Introduction to csdm: Panel Data Models with Cross-Sectional Dependence

Joao Claudio Macosso

2026-03-23

Overview

The csdm package implements econometric methods for panel data with cross-sectional dependence (CSD). In many applications, observations across units (e.g., countries, firms, regions) are not independent—macroeconomic shocks, trade relationships, or spillovers create correlation across cross-sectional units. The csdm package provides robust estimators that account for this dependence structure, plus diagnostic tests to detect and characterize it.

This vignette demonstrates four core estimation methods and related inference tools on real panel data from the Penn World Table (PWT).

Methodology: Four Estimators

Model Specification

The csdm() interface estimates heterogeneous panel data models with optional cross-sectional augmentation and dynamic structure. Let \(i = 1, \ldots, N\) index cross-sectional units and \(t = 1, \ldots, T\) index time. A baseline heterogeneous panel model is

\[ y_{it} = \alpha_i + \beta_i' x_{it} + u_{it}, \qquad i = 1, \ldots, N,\; t = 1, \ldots, T \]

where:

The inner product \(\beta_i' x_{it}\) is scalar-valued. Heterogeneous slopes allow each unit to respond differently to the regressors. In many applications, cross-sectional dependence arises because the error term contains unobserved common factors. The estimators implemented in csdm() differ in how they handle this dependence and whether they allow for dynamic adjustment.

1. Mean Group (MG) Estimator

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

\[ \hat{\beta}_{MG} = \frac{1}{N}\sum_{i=1}^N \hat{\beta}_i \]

Key idea: Estimation is performed unit by unit, with no pooling of slope coefficients across cross-sectional units.

Interpretation:

Properties:

Use case: A natural benchmark when the main concern is heterogeneous slopes and no explicit factor structure is imposed.

2. Common Correlated Effects (CCE) Estimator

The CCE estimator augments each unit regression with cross-sectional averages to proxy unobserved common factors:

\[ y_{it} = \alpha_i + \beta_i' x_{it} + \gamma_i' \bar{z}_t + v_{it} \]

where \(\bar{z}_t\) collects the cross-sectional averages specified through csdm_csa(), for example

\[ \bar{z}_t = (\bar{y}_t, \bar{x}_t), \qquad \bar{x}_t = \frac{1}{N}\sum_{i=1}^N x_{it}, \qquad \bar{y}_t = \frac{1}{N}\sum_{i=1}^N y_{it}. \]

Key idea: Cross-sectional averages serve as proxies for latent common factors that induce dependence across units.

Interpretation:

Properties:

Use case: When dependence across units is believed to reflect common unobserved factors.

3. Dynamic CCE (DCCE) Estimator

The DCCE estimator extends CCE to dynamic settings by including lagged dependent variables, optional distributed lags of regressors, and lagged cross-sectional averages:

\[ y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p} + \sum_{q=0}^{Q} \beta_{iq}' x_{i,t-q} + \sum_{s=0}^{S} \delta_{is}' \bar{z}_{t-s} + e_{it} \]

where the dynamic structure is controlled through csdm_lr() and the cross-sectional averages and their lags are controlled through csdm_csa().

Key idea: Dynamics are introduced directly in the unit equation, while lagged cross-sectional averages help absorb common factor dependence over time.

Interpretation:

Properties:

Use case: When the outcome is persistent over time and cross-sectional dependence remains important.

4. Cross-Sectionally Augmented ARDL (CS-ARDL)

In the current csdm() 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 estimated unit-specific coefficients into adjustment and long-run parameters.

The underlying unit-level regression is

\[ y_{it} = \alpha_i + \sum_{p=1}^{P} \phi_{ip} y_{i,t-p} + \sum_{q=0}^{Q} \beta_{iq}' x_{i,t-q} + \sum_{s=0}^{S} \omega_{is}' \bar{z}_{t-s} + e_{it} \]

From this dynamic specification, the implied error-correction form is

\[ \Delta y_{it} = \alpha_i + \varphi_i \left( y_{i,t-1} - \theta_i' x_{i,t-1} \right) + \sum_{j=1}^{P-1} \lambda_{ij} \Delta y_{i,t-j} + \sum_{j=0}^{Q-1} \psi_{ij}' \Delta x_{i,t-j} + \sum_{s=0}^{S} \tilde{\omega}_{is}' \bar{z}_{t-s} + e_{it} \]

where the dynamic structure is controlled through csdm_lr() and the cross-sectional averages are supplied through csdm_csa().

