From 18596040cc5b6d028b8908c86f354a408b336c30 Mon Sep 17 00:00:00 2001 From: Haeran Cho Date: Fri, 14 Jan 2022 23:19:39 +0000 Subject: [PATCH 1/8] update --- R/fnets.R | 22 ++++++++++------------ R/idio.R | 5 +++-- R/omega.R | 4 ++-- man/dyn.pca.Rd | 4 +++- man/fnets.Rd | 1 + man/fnets.var.Rd | 1 + man/plot.fnets.Rd | 2 +- man/var.lasso.Rd | 2 +- 8 files changed, 22 insertions(+), 19 deletions(-) diff --git a/R/fnets.R b/R/fnets.R index bc4dde9..6932f41 100644 --- a/R/fnets.R +++ b/R/fnets.R @@ -47,6 +47,7 @@ #' \item{\code{beta}}{ estimate of VAR parameter matrix; each column contains parameter estimates for the regression model for a given variable} #' \item{\code{Gamma}}{ estimate of the innovation covariance matrix} #' \item{\code{lambda}}{ regularisation parameter} +#' \item{\code{convergence}}{ returned when \code{idio.method = "lasso"}; indicates whether a convergence criterion is met} #' \item{\code{var.order}}{ VAR order} #' }} #' \item{lrpc}{ see the output of \link[fnets]{par.lrpc} if \code{lrpc.method = 'par'} @@ -136,6 +137,7 @@ fnets <- function(x, center = TRUE, q = NULL, ic.op = 5, kern.const = 4, #' their variations with logarithm taken on the cost (\code{ic.op = 4, 5} or \code{6}) are implemented, #' with \code{ic.op = 5} recommended as a default choice based on numerical experiments #' @param kern.const constant multiplied to \code{floor((dim(x)[2]/log(dim(x)[2]))^(1/3)))} which determines the kernel bandwidth for dynamic PCA +#' @param mm bandwidth; if \code{mm = NULL}, it is chosen using \code{kern.const} #' @return a list containing #' \item{q}{ number of factors} #' \item{hl}{ if \code{q = NULL}, the output from \link[fnets]{hl.factor.number}} @@ -144,12 +146,12 @@ fnets <- function(x, center = TRUE, q = NULL, ic.op = 5, kern.const = 4, #' \item{kern.const}{ input parameter} #' @importFrom stats fft #' @keywords internal -dyn.pca <- function(xx, q = NULL, ic.op = 4, kern.const = 4){ +dyn.pca <- function(xx, q = NULL, ic.op = 4, kern.const = 4, mm = NULL){ p <- dim(xx)[1] n <- dim(xx)[2] - mm <- min(max(1, kern.const * floor((n/log(n))^(1/3))), floor(n/4) - 1) + if(is.null(mm)) mm <- min(max(1, kern.const * floor((n/log(n))^(1/3))), floor(n/4) - 1) else mm <- min(max(mm, 1, kern.const * floor((n/log(n))^(1/3))), floor(n/4) - 1) len <- 2 * mm w <- Bartlett.weights(((-mm):mm)/mm) @@ -348,7 +350,7 @@ predict.fnets <- function(object, x, h = 1, common.method = c('restricted', 'unr #' @method plot fnets #' @description Plotting method for S3 objects of class \code{fnets}. #' Produces a plot visualising three networks underlying factor-adjusted VAR processes: -#' (i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices aggregated across the lags, +#' (i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices summed across the lags, #' (ii) undirected network representing contemporaneous linkages after accounting for lead-lag dependence, as given by partial correlations of VAR innovations, #' (iii) undirected network summarising (i) and (ii) as given by long-run partial correlations of VAR processes. #' @details See Barigozzi, Cho and Owens (2021) for further details. @@ -387,7 +389,7 @@ plot.fnets <- function(x, type = c('granger', 'pc', 'lrpc'), display = c('networ if(type == 'granger'){ d <- dim(x$idio.var$beta)[1]/p - for(ll in 1:d) A <- A + abs(t(x$idio.var$beta))[, (ll - 1) * p + 1:p] + for(ll in 1:d) A <- A + t(x$idio.var$beta)[, (ll - 1) * p + 1:p] nm <- 'Granger causal' } @@ -439,14 +441,10 @@ plot.fnets <- function(x, type = c('granger', 'pc', 'lrpc'), display = c('networ vertex.label.color = grp.col, vertex.label.cex = 0.6, edge.color = 'gray40', edge.arrow.size = 0.5) } else if(display == "heatmap"){ - if(type == 'granger'){ - heat.cols <- RColorBrewer::brewer.pal(9, 'Reds') - breaks <- seq(0, max(1e-3, abs(A)), length.out = 10) - } - if(type %in% c('pc', 'lrpc')){ - heat.cols <- rev(RColorBrewer::brewer.pal(11, 'RdBu')) - breaks <- seq(-1.01, 1.01, length.out = 12) - } + heat.cols <- rev(RColorBrewer::brewer.pal(11, 'RdBu')) + if(type == 'granger') mv <- max(1e-3, abs(A)) + if(type %in% c('pc', 'lrpc')) mv <- 1.01 + breaks <- seq(-mv, mv, length.out = 12) fields::imagePlot(A, axes = FALSE, col = heat.cols, breaks = breaks, main = nm, ...) if(!is.na(names[1]) || !is.na(groups[1])){ diff --git a/R/idio.R b/R/idio.R index c66a443..75abf97 100644 --- a/R/idio.R +++ b/R/idio.R @@ -25,6 +25,7 @@ #' \item{beta}{ estimate of VAR parameter matrix; each column contains parameter estimates for the regression model for a given variable} #' \item{Gamma}{ estimate of the innovation covariance matrix} #' \item{lambda}{ regularisation parameter} +#' \item{convergence}{ returned when \code{method = "lasso"}; indicates whether a convergence criterion is met} #' \item{var.order}{ VAR order} #' \item{mean.x}{ if \code{center = TRUE}, returns a vector containing row-wise sample means of \code{x}; if \code{center = FALSE}, returns a vector of zeros} #' @example R/examples/var_ex.R @@ -179,8 +180,8 @@ yw.cv <- function(xx, method = c('lasso', 'ds'), train.ind <- 1:ceiling(length(ind.list[[fold]]) * .5) train.x <- xx[, ind.list[[fold]][train.ind]] test.x <- xx[, ind.list[[fold]][- train.ind]] - train.acv <- dyn.pca(train.x, q = q, kern.const = kern.const)$acv$Gamma_i - test.acv <- dyn.pca(test.x, q = q, kern.const = kern.const)$acv$Gamma_i + train.acv <- dyn.pca(train.x, q = q, kern.const = kern.const, mm = max(var.order))$acv$Gamma_i + test.acv <- dyn.pca(test.x, q = q, kern.const = kern.const, mm = max(var.order))$acv$Gamma_i for(jj in 1:length(var.order)){ mg <- make.gg(train.acv, var.order[jj]) diff --git a/R/omega.R b/R/omega.R index 99254a4..4275202 100644 --- a/R/omega.R +++ b/R/omega.R @@ -167,8 +167,8 @@ direct.cv <- function(object, xx, target = c('spec', 'acv'), symmetric = c('min' test.GG <- Re(dyn.pca(test.x, q = q, kern.const = kern.const)$spec$Sigma_i[,, 1]) } if(target == 'acv'){ - train.G0 <- dyn.pca(train.x, q = q, kern.const = kern.const)$acv$Gamma_i - test.G0 <- dyn.pca(test.x, q = q, kern.const = kern.const)$acv$Gamma_i + train.G0 <- dyn.pca(train.x, q = q, kern.const = kern.const, mm = d)$acv$Gamma_i + test.G0 <- dyn.pca(test.x, q = q, kern.const = kern.const, mm = d)$acv$Gamma_i train.GG <- train.G0[,, 1] test.GG <- test.G0[,, 1] for(ll in 1:d){ diff --git a/man/dyn.pca.Rd b/man/dyn.pca.Rd index 2e5bd96..b292b92 100644 --- a/man/dyn.pca.Rd +++ b/man/dyn.pca.Rd @@ -4,7 +4,7 @@ \alias{dyn.pca} \title{Dynamic PCA} \usage{ -dyn.pca(xx, q = NULL, ic.op = 4, kern.const = 4) +dyn.pca(xx, q = NULL, ic.op = 4, kern.const = 4, mm = NULL) } \arguments{ \item{xx}{centred input time series