Package: convergenceDFM 0.3.2

convergenceDFM: Convergence and Dynamic Factor Models

Tests convergence in macro-financial panels combining Dynamic Factor Models (DFM) and mean-reverting, discrete-time Ornstein-Uhlenbeck/AR(1) factor processes. Provides: (i) static factor extraction with VAR stability checks, Portmanteau tests and rolling out-of-sample R^2, in the spirit of Stock and Watson (2002) <doi:10.1198/073500102317351921> and the Generalized Dynamic Factor Model of Forni, Hallin, Lippi and Reichlin (2000) <doi:10.1162/003465300559037>; (ii) cointegration analysis a la Johansen (1988) <doi:10.1016/0165-1889(88)90041-3>; (iii) Bayesian factor-OU/AR(1) estimation with convergence and half-life summaries grounded in Uhlenbeck and Ornstein (1930) <doi:10.1103/PhysRev.36.823> and Vasicek (1977) <doi:10.1016/0304-405X(77)90016-2>, with full Markov chain Monte Carlo convergence diagnostics; (iv) heteroskedasticity-consistent (HC) and, when the suggested 'sandwich' (Zeileis (2004) <doi:10.18637/jss.v011.i10>) and 'lmtest' packages are available, heteroskedasticity- and autocorrelation- consistent (HAC) robust inference, with a self-contained HC fallback; (v) coupling significance tests based on time-shift / block-bootstrap nulls that preserve marginal dynamics while breaking cross-series dependence; and (vi) optional PLS-based factor preselection (Mevik and Wehrens (2007) <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility (explicit seeds throughout) and clear, publication-ready summaries.

Authors:José Mauricio Gómez Julián [aut, cre]

convergenceDFM_0.3.2.tar.gz
convergenceDFM_0.3.2.zip(r-4.7-any)convergenceDFM_0.3.2.zip(r-4.6-any)convergenceDFM_0.3.2.zip(r-4.5-any)
convergenceDFM_0.3.2.tgz(r-4.6-any)convergenceDFM_0.3.2.tgz(r-4.5-any)
convergenceDFM_0.3.2.tar.gz(r-4.7-any)convergenceDFM_0.3.2.tar.gz(r-4.6-any)
convergenceDFM_0.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
convergenceDFM/json (API)

# Install 'convergenceDFM' in R:
install.packages('convergenceDFM', repos = c('https://isadorenabi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/isadorenabi/convergencedfm/issues

On CRAN:

Conda:

dynamic-factor-modeleconometrics

4.70 score 1 stars 4 scripts 374 downloads 27 exports 38 dependencies

Last updated from:e01aa2c0a6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK242
source / vignettesOK250
linux-releaseOK250
macos-releaseOK186
macos-oldrelOK171
windows-develOK163
windows-releaseOK214
windows-oldrelOK224
wasm-releaseOK137

Exports:build_cluster_mapchoose_var_lagcompute_wedgedeltaR2_oudiagnose_dataestimate_DFMestimate_factor_OUmake_X_innovationsplacebo_valuesplot_error_correction_panelread_cpirescue_short_run_channelrotation_null_testrow_norm1run_complete_factor_analysis_robustrun_convergence_robustness_testsrun_rotation_null_on_resultsselect_optimal_components_safetest_cointegration_controltest_jackknife_sectorstest_leave_cluster_outtest_permutation_robustnesstest_reweighting_robustnessto_num_commasvisualize_factor_dynamicsvisualize_factor_dynamics_simplewedge_stationarity

Dependencies:BayesianDisaggregationcellrangerclicpp11crayondplyrgenericsgluehmslatticelifecyclelmtestmagrittrMASSnlmepillarpkgconfigplsprettyunitsprogresspurrrR6readxlrematchrlangsandwichstringistringrstrucchangetibbletidyrtidyselecturcautf8varsvctrswithrzoo

Canonical disaggregation and the Leave-Cluster-Out test
1. One disaggregation engine, not two | Where the engine is used here | 2. Leave-Cluster-Out | Why a cluster, not a single sector | The cluster map is pluggable | A documented fallback until the MIP arrives | Reading the statistical layer honestly
Rendered fromcanonical-disaggregation-and-leave-cluster-out.Rmdusingknitr::rmarkdown

Last update: 2026-06-18
Started: 2026-06-18

Introduction to Convergence Analysis with convergenceDFM
Introduction | Basic usage | Coupling significance (corrected null) | Convergence test | Methodological notes and design decisions | Reproducibility
Rendered fromconvergence-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-06-04
Started: 2025-12-02

Readme and manuals

Help Manual

Help pageTopics
Build a fallback sector-to-cluster mapbuild_cluster_map
Select optimal VAR lag order with multiple criteriachoose_var_lag
Construct the transformation wedge W = Phi - V = K * G' - pcompute_wedge
Incremental R-squared from X in OU modeldeltaR2_ou
Diagnose and prepare data matricesdiagnose_data
Estimate Dynamic Factor Model with VAR dynamicsestimate_DFM
Estimate Factor Ornstein-Uhlenbeck / AR(1) model (Stan if available)estimate_factor_OU
Extract X innovations (reduced-form VAR residuals)make_X_innovations
Aggregate-preserving placebo values (negative controls for gravitation)placebo_values
Plot error correction panelplot_error_correction_panel
Read CPI data from an Excel fileread_cpi
Rescue short-run channel testrescue_short_run_channel
Null hypothesis test for X->Y factor couplingrotation_null_test
Normalize matrix rows to sum to onerow_norm1
Complete factor-OU convergence analysis pipelinerun_complete_factor_analysis_robust
Run comprehensive robustness test suiterun_convergence_robustness_tests
Run the coupling null test on complete analysis resultsrun_rotation_null_on_results
Select optimal number of PLS components with cross-validationselect_optimal_components_safe
Classical cointegration control (Johansen trace or eigen)test_cointegration_control
Delete-one-sector jackknife of the X->Y couplingtest_jackknife_sectors
Leave-cluster-out robustness of the X->Y couplingtest_leave_cluster_out
Permutation-based robustness test for X->Y couplingtest_permutation_robustness
Reweighting-based robustness testtest_reweighting_robustness
Convert localized number strings to numericto_num_commas
Visualize factor dynamics comprehensivelyvisualize_factor_dynamics
Simple factor dynamics visualizationvisualize_factor_dynamics_simple
Per-sector and panel stationarity / mean-reversion of the wedgewedge_stationarity