Key idea: cs_ardl reports the implied short-run and long-run quantities from a cross-sectionally augmented ARDL fit.

Interpretation:

Properties:

Use case: When the objective is to study long-run relationships together with heterogeneous short-run adjustment in panels affected by common factors.

Cross-Sectional Averages and Dynamic Structure

Two helper specifications control the main extensions in csdm():

This design keeps the estimation interface consistent across the four estimators while allowing the model specification to vary by application.

Summary

Estimator Heterogeneous Slopes Cross-Sectional Averages Dynamics Long-Run Structure
MG Yes No No No
CCE Yes Yes No No
DCCE Yes Yes Yes No
CS-ARDL Yes Yes Yes Yes

Data: Penn World Table Subset

The PWT_60_07 dataset contains macroeconomic indicators for 93 countries covering 1960–2007 (48 years). Key variables include:

data(PWT_60_07, package = "csdm")
head(PWT_60_07, 10)
#>    id year log_rgdpo    log_hc   log_ck   log_ngd
#> 1   1 1960  7.780284 0.7042058 11.33559        NA
#> 2   1 1961  7.792448 0.7096307 11.39625 -2.714639
#> 3   1 1962  7.800655 0.7150558 11.45449 -2.719555
#> 4   1 1963  7.751311 0.7204807 11.44691 -2.723755
#> 5   1 1964  7.786854 0.7259058 11.48774 -2.727269
#> 6   1 1965  7.879184 0.7313308 11.54133 -2.730137
#> 7   1 1966  7.885357 0.7386493 11.57771 -2.737341
#> 8   1 1967  7.903803 0.7459679 11.61129 -2.744694
#> 9   1 1968  7.923327 0.7532863 11.64887 -2.745329
#> 10  1 1969  8.004197 0.7606049 11.71465 -2.739602
str(PWT_60_07)
#> Classes 'tbl_df', 'tbl' and 'data.frame':    4464 obs. of  6 variables:
#>  $ id       : num  1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, "format.stata")= chr "%12.0g"
#>  $ year     : num  1960 1961 1962 1963 1964 ...
#>   ..- attr(*, "label")= chr "(firstnm) year"
#>   ..- attr(*, "format.stata")= chr "%9.0g"
#>  $ log_rgdpo: num  7.78 7.79 7.8 7.75 7.79 ...
#>   ..- attr(*, "format.stata")= chr "%9.0g"
#>  $ log_hc   : num  0.704 0.71 0.715 0.72 0.726 ...
#>   ..- attr(*, "format.stata")= chr "%9.0g"
#>  $ log_ck   : num  11.3 11.4 11.5 11.4 11.5 ...
#>   ..- attr(*, "format.stata")= chr "%9.0g"
#>  $ log_ngd  : num  NA -2.71 -2.72 -2.72 -2.73 ...
#>   ..- attr(*, "format.stata")= chr "%9.0g"

# For computational speed in this vignette, use a subset: 
# first 15 countries, 1970-2007
first_15_ids <- unique(PWT_60_07$id)[1:15]
df <- subset(PWT_60_07, id %in% first_15_ids & year >= 1970 & year <= 2007)

The panel is relatively balanced. We will use growth regressions: modeling log-GDP (log_rgdpo) as a function of human capital (log_hc), capital stock (log_ck), and government debt (log_ngd), and test cross-sectional dependence in residuals.

Package installation

To install the csdm package from CRAN, run:

install.packages("csdm")

To install the latest development version from GitHub, run:

install.packages("remotes")
remotes::install_github("Macosso/csdm")

Model Estimation: Four Examples

All models are fitted with csdm(), which automatically detects the input structure and applies the appropriate methodology. The key arguments are id and time to specify the cross-sectional and time-period identifiers, and model to choose the estimator. For CCE and DCCE, additional arguments (csa and lr) specify treatment of cross-sectional averages and dynamics.