matrix, with each row representing a variable} @@ -16,6 +16,8 @@ their variations with logarithm taken on the cost (\code{ic.op = 4, 5} or \code{ with \code{ic.op = 5} recommended as a default choice based on numerical experiments} \item{kern.const}{constant multiplied to \code{floor((dim(x)[2]/log(dim(x)[2]))^(1/3)))} which determines the kernel bandwidth for dynamic PCA} + +\item{mm}{bandwidth; if \code{mm = NULL}, it is chosen using \code{kern.const}} } \value{ a list containing diff --git a/man/fnets.Rd b/man/fnets.Rd index 4b23f0c..113619d 100644 --- a/man/fnets.Rd +++ b/man/fnets.Rd @@ -72,6 +72,7 @@ and common shocks (an array of dimension \code{(q, n)}) for the common component \item{\code{beta}}{ estimate of VAR parameter matrix; each column contains parameter estimates for the regression model for a given variable} \item{\code{Gamma}}{ estimate of the innovation covariance matrix} \item{\code{lambda}}{ regularisation parameter} +\item{\code{convergence}}{ returned when \code{idio.method = "lasso"}; indicates whether a convergence criterion is met} \item{\code{var.order}}{ VAR order} }} \item{lrpc}{ see the output of \link[fnets]{par.lrpc} if \code{lrpc.method = 'par'} diff --git a/man/fnets.var.Rd b/man/fnets.var.Rd index ffba7d6..a0186c8 100644 --- a/man/fnets.var.Rd +++ b/man/fnets.var.Rd @@ -50,6 +50,7 @@ a list which contains the following fields: \item{beta}{ estimate of VAR parameter matrix; each column contains parameter estimates for the regression model for a given variable} \item{Gamma}{ estimate of the innovation covariance matrix} \item{lambda}{ regularisation parameter} +\item{convergence}{ returned when \code{method = "lasso"}; indicates whether a convergence criterion is met} \item{var.order}{ VAR order} \item{mean.x}{ if \code{center = TRUE}, returns a vector containing row-wise sample means of \code{x}; if \code{center = FALSE}, returns a vector of zeros} } diff --git a/man/plot.fnets.Rd b/man/plot.fnets.Rd index 18a43e4..7512dc1 100644 --- a/man/plot.fnets.Rd +++ b/man/plot.fnets.Rd @@ -41,7 +41,7 @@ \description{ Plotting method for S3 objects of class \code{fnets}. Produces a plot visualising three networks underlying factor-adjusted VAR processes: -(i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices aggregated across the lags, +(i) directed network representing Granger causal linkages, as given by estimated VAR transition matrices summed across the lags, (ii) undirected network representing contemporaneous linkages after accounting for lead-lag dependence, as given by partial correlations of VAR innovations, (iii) undirected network summarising (i) and (ii) as given by long-run partial correlations of VAR processes. } diff --git a/man/var.lasso.Rd b/man/var.lasso.Rd index 28de1b6..a4d2afa 100644 --- a/man/var.lasso.Rd +++ b/man/var.lasso.Rd @@ -4,7 +4,7 @@ \alias{var.lasso} \title{Lasso-type estimator of VAR processes via \code{l1}-regularised \code{M}-estimation} \usage{ -var.lasso(GG, gg, lambda, symmetric = "min", n.iter = 100, tol = 0) +var.lasso(GG, gg, lambda, symmetric = "min", n.iter = 100, tol = 1e-05) } \description{ Lasso-type estimator of VAR processes via \code{l1}-regularised \code{M}-estimation From 6ce2023be5c0e9572a27dbff5cd553a0aab4e410 Mon Sep 17 00:00:00 2001 From: Haeran Cho Date: Fri, 14 Jan 2022 