depracate/include.R

In kvasilopoulos/psvar: What the Package Does (One Line, Title Case)

# DATA <- ramey_econ214
# VAR <- psvar(data = DATA[, c(5, 2, 4, 6)], mshock = DATA[, 10])
# # 
# # Data preparation --------------------------------------------------------
# 
# 
# VARNA <- list()
# VARNA$vars <- as.matrix(DATA[, c(5, 2, 4, 6)])
# VARNA$p <- 12
# t <- nrow(VARNA$vars)
# n <- ncol(VARNA$vars)
# VARNA$DET <- ones(t, 1)
# VARNA$irhor <- 48
# VARNA$mshocks <- as.matrix(DATA[, 10])
# 
# # Demean the shock
# VARNA$mshocks - mean(VARNA$mshocks, na.rm = TRUE)
# 
# DAT <- list()
# DAT$TRY <- 0.1746 # Average ratio of federal tax revenues to GDP
# DAT$GY <- 0.0982 # Average ratio of federal expenditures to GDP
# 
# VARNA$mshocksize <- 1
# VARNA$gshocksize <- 0.01 / DAT$GY # left in because one of the other programs needs it
# 
# 
# # Main Function -----------------------------------------------------------
# 
# VARSLAGS <- lagmatrix(VARNA$vars, VARNA$p)
# VARS <- VARNA$vars[-c(1:VARNA$p), ]
# MSHOCKS <- VARNA$mshocks[-c(1:VARNA$p), , drop = FALSE]
# 
# VARNA$t <- nrow(VARS)
# VARNA$n <- ncol(VARS)
# VARNA$k <- ncol(MSHOCKS)
# 
# # Reduced-form VAR
# matX <- cbind(VARSLAGS, VARNA$DET[-c(1:VARNA$p), ])
# bet <- reg_xy(matX, VARS)
# res <- VARS - matX %*% bet
# 
# 
# sigma <- VARNA$Sigma <- crossprod(res) / (VARNA$t - VARNA$n * VARNA$p - 1)
# Sig11 <- VARNA$Sigma[1:VARNA$k, 1:VARNA$k]
# Sig21 <- VARNA$Sigma[(VARNA$k + 1):VARNA$n, 1:VARNA$k, drop = FALSE]
# Sig22 <- VARNA$Sigma[(VARNA$k + 1):VARNA$n, (VARNA$k + 1):VARNA$n]
# 
# # Narrative Identification
# LAMb = reg_xy(MSHOCKS, res)
# LAMb11  = LAMb[1:VARNA$k, 1:VARNA$k]
# LAMb21  = LAMb[1:VARNA$k, (VARNA$k+1):VARNA$n]
# 
# # Restriction
# b21ib11 <- t(reg_xy(LAMb11, LAMb21))
# ZZp <- b21ib11 %*% Sig11 %*% t(b21ib11) - (Sig21 %*% t(b21ib11) + b21ib11 %*% t(Sig21)) + Sig22
# b12b12p <- t(Sig21 - b21ib11 %*% Sig11) %*% mat_div(ZZp, (Sig21 - b21ib11 %*% Sig11))
# b11b11p <- Sig11 - b12b12p
# b22b22p <- ZZp + (Sig21 - b21ib11 %*% Sig11) %*% t(b21ib11) + b21ib11 %*% t(Sig21 - b21ib11 %*% Sig11) + b21ib11 %*% b12b12p %*% t(b21ib11)
# 
# 
# b22 <- t(chol(b22b22p))
# b12 <- (t(Sig21 - b21ib11 %*% Sig11) + b12b12p %*% t(b21ib11)) %*% solve(t(b22))
# b12ib22 <- b12 %*% inv(b22)
# 
# 
# VARNA$sigmaG <- b22[1, 1]
# VARNA$thetaG <- b12ib22[, 1]
# VARNA$thetaY <- b12ib22[, 2]
# VARNA$thetaW <- b12ib22[, 3]
# 
# b11iSig <- inv(eye(VARNA$k) - b12ib22 %*% b21ib11)
# b21iSig <- b21ib11 %*% b11iSig
# 
# VARNA$gammaT <- t(b21iSig[1, ])
# 
# F2 <- b22[2, 1] / VARNA$sigmaG - b21iSig[2, ] %*% VARNA$thetaG
# F1 <- t(-F2 %*% t(VARNA$gammaT) + (1 - t(VARNA$gammaT) %*% VARNA$thetaG) %*% b21iSig[2.])
# VARNA$zetaT <- mat_div(eye(VARNA$k) %*% (1 - t(VARNA$gammaT) %*% VARNA$thetaG) + F1 %*% t(VARNA$thetaY), F1)
# VARNA$zetaG <- mat_div((1 - t(VARNA$zetaT) %*% VARNA$thetaY), (1 - t(VARNA$gammaT) %*% VARNA$thetaG) %*% F2)
# VARNA$sigmaY <- (1 - t(VARNA$zetaT) %*% VARNA$thetaY) %*% b22[2, 2]
# SigmaTSigmaTp <- mat_div(b11iSig, b11b11p) %*% solve(t(b11iSig))
# 
# 
# 
# # Impulse Response to a monetary Shock
# VARNA$SigmaT <- array(numeric(), c(1, 1, VARNA$k))
# VARNA$D <- array(numeric(), c(VARNA$n, VARNA$n, 1))
# irs <- matrix(0, nrow = VARNA$p + VARNA$irhor, VARNA$n)
# VARNA$irs <- VARNA$irsTRY <- array(numeric(), c(VARNA$irhor, VARNA$n, VARNA$k))
# for (j in 1:VARNA$k) {
#   ind <- 1:VARNA$k
#   ind[VARNA$k] <- j
#   ind[j] <- VARNA$k
# 
#   SSp <- SigmaTSigmaTp[ind, ind]
#   SigmaT <- t(chol(SSp))
# 
#   VARNA$SigmaT[, , j] <- SigmaT[ind, ind]
#   
#   b11 <- b11iSig %*% VARNA$SigmaT[, , j]
#   b21 <- b11iSig %*% VARNA$SigmaT[, , j]
#   VARNA$D[, , j] <- rbind(
#     cbind(b11, b12),
#     cbind(b21, b22)
#   )
# 
#   irs[VARNA$p + 1, ] <- VARNA$D[, j, j] %*% solve(VARNA$D[j, j, j]) %*% VARNA$mshocksize[j]
#   for (jj in 2:VARNA$irhor) {
#     lvars <- t(irs[seq(VARNA$p + jj - 1, jj, -1), ])
#     irs[VARNA$p + jj, ] <- t(c(lvars)) %*% bet[1:(VARNA$p * VARNA$n), ]
#   }
#   VARNA$irs[, , j] <- irs[-c(1:VARNA$p), ]
# 
#   if (VARNA$k == 1) {
#     VARNA$irsTRY <- VARNA$irs[, 1, ] - VARNA$irs[, 3, ]
#   }
# }
# 
# # % Reliability
# # %%%%%%%%%%%%%%
# 
# # m = MSHOCKS;
# # ED    = eye(VAR$k) %*% sum(sum(m,2)~=0)/VAR.T;
# # mu1   = m'*res(:,1:VAR.k)/VAR.T;
# # b11 = sqrt(b11b11p);
# #
# # Bi = [1/b11+(Sig21-Sig11*b21ib11)'*inv(ZZp)/b11*b21ib11 -(Sig21-Sig11*b21ib11)'*inv(ZZp)/b11];
# # VAR.et = (Bi*res')'; % Estimated monetary Shocks
# #
# # PHI = mu1/b11;
# # GAM = inv(ED)*PHI;
# # E  = GAM*VAR.et(sum(m,2)~=0);
# # V  = m(sum(m,2)~=0)-E;
# # VAR.RM = inv(E'*E+V'*V)*E'*E;