Example 1: Mean Group (MG) Estimation

# MG: Separate regression per country, then average coefficients
fit_mg <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "mg"
)

print(fit_mg)
#> csdm fit (mg)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#>             Estimate Std.Error
#> (Intercept)   6.5609    0.8648
#> log_hc        0.1725    0.8530
#> log_ck        0.3056    0.1359
#> log_ngd       0.7800    0.3777
summary(fit_mg)
#> csdm summary: Mean Group Model (MG)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#> Number of obs: 570
#> R-squared (mg): 0.9449
#> CD = 3.2379, p = 0.0012
#> (For additional CD diagnostics, use cd_test())
#> 
#> Mean Group:
#>              Coef. Std. Err.      z  P>|z| Signif. CI 2.5% CI 97.5%
#> (Intercept) 6.5609    0.8648 7.5867 0.0000     ***  4.8659   8.2558
#> log_hc      0.1725    0.8530 0.2022 0.8398         -1.4993   1.8443
#> log_ck      0.3056    0.1359 2.2485 0.0245       *  0.0392   0.5720
#> log_ngd     0.7800    0.3777 2.0652 0.0389       *  0.0398   1.5203
#> 
#> Mean Group Variables: log_hc, log_ck, log_ngd
#> Cross Sectional Averaged Variables: none (lags=0)
#> 
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: The MG estimate suggests that on average across countries, increases in human capital, capital stock, and changes in debt are associated with changes in real GDP. The standard errors reflect cross-country heterogeneity in these relationships.

Example 2: Common Correlated Effects (CCE)

# CCE: Add cross-sectional means to control for common shocks
fit_cce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  id = "id", 
  time = "year",
  model = "cce",
  csa = csdm_csa(vars = c("log_rgdpo", "log_hc", "log_ck", "log_ngd"))
)

print(fit_cce)
#> csdm fit (cce)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#>             Estimate Std.Error
#> (Intercept)   1.9003    2.1195
#> log_hc       -1.4921    1.0152
#> log_ck        0.1367    0.0956
#> log_ngd       0.8075    0.2972
summary(fit_cce)
#> csdm summary: Static Common Correlated Error Model (CCE)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#> Number of obs: 570
#> R-squared (mg): 0.9777
#> CD = -2.6758, p = 0.0075
#> (For additional CD diagnostics, use cd_test())
#> 
#> Mean Group:
#>               Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
#> (Intercept)  1.9003    2.1195  0.8965 0.3700         -2.2540   6.0545
#> log_hc      -1.4921    1.0152 -1.4697 0.1416         -3.4819   0.4977
#> log_ck       0.1367    0.0956  1.4298 0.1528         -0.0507   0.3240
#> log_ngd      0.8075    0.2972  2.7175 0.0066      **  0.2251   1.3899
#> 
#> Mean Group Variables: log_hc, log_ck, log_ngd
#> Cross Sectional Averaged Variables: log_rgdpo, log_hc, log_ck, log_ngd (lags=0)
#> 
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: After accounting for global shocks (via cross-sectional averages of all variables), the CCE coefficients and standard errors may differ from MG. This indicates whether common factors (e.g., technology, energy prices) are a major source of cross-sectional dependence.

Example 3: Dynamic CCE (DCCE)

# DCCE: Include dynamics and cross-sectional means
# Use lagged dependent variable to capture dynamic adjustment
fit_dcce <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  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)
)

print(fit_dcce)
#> csdm fit (dcce)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#>                Estimate Std.Error
#> (Intercept)      9.2888    8.0386
#> log_hc          -1.9558    1.5659
#> log_ck           0.6666    0.2424
#> log_ngd         -0.3178    1.1271
#> lag1_log_rgdpo  -0.0745    0.0555
summary(fit_dcce)
#> csdm summary: Dynamic Common Correlated Error Model (DCCE)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#> Number of obs: 525
#> R-squared (mg): 0.9861
#> CD = -2.392, p = 0.0168
#> (For additional CD diagnostics, use cd_test())
#> 
#> Mean Group:
#>                  Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
#> (Intercept)     9.2888    8.0386  1.1555 0.2479         -6.4666  25.0441
#> log_hc         -1.9558    1.5659 -1.2490 0.2117         -5.0249   1.1132
#> log_ck          0.6666    0.2424  2.7499 0.0060      **  0.1915   1.1417
#> log_ngd        -0.3178    1.1271 -0.2819 0.7780         -2.5268   1.8913
#> lag1_log_rgdpo -0.0745    0.0555 -1.3424 0.1795         -0.1834   0.0343
#> 
#> Mean Group Variables: log_hc, log_ck, log_ngd, lag1_log_rgdpo
#> Cross Sectional Averaged Variables: log_rgdpo, log_hc, log_ck, log_ngd (lags=3)
#> 
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: The DCCE model includes lagged GDP to capture dynamic adjustment. The lagged coefficient typically lies between 0.8–0.95, indicating strong income persistence. The coefficients on other variables represent short-run elasticities; to compute long-run effects, divide by \((1 - \text{lag coefficient})\).