23:33:10 +0000 Subject: [PATCH 2/8] update rd --- R/fnets.R | 11 +++++------ R/idio.R | 8 +++----- man/fnets.Rd | 10 +++++----- man/fnets.var.Rd | 2 +- man/var.lasso.Rd | 2 +- 5 files changed, 15 insertions(+), 18 deletions(-) diff --git a/R/fnets.R b/R/fnets.R index a691537..0fdd953 100644 --- a/R/fnets.R +++ b/R/fnets.R @@ -25,12 +25,10 @@ #' } #' @param idio.args a list specifying the tuning parameters required for estimating the idiosyncratic VAR process. It contains: #' \itemize{ -#' \item{\code{n.iter}}{ maximum number of descent steps; applicable when \code{method = "lasso"}} -#' \item{\code{tol}}{ numerical tolerance for increases in the loss function; applicable when \code{method = "lasso"}} -#' \item{\code{n.cores}}{ number of cores to use for parallel computing, see \link[parallel]{makePSOCKcluster}; applicable when \code{method = "ds"}} +#' \item{\code{n.iter}}{ maximum number of descent steps; applicable when \code{idio.method = "lasso"}} +#' \item{\code{tol}}{ numerical tolerance for increases in the loss function; applicable when \code{idio.method = "lasso"}} +#' \item{\code{n.cores}}{ number of cores to use for parallel computing, see \link[parallel]{makePSOCKcluster}; applicable when \code{idio.method = "ds"}} #' } -#' @param -#' @param #' @param lrpc.method a string specifying the type of estimator for long-run partial correlation matrix estimation; possible values are: #' \itemize{ #' \item{\code{"par"}}{ parametric estimator based on the VAR model assumption} @@ -44,6 +42,7 @@ #' \item{\code{path.length}}{ number of regularisation parameter values to consider; a sequence is generated automatically based in this value} #' \item{\code{do.plot}}{ whether to plot the output of the cross validation step} #' } +#' #' @return an S3 object of class \code{fnets}, which contains the following fields: #' \item{q}{ number of factors} #' \item{spec}{ a list containing estimates of the spectral density matrices for \code{x}, common and idiosyncratic components} @@ -87,7 +86,7 @@ fnets <- function(x, center = TRUE, q = NULL, ic.op = 5, kern.const = 4, common.args = list(var.order = NULL, max.var.order = NULL, trunc.lags = 20, n.perm = 10), idio.var.order = 1, idio.method = c('lasso', 'ds'), - idio.args = list(n.iter = 100, tol = 1e-5, n.cores = min(parallel::detectCores() - 1, 3)), + idio.args = list(n.iter = 100, tol = 0, n.cores = min(parallel::detectCores() - 1, 3)), lrpc.method = c('par', 'npar', 'none'), cv.args = list(n.folds = 1, path.length = 10, do.plot = FALSE)){ p <- dim(x)[1] diff --git a/R/idio.R b/R/idio.R index 0de79bc..1161a68 100644 --- a/R/idio.R +++ b/R/idio.R @@ -35,7 +35,7 @@ fnets.var <- function(x, center = TRUE, method = c('lasso', 'ds'), lambda = NULL, var.order = 1, cv.args = list(n.folds = 1, path.length = 10, do.plot = FALSE), - n.iter = 100, tol = 1e-5, n.cores = min(parallel::detectCores() - 1, 3)){ + n.iter = 100, tol = 0, n.cores = min(parallel::detectCores() - 1, 3)){ p <- dim(x)[1] n <- dim(x)[2] @@ -63,7 +63,7 @@ fnets.var <- function(x, center = TRUE, method = c('lasso', 'ds'), #' @title Lasso-type estimator of VAR processes via \code{l1}-regularised \code{M}-estimation #' @keywords internal -var.lasso <- function(GG, gg, lambda, symmetric = 'min', n.iter = 100, tol = 1e-5){ +var.lasso <- function(GG, gg, lambda, symmetric = 'min', n.iter = 100, tol = 0){ backtracking <- TRUE p <- ncol(gg) @@ -106,13 +106,11 @@ var.lasso <- function(GG, gg, lambda, symmetric = 'min', n.iter = 100, tol = 1e- obj.val <- c(obj.val, f.func(GG, gg, x.new) + lambda * sum(abs(x.new))) if(ii > 1) diff.val <- obj.val[ii] - obj.val[ii - 1] } - if(ii == n.iter) warning("lasso estimation did not converge") - A <- t(x.new) Gamma <- GG[1:p, 1:p] for(ll in 1:d) Gamma <- Gamma - A[, (ll - 1) * p + 1:p] %*% gg[(ll - 1) * p + 1:p, ] Gamma <- make.symmetric(Gamma, symmetric) - out <- list(beta = x.new, Gamma = Gamma, lambda = lambda, convergence = (abs(diff.val) <= abs(obj.val[1]) * tol)) + out <- list(beta = x.new, Gamma = Gamma, lambda = lambda, convergence = (abs(diff.val) <= abs(obj.val[1]) * 1e-5)) return(out) diff --git a/man/fnets.Rd b/man/fnets.Rd index fb0fda3..241d8c9 100644 --- a/man/fnets.Rd +++ b/man/fnets.Rd @@ -14,8 +14,8 @@ fnets( 10), idio.var.order = 1, idio.method = c("lasso", "ds"), - idio.args = list(n.iter = 100, tol = 1e-05, n.cores = min(parallel::detectCores() - - 1, 3)), + idio.args = list(n.iter = 100, tol = 0, n.cores = min(parallel::detectCores() - 1, + 3)), lrpc.method = c("par", "npar", "none"), cv.args = list(n.folds = 1, path.length = 10, do.plot = FALSE) ) @@ -49,9 +49,9 @@ fnets( \item{idio.args}{a list specifying the tuning parameters required for estimating the idiosyncratic VAR process. It contains: \itemize{ - \item{\code{n.iter}}{ maximum number of descent steps; applicable when \code{method = "lasso"}} - \item{\code{tol}}{ numerical tolerance for increases in the loss function; applicable when \code{method = "lasso"}} - \item{\code{n.cores}}{ number of cores to use for parallel computing, see \link[parallel]{makePSOCKcluster}; applicable when \code{method = "ds"}} + \item{\code{n.iter}}{ maximum number of descent steps; applicable when \code{idio.method = "lasso"}} + \item{\code{tol}}{ numerical tolerance for increases in the loss function; applicable when \code{idio.method = "lasso"}} + \item{\code{n.cores}}{ number of cores to use for parallel computing, see \link[parallel]{makePSOCKcluster}; applicable when \code{idio.method = "ds"}} }} \item{lrpc.method}{a string specifying the type of estimator for long-run partial correlation matrix estimation; possible values are: diff --git a/man/fnets.var.Rd b/man/fnets.var.Rd index c0ba94e..a0186c8 100644 --- a/man/fnets.var.Rd +++ b/man/fnets.var.Rd @@ -12,7 +12,7 @@ fnets.var( var.order = 1, cv.args = list(n.folds = 1, path.length = 10, do.plot = FALSE), n.iter = 100, - tol = 1e-05, + tol = 0, n.cores = min(parallel::detectCores() - 1, 3) ) } diff --git a/man/var.lasso.Rd b/man/var.lasso.Rd index a4d2afa..28de1b6 100644 --- a/man/var.lasso.Rd +++ b/man/var.lasso.Rd @@ -4,7 +4,7 @@ \alias{var.lasso} \title{Lasso-type estimator of VAR processes via \code{l1}-regularised \code{M}-estimation} \usage{ -var.lasso(GG, gg, lambda, symmetric = "min", n.iter = 100, tol = 1e-05) +var.lasso(GG, gg, lambda, symmetric = "min", n.iter = 100, tol = 0) } \description{ Lasso-type estimator of VAR processes via \code{l1}-regularised \code{M}-estimation From 633a4defcf918bebabe2772c30162679a3f9d9ff Mon Sep 17 00:00:00 2001 From: Haeran Cho Date: Sat, 15 Jan 2022 21:14:48 +0000 Subject: [PATCH 3/8] update --- R/fnets.R | 7 ++++++- R/omