pvars (version 1.1.1)

pid.grt: Identification of panel SVEC models by imposing long- and short-run restrictions

Description

Identifies a panel of SVEC models by utilizing a scoring algorithm to impose long- and short-run restrictions. See the details of SVEC in vars.

Usage

pid.grt(
  x,
  LR = NULL,
  SR = NULL,
  start = NULL,
  max.iter = 100,
  conv.crit = 1e-07,
  maxls = 1
)

Value

List of class 'pid' with elements:

A

Matrix. The lined-up coefficient matrices \(A_j, j=1,\ldots,p\) or the lagged variables in the panel VAR.

B

Matrix. Mean group of the estimated structural impact matrices \(B_i\), i.e. the unique decomposition of the covariance matrices of reduced-form errors.

L.varx

List of 'varx' objects for the individual estimation results to which the structural impact matrices \(B_i\) have been added.

args_pid

List of characters and integers indicating the identification methods and specifications that have been used.

args_pvarx

List of characters and integers indicating the estimator and specifications that have been used.

Arguments

x

An object of class 'pvarx' or a list of VECM objects that will be coerced to 'varx'. Panel of VAR objects estimated under rank-restriction.

LR

Matrix. The restricted long-run impact matrix.

SR

Matrix. The restricted contemporaneous impact matrix.

start

Vector. The starting values for \(\gamma\), set by rnorm if NULL (the default).

max.iter

Integer. The maximum number of iterations.

conv.crit

Real number. Convergence value of algorithm.

maxls

Real number. Maximum movement of the parameters between two iterations of the scoring algorithm.

References

Amisano, G. and Giannini, C. (1997): Topics in Structural VAR Econometrics, Springer, 2nd ed.

Breitung, J., Brueggemann R., and Luetkepohl, H. (2004): "Structural Vector Autoregressive Modeling and Impulse Responses", in Applied Time Series Econometrics, ed. by H. Luetkepohl and M. Kraetzig, Cambridge University Press, Cambridge.

Johansen, S. (1996): Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Advanced Texts in Econometrics, Oxford University Press, USA.

Luetkepohl, H. (2005): New Introduction to Multiple Time Series Analysis, Springer, 2nd ed.

Pfaff, B. (2008): "VAR, SVAR and SVEC Models: Implementation within R Package vars", Journal of Statistical Software, 27, pp. 1-32.

See Also

... the original SVEC by Pfaff (2008) in vars. Note that pid.grt relies on this underlying procedure, but allows for the additional model specifications in pvarx.VEC and for the bootstrap procedures in sboot.pmb, both provided by the pvars package.

Other panel identification functions: pid.chol(), pid.cvm(), pid.dc(), pid.iv()

Examples

data("PCAP")
names_k = c("g", "k", "l", "y")  # variable names
names_i = levels(PCAP$id_i)      # country names
names_s = NULL                   # optional shock names
L.data  = sapply(names_i, FUN=function(i) 
  ts(PCAP[PCAP$id_i==i, names_k], start=1960, end=2019, frequency=1), 
  simplify=FALSE)

# colnames of the restriction matrices are passed as shock names #
SR = matrix(NA, nrow=4, ncol=4, dimnames=list(names_k, names_s))
SR[1, 2] = 0
SR[3, 4] = 0
LR = matrix(NA, nrow=4, ncol=4, dimnames=list(names_k, names_s))
LR[ , 3:4] = 0

# estimate and identify panel SVECM #
R.pvec = pvarx.VEC(L.data, lags=2, dim_r=2, type="Case4")
R.pid  = pid.grt(R.pvec, LR=LR, SR=SR)