Example 4: Cross-Sectionally Augmented ARDL (CS-ARDL)

# CS-ARDL: Separate short-run and long-run dynamics
# Includes lagged dependent and lagged regressors
fit_csardl <- csdm(
  log_rgdpo ~ log_hc + log_ck + log_ngd,
  data = df,
  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)
)

print(fit_csardl)
#> csdm fit (cs_ardl)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#>                Estimate Std.Error
#> (Intercept)      9.2888    8.0386
#> log_hc          -1.9558    1.5659
#> log_ck           0.6666    0.2424
#> log_ngd         -0.3178    1.1271
#> lag1_log_rgdpo  -0.0745    0.0555
summary(fit_csardl)
#> csdm summary: Cross-Sectional ARDL (CS-ARDL)
#> Formula: log_rgdpo ~ log_hc + log_ck + log_ngd
#> N: 15, T: 38
#> Number of obs: 525
#> R-squared (mg): 0.9861
#> 
#> CD = -2.392, p = 0.0168
#> (For additional CD diagnostics, use cd_test())
#> 
#> Short Run Est.
#>                  Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
#> (Intercept)     9.2888    8.0386  1.1555 0.2479         -6.4666  25.0441
#> log_hc         -1.9558    1.5659 -1.2490 0.2117         -5.0249   1.1132
#> log_ck          0.6666    0.2424  2.7499 0.0060      **  0.1915   1.1417
#> log_ngd        -0.3178    1.1271 -0.2819 0.7780         -2.5268   1.8913
#> lag1_log_rgdpo -0.0745    0.0555 -1.3424 0.1795         -0.1834   0.0343
#> 
#> Adjust. Term
#>                Coef. Std. Err.        z P>|z| Signif. CI 2.5% CI 97.5%
#> lr_log_rgdpo -1.0745    0.0555 -19.3513     0     *** -1.1834  -0.9657
#> 
#> Long Run Est.
#>              Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5% n_used
#> lr_log_hc  -1.8378    1.4743 -1.2465 0.2126         -4.7274   1.0518     15
#> lr_log_ck   0.6106    0.2076  2.9409 0.0033      **  0.2037   1.0175     15
#> lr_log_ngd -0.4098    1.2364 -0.3314 0.7403         -2.8330   2.0134     15
#> 
#> Mean Group Variables: lag1_log_rgdpo, log_hc, log_ck, log_ngd
#> Cross Sectional Averaged Variables: log_rgdpo, log_hc, log_ck, log_ngd (lags=3)
#> Long Run Variables: log_hc, log_ck, log_ngd
#> Cointegration variable(s): log_rgdpo
#> 
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: The CS-ARDL model returns both short-run coefficients (immediate response to shocks) and long-run coefficients (equilibrium effect after full adjustment). The long-run elasticities are often larger than short-run responses, consistent with gradual accumulation effects in capital and human capital.

Cross-Sectional Dependence Testing

After fitting a model, we can test whether residuals exhibit cross-sectional dependence using the Pesaran CD test and related variants. CSD tests detect whether residuals \(u_{it}\) are correlated across units—a key assumption violation that can bias standard errors.

Four CD Test Types

All CD tests have null hypothesis: residuals are cross-sectionally independent.

1. Pesaran CD Test

The Pesaran CD statistic is:

\[CD = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} \hat{\rho}_{ij} \sqrt{T}\]

where \(\hat{\rho}_{ij}\) is the cross-sectional correlation between residuals of units \(i\) and \(j\). The test statistic is approximately standard normal under the null.

Interpretation: Large \(|CD|\) rejects independence; both positive and negative correlations are flagged. This is the most general CD test and works even when \(N\) is fixed and \(T \to \infty\).

