VAR Modeling for Heterogeneous Panels
Coerce into a 'pplot' object
Coerce into a 'pvarx' object
Deterministic regressors in pvars
Coerce into a 'varx' object
Test procedures for the cointegration rank
Forecast Error Variance Decomposition
Identification of SVEC models by imposing long- and short-run restrict...
Identification of SVAR models by means of proxy variables
Impulse Response Functions for panel SVAR models
Impulse Response Functions
Panel cointegration rank tests
Recursive identification of panel SVAR models via Cholesky decompositi...
Independence-based identification of panel SVAR models via Cramer-von ...
Independence-based identification of panel SVAR models using distance ...
Identification of panel SVEC models by imposing long- and short-run re...
Identification of panel SVAR models by means of proxy variables
Persistence Profiles
pvars: VAR Modeling for Heterogeneous Panels
Estimation of VAR models for heterogeneous panels
Bootstrap for JB normality test
Bootstrap with residual moving blocks for individual SVAR models
Mean group inference for panel SVAR models
Bootstrap with residual panel blocks for panel SVAR models
Criteria on the number of common factors
Criteria on the lag-order and break period(s)
Estimation of a Vector Error Correction Model
Implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).