,
  type = c("CD", "CDw", "CDw+", "CDstar", "all"),
  n_pc = 4L,
  seed = NULL,
  min_overlap = 2L,
  na.action = c("pairwise", "drop.incomplete.times"),
  ...
)

## S3 method for class 'cd_test'
print(x, digits = 3, ...)

Arguments

Details

Notation

Let E be the residual matrix with N cross-sectional units and T time periods. For each unit pair (i,j), let T_{ij} be the number of overlapping time periods and \rho_{ij} the pairwise correlation.

Test statistics

CD (Pesaran, 2015)

CD = \sqrt{\frac{2}{N(N-1)}} \sum_{i<j} \sqrt{T_{ij}} \, \rho_{ij}

CDw (Juodis and Reese, 2022)

Independent Rademacher weights w_i \in \{-1,1\} are applied by unit. For a balanced residual panel, the statistic is

CD_W = \left(\frac{1}{NT}\sum_{i,t}w_i^2 e_{it}^2\right)^{-1} \sqrt{\frac{2}{TN(N-1)}} \sum_t\sum_{i<j}w_i e_{it}w_j e_{jt}.

The first factor is the inverse pooled residual variance. One set of random weights is drawn per call; use seed for reproducibility.

CDw+ (Juodis and Reese, 2022; Fan, Liao, and Yao, 2015)

The power-enhanced statistic is

CD_{W+} = CD_W + \sum_{i<j}|\rho_{ij}| 1\left\{|\rho_{ij}| > 2\sqrt{\log(N)/T}\right\}.

Here \rho_{ij} is the ordinary residual correlation, without multiplication by \sqrt{T}. The nonnegative screening term is asymptotically zero under the conditions of the null hypothesis.

CD* (Pesaran and Xie, 2021)

CD is computed on residuals after removing n_pc principal components from E. This provides a bias-corrected test under multifactor errors.

CD* requires a nondegenerate bias-correction denominator. Near-zero denominators can produce severe size distortions, including proportional loading/error-scale designs after standardization. Numerical rank checks do not establish the validity of the asymptotic approximation.

Missing data and balance

Time periods containing no finite residual for any retained unit are outside the effective residual sample and are always removed before balance is assessed. Partially observed periods are handled according to na.action.

CD

Uses pairwise-complete observations by default. Each pairwise correlation uses available overlaps.

CDw, CDw+

Require a balanced sample; explicitly select complete times if desired.

CD*

Requires a balanced panel. Explicitly setting na.action = "drop.incomplete.times" removes any time period with missing observations. With na.action = "pairwise", CD* returns NA and a warning when missing values are present.

Value

An object of class cd_test with fields tests, type, N, T, na.action, excluded_units, excluded_times, kept_times, and call. The tests list contains one or more test results, each with statistic and p.value.

References

Pesaran MH (2015). “Testing weak cross-sectional dependence in large panels.” Econometric Reviews, 34(6-10), 1089–1117.

Pesaran MH (2021). “General diagnostic tests for cross-sectional dependence in panels.” Empirical Economics, 60(1), 13–50.

Juodis A, Reese S (2021). “The incidental parameters problem in testing for remaining cross-sectional correlation.” Journal of Business and Economic Statistics, 40(3), 1191–1203.

Fan J, Liao Y, Yao J (2015). “Power Enhancement in High-Dimensional Cross-Section Tests.” Econometrica, 83(4), 1497–1541.

Pesaran MH, Xie Y (2021). “A bias-corrected CD test for error cross-sectional dependence in panel models.” Econometric Reviews, 41(6), 649–677.

Examples

# Simulate independent and dependent panels
set.seed(1)
E_indep <- matrix(rnorm(100), nrow = 10)
E_dep <- matrix(rnorm(10), nrow = 10, ncol = 10, byrow = TRUE)

# Compute all tests
cd_test(E_indep, type = "all")
cd_test(E_dep, type = "CD")

# Specific test with parameters
cd_test(E_indep, type = "CDstar", n_pc = 2)

# From a fitted csdm model
data(PWT_60_07, package = "csdm")
df <- PWT_60_07
ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% ids & df$year >= 1970, ]
fit <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small,
  id = "id",
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)
cd_test(fit, type = "all")

Deprecated cluster-robust variance-covariance utility

Description

cluster_vcov() is deprecated and is not used by csdm() estimators. For an ordinary OLS model, use sandwich::vcovCL() instead. The function remains available temporarily so existing code can migrate.