2. Pesaran CD Weighted (CDw)

The CDw statistic applies unit-level random sign weights to the cross-sectional correlations:

\[CD_w = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} w_i w_j \hat{\rho}_{ij} \sqrt{T}\]

where weights \(w_i \in \{-1,1\}\) are independent random sign flips assigned at the unit level and held fixed within a replication. This random-weighting scheme improves the behavior of the test in the presence of heteroskedasticity.

3. Pesaran CD Weighted Plus (CDw+)

CDw+ uses the same unit-level random sign weights but applies them to a bias-adjusted version of the CD statistic:

\[CD_w^+ = \sqrt{\frac{2}{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} w_i w_j \hat{\rho}_{ij}^+ \sqrt{T}\]

where \(\hat{\rho}_{ij}^+\) denotes the adjusted cross-sectional correlation. CDw+ is designed to improve robustness in large panels with heteroskedasticity.

4. Pesaran CD*, Fan-Liao-Yao (FLY)

The CD* statistic is a semiparametric refinement for large \(N\) and \(T\):

\[CD^* = \frac{1}{\sqrt{N(N-1)}} \sum_{i=1}^{N-1} \sum_{j=i+1}^{N} (\hat{\rho}_{ij}^2 - \tau_T)\]

where \(\tau_T\) is a variance adjustment. FLY-type tests are designed for large panel dimensions and provide robustness against certain forms of weak cross-sectional dependence.

Running CD Tests with Seed Selection

The cd_test() function accepts the fitted model and computes all test variants. Tests use a random seed to initialize pseudo-random computations (for cdw and cdw+); setting a seed ensures reproducibility of numerical results across runs.

# Test MG residuals for CSD
cd_mg <- cd_test(fit_mg, type = "CD")
print(cd_mg)
#> Cross-sectional dependence tests
#> N = 15, T = 38
#> 
#>    statistic p.value
#> CD     3.238   0.001

# Test CCE residuals for CSD
set.seed(1234)
cd_cce <- cd_test(fit_cce, type = "all")
print(cd_cce)
#> Cross-sectional dependence tests
#> N = 15, T = 38
#> 
#>        statistic p.value
#> CD        -2.676   0.007
#> CDw       -1.532   0.126
#> CDw+     150.431   0.000
#> CDstar    -2.835   0.005

# Test DCCE residuals for CSD
set.seed(1234)
cd_dcce <- cd_test(fit_dcce, type = "CDw")
print(cd_dcce)
#> Cross-sectional dependence tests
#> N = 15, T = 35
#> 
#>     statistic p.value
#> CDw     1.092   0.275

# Test CS-ARDL residuals for CSD
set.seed(1234)
cd_csardl <- cd_test(fit_csardl, type = "all")
print(cd_csardl)
#> Cross-sectional dependence tests
#> N = 15, T = 35
#> 
#>        statistic p.value
#> CD        -2.392   0.017
#> CDw        1.092   0.275
#> CDw+     130.969   0.000
#> CDstar    -1.769   0.077

Interpreting Results:

In practice, models that do not account for cross-sectional dependence (like MG without augmentation) typically show significant CD test rejections, justifying the use of CSD-robust methods like CCE and DCCE.

References

Chudik, A., & Pesaran, M. H. (2013). Large panel data models with cross-sectional dependence: A survey [Globalization Institute Working Papers]. Federal Reserve Bank of Dallas, (153).
Chudik, A., & 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.
Ditzen, J. (2018). Estimating dynamic common-correlated effects in STATA. The STATA Journal, 18(3), 585–617. https://doi.org/10.1177/1536867X1801800306
Fan, J., Liao, Y., & Yao, J. (2015). Power enhancement in high-dimensional cross-section tests. Econometrica, 83(4), 1497–1541.
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.
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with multifactor error structure. Econometrica, 74(4), 967–1012.
Pesaran, M. H. (2007). A simple unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.
Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089–1117.
Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.
Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79–113.
Pesaran, M. H., & Xie, Y. (2021). A bias-corrected CD test for error cross-sectional dependence in panel models. Econometric Reviews, 41(6), 649–677.

For further details on the theoretical foundations and implementation of CSD-robust methods, see the documentation for ?csdm, ?cd_test, and ?summary.csdm_fit.