Usage

cluster_vcov(X, u, cluster, df_correction = TRUE, type = c("oneway", "twoway"))

Arguments

Value

A k x k variance-covariance matrix.

Deprecation

This low-level matrix utility is not a covariance method for csdm_fit objects. Applying it to stacked CCE residuals does not produce the mean-group covariance reported by vcov().

Extract model coefficients from a fitted csdm model

Description

Returns estimated mean-group coefficients from a csdm_fit object. For model = "cs_ardl", the returned vector includes short-run mean-group coefficients, the adjustment coefficient (named lr_<y>), and long-run coefficients when available.

Usage

## S3 method for class 'csdm_fit'
coef(object, component = c("all", "levels", "adjustment", "long_run"), ...)

Arguments

Value

A named numeric vector of estimated coefficients.

See Also

summary.csdm_fit(), vcov.csdm_fit()

Cross-sectional averages by time (with optional leave-one-out)

Description

Computes cross-sectional averages (CSAs) of specified variables for each time period, optionally in a leave-one-out (LOO) fashion per observation. Supports unbalanced panels and observation weights.

Usage

cross_sectional_avg(
  data,
  id = NULL,
  time = NULL,
  vars,
  leave_out = FALSE,
  weights = NULL,
  suffix = "csa",
  return_mode = c("attach", "time"),
  na.rm = TRUE
)

Arguments

Details

This is a standalone data utility. It does not configure the averages used by csdm(); use csdm_csa() for that purpose. Model fitting constructs averages from the evaluated model terms and its documented source sample, which can differ from averages of raw data columns produced here.

Efficiently computes, for each v in vars and time t,

\bar v_t = \frac{\sum_i w_{it}\, 1_{\{v_{it}\text{ finite}\}}\, v_{it}} {\sum_i w_{it}\, 1_{\{v_{it}\text{ finite}\}}}

For leave_out=TRUE, each row's CSA excludes its own contribution; if the denominator becomes \le 0 (e.g., only one finite observation at that time), the LOO mean is set to NA for that row/variable.

Value

A data.frame:

• If return_mode="attach": original data + CSA columns named paste0(suffix, "_", vars).

• If return_mode="time": unique time rows with CSA columns.

Panel Model Estimation with Cross-Sectional Dependence

Description

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.

Usage

csdm(
  formula,
  data,
  id,
  time,
  model = c("mg", "cce", "dcce", "cs_ardl"),
  csa = csdm_csa(),
  lr = csdm_lr(),
  pooled = NULL,
  trend = c("none", "unit"),
  fullsample = FALSE,
  mgmissing = FALSE,
  vcov = csdm_vcov(),
  time_step = 1,
  subset = NULL,
  na.action = stats::na.omit,
  ...
)

Arguments

Details

Let i = 1, \ldots, N index cross-sectional units and t = 1, \ldots, T index time. A baseline heterogeneous panel model is

y_{it} = \alpha_i + \beta_i^T x_{it} + u_{it}.

Here \alpha_i is a unit-specific intercept, x_{it} is a vector of regressors, \beta_i is a vector of unit-specific slopes, and 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.

Implemented estimators

MG (Pesaran and Smith, 1995)

The Mean Group estimator fits separate regressions for each unit and averages the resulting coefficients:

\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.

CCE (Pesaran, 2006)

Regressions are augmented with cross-sectional averages to proxy unobserved common factors:

y_{it} = \alpha_i + \beta_i^T x_{it} + \gamma_i^T \bar{z}_{t} + v_{it}.

A common choice is

\bar{z}_t = (\bar{y}_t, \bar{x}_t),

with

\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, \bar{z}_t collects the cross-sectional averages specified in csa.

DCCE (Chudik and Pesaran, 2015)

Dynamic CCE extends CCE by allowing lagged dependent variables and lagged cross-sectional averages:

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 lr, while contemporaneous and lagged cross-sectional averages are controlled through csa.

CS-ARDL (Chudik and Pesaran, 2015)

In the package implementation, model = "cs_ardl" is obtained by first estimating a cross-sectionally augmented ARDL-style regression in levels, using the same dynamic specification as model = "dcce", and then transforming the unit-specific coefficients into adjustment and long-run parameters.

The underlying unit-level regression is of the form

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

\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 \varphi_i is the adjustment coefficient and \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.

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.

Value

An object of class csdm_fit containing estimated coefficients, residuals, variance-covariance estimates, model metadata, and diagnostics. Use summary(), coef(), residuals(), vcov(), and cd_test() to access standard outputs.

References

Pesaran MH, Smith R (1995). “Estimating long-run relationships from dynamic heterogeneous panels.” Journal of Econometrics, 68(1), 79–113.

Pesaran MH (2006). “Estimation and inference in large heterogeneous panels with multifactor error structure.” Econometrica, 74(4), 967–1012.

Chudik A, Pesaran MH (2015). “Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors.” Journal of Econometrics, 188(2), 393–420.

Examples

library(csdm)
data(PWT_60_07, package = "csdm")
df <- PWT_60_07

# Keep examples fast but fully runnable
keep_ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% keep_ids & df$year >= 1970, ]

# Mean Group (MG)
mg <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small, id = "id", time = "year", model = "mg"
)
summary(mg)

# Common Correlated Effects (CCE)
cce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small, id = "id", time = "year", model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)
summary(cce)

# Dynamic CCE (DCCE)
dcce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small, id = "id", time = "year", model = "dcce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), lags = 3),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 0)
)
summary(dcce)

# CS-ARDL
cs_ardl <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small, id = "id", time = "year", model = "cs_ardl",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), lags = 3),
  lr = csdm_lr(type = "ardl", ylags = 1, xdlags = 0)
)
summary(cs_ardl)

Specification: Cross-sectional averages (CSA)

Description

Specification: Cross-sectional averages (CSA)

Usage

csdm_csa(vars = "_all", lags = 0, scope = "estimation", cluster = NULL)

Arguments

Value

A spec object (list) used by csdm().

Examples

# Cross-sectional averages (CSA) configuration for DCCE
csa <- csdm_csa(
  vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"),
  lags = 3
)
csa

Extract fitted model data and sample information

Description

The model frame and economic design matrix contain only estimated observations, in panel order. formula() and terms() include constructed economic lags. nobs() counts estimated observations. df.residual() returns Inf for asymptotic normal MG inference; individual regression degrees of freedom are in object$units.

Usage

## S3 method for class 'csdm_fit'
nobs(object, ...)

## S3 method for class 'csdm_fit'
model.frame(formula, ...)

## S3 method for class 'csdm_fit'
model.matrix(object, ...)

## S3 method for class 'csdm_fit'
terms(x, ...)

## S3 method for class 'csdm_fit'
formula(x, ...)

## S3 method for class 'csdm_fit'
df.residual(object, ...)

Arguments

Specification: Long-run configuration

Description

Specification: Long-run configuration

Usage

csdm_lr(
  vars = NULL,
  type = c("none", "ardl"),
  ylags = 0,
  xdlags = 0,
  options = list()
)

Arguments

Value

A spec object (list) used by csdm().

Examples

# Long-run / dynamic configuration (ARDL-style lags)
lr <- csdm_lr(type = "ardl", ylags = 1)
lr

# Minimal end-to-end DCCE example (kept small for speed)
data(PWT_60_07, package = "csdm")
df <- PWT_60_07
keep_ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% keep_ids & df$year >= 1970, ]
fit <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small,
  id = "id",
  time = "year",
  model = "dcce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"), lags = 3),
  lr = csdm_lr(type = "ardl", ylags = 1)
)
summary(fit)

Deprecated pooled-constraint specification

Description

csdm_pooled() is deprecated because pooled restrictions are not implemented. Explicit pooled specifications continue to be rejected by csdm().

Usage

csdm_pooled(vars = NULL, constant = FALSE, trend = FALSE)

Arguments

Value

A deprecated specification object retained for compatibility.

Tidy model coefficients, fit statistics, and observations

Description

Tidy model coefficients, fit statistics, and observations

Usage

## S3 method for class 'csdm_fit'
tidy(
  x,
  component = c("all", "levels", "adjustment", "long_run"),
  conf.int = FALSE,
  conf.level = 0.95,
  ...
)

## S3 method for class 'csdm_fit'
glance(x, ...)

## S3 method for class 'csdm_fit'
augment(x, data = x$data, newdata = NULL, ...)

Arguments

Specification: Mean-group variance-covariance estimator

Description

Specification: Mean-group variance-covariance estimator

Usage

csdm_vcov(type = "mg", ...)

Arguments

Value

A spec object (list) used by csdm().

Extract fitted panel values

Description

Extract fitted panel values

Usage

## S3 method for class 'csdm_fit'
fitted(object, format = c("matrix", "vector", "long"), ...)

Arguments

Deprecated residual-matrix accessor

Description

get_residuals() is deprecated. Use residuals() for fitted csdm models, or pass a numeric residual matrix directly to cd_test().

Usage

get_residuals(object, type = c("auto", "cce", "pca", "pca_std"), strict = TRUE)

Arguments

Details

Residual types

cce

Residuals from the cross-sectionally augmented unit regressions.

pca

Residuals after principal-component factor removal.

pca_std

PCA residuals standardized by unit-specific scale.

auto

Priority rule: pca_std -> pca -> cce -> generic residual slots.

Assumptions and usage

The returned matrix is intended for diagnostics that operate on unit-time panels, including cd_test(). Missing values are preserved unless downstream routines explicitly filter or balance the panel.

Value

A numeric matrix of residuals with rows = units and columns = time, preserving rownames and colnames when available; or NULL if nothing suitable is found and strict = FALSE.

Examples

data(PWT_60_07, package = "csdm")
df <- PWT_60_07
ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% ids & df$year >= 1970, ]

fit <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small,
  id = "id",
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)

E <- get_residuals(fit, type = "auto")
dim(E)

Deprecated fixed-weight mean-group covariance utility

Description

pooled_vcov() is deprecated and is not used by csdm() estimators. Use vcov() on a fitted model for supported mean-group inference. The function remains available temporarily so existing code can migrate.

Usage

pooled_vcov(beta_i, weights = NULL, pairwise = TRUE)

Arguments

Details

Weights are fixed relative weights, normalized on the retained units. The calculation assumes independent unit estimates with a common covariance: the weighted sample covariance is divided by 1-\sum_i w_i^2, then multiplied by \sum_i w_i^2. Equal weights give sample covariance divided by N. With missing coefficients, use pairwise=FALSE to select one complete-unit sample. Pairwise covariance with missing coefficients is not implemented. This is not an inverse-variance pooled estimator or a general covariance estimator for arbitrary unit-specific covariance matrices. Its name can be misleading because it calculates the covariance of a fixed-weight average of unit estimates under the stated common-covariance and independence assumptions.

Value

A K x K covariance matrix for the MG mean, with dimnames inherited from colnames(beta_i).

Predict method for csdm models

Description

Produces fitted values (index "xb") when available, or returns model residuals. Prediction on new data is not yet implemented.

Usage

## S3 method for class 'csdm_fit'
predict(object, newdata = NULL, type = c("xb", "residuals"), ...)

Arguments

Value

A numeric matrix of fitted values or residuals, depending on type.

See Also

residuals.csdm_fit(), summary.csdm_fit()

Deprecated residual preprocessing utility

Description

prepare_cd_input() is deprecated and is not used by cd_test(). Transforming residuals before a dependence test can change the tested hypothesis. Pass the original residual matrix to cd_test() and use its documented missing-data policy instead.

For compatibility, this function still performs:

1. Dropping time periods with fewer than min_per_time finite observations.

2. Optional row-wise standardization to unit variance over available times.

3. Optional demeaning across units at each time (changes the tested residuals).

Usage

prepare_cd_input(
  E,
  standardize = c("row", "none"),
  demean_time = FALSE,
  min_per_time = 2L
)

Arguments

Details

This helper is retained temporarily for compatibility. Row scaling changes the weighted covariance underlying CDw, while cross-sectional time demeaning can mechanically induce dependence. Neither transformation is applied by cd_test().

Transformation steps

1. Time periods with fewer than min_per_time finite observations are removed.

2. If standardize = "row", each unit is scaled by its observed standard deviation.

3. If demean_time = TRUE, each time slice is demeaned across available units.

Why this preprocessing matters

CD-type tests are sensitive to scale heterogeneity and sparse columns in unbalanced panels. This helper creates a better-conditioned input matrix while preserving as much usable information as possible.

Value

A list with:

Examples

data(PWT_60_07, package = "csdm")
df <- PWT_60_07
ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% ids & df$year >= 1970, ]
fit <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small,
  id = "id",
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)
E <- get_residuals(fit)
prep <- prepare_cd_input(E, standardize = "row", demean_time = TRUE, min_per_time = 3)
dim(prep$Z)

Compact print method for fitted csdm models

Description

Prints a concise overview of a fitted csdm_fit object, including the model type, formula, panel dimensions, and a coefficient table with standard errors when available.

Usage

## S3 method for class 'csdm_fit'
print(x, digits = 4, ...)

Arguments

Value

Invisibly returns x.

See Also

summary.csdm_fit(), coef.csdm_fit(), residuals.csdm_fit()

Print method for csdm summary objects

Description

Formats and prints a summary.csdm_fit object. Output adapts to model type and includes coefficient tables, selected goodness-of-fit diagnostics, and compact model metadata.

Usage

## S3 method for class 'summary.csdm_fit'
print(x, digits = 4, ...)

Arguments

Details

The printout includes classic Pesaran CD diagnostics from the summary object. For a full CD diagnostic panel (CD, CDw, CDw+, CD*), use cd_test() on the fitted model.

Value

Invisibly returns x.

See Also

summary.csdm_fit(), cd_test()

Extract residual matrix from a fitted csdm model

Description

Returns residuals as an N x T matrix (rows are units, columns are time). This method is designed for panel diagnostics and downstream tools such as cd_test().

Usage

## S3 method for class 'csdm_fit'
residuals(
  object,
  type = c("e", "u"),
  format = c("matrix", "vector", "long"),
  ...
)

Arguments

Value

A numeric matrix of residuals with dimensions N x T.

See Also

cd_test(), predict.csdm_fit()

Deprecated heteroskedasticity-robust covariance utility

Description

sandwich_vcov() is deprecated and is not used by csdm() estimators. For an ordinary OLS model, use sandwich::vcovHC() instead. The function remains available temporarily so existing code can migrate.

Usage

sandwich_vcov(X, u, type = c("HC0", "HC1", "HC2", "HC3"))

Arguments

Value

A k x k variance-covariance matrix.

Deprecation

This low-level matrix utility is not a covariance method for csdm_fit objects. Use vcov() to extract the supported mean-group covariance.

Summarize csdm model estimation results

Description

Computes post-estimation summaries for csdm_fit objects, including mean-group coefficient inference, model-level diagnostics, and model-specific summary tables (for example, short-run and long-run blocks for CS-ARDL).

Usage

## S3 method for class 'csdm_fit'
summary(object, digits = 4, ...)

Arguments

Details

Reported inference

For each coefficient \hat\beta_k, the summary reports standard errors, z-statistics, and two-sided normal-approximation p-values:

z_k = \frac{\hat\beta_k}{\operatorname{se}(\hat\beta_k)}, \qquad p_k = 2\{1-\Phi(|z_k|)\}.

Diagnostics

The printed summary shows the classic Pesaran CD diagnostic by default. Extended diagnostics (CDw, CDw+, CD*) are available through cd_test().

Value

An object of class summary.csdm_fit with core metadata (call/formula/model/N/T), coefficient tables, fit statistics, and model-specific components for printing and downstream inspection.

See Also

print.summary.csdm_fit(), cd_test(), coef.csdm_fit(), vcov.csdm_fit()

Examples

data(PWT_60_07, package = "csdm")
df <- PWT_60_07
ids <- unique(df$id)[1:10]
df_small <- df[df$id %in% ids & df$year >= 1970, ]
fit <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df_small,
  id = "id",
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)
s <- summary(fit)
s

Update a fitted panel model

Description

Update a fitted panel model

Usage

## S3 method for class 'csdm_fit'
update(object, formula., ..., evaluate = TRUE)

Arguments

Extract coefficient covariance matrix from a fitted csdm model

Description

Extract coefficient covariance matrix from a fitted csdm model

Usage

## S3 method for class 'csdm_fit'
vcov(object, component = c("all", "levels", "adjustment", "long_run"), ...)

Arguments

Value

A numeric variance-covariance matrix aligned with coef(object) for models where this is available.

See Also

coef.csdm_fit(), summary.csdm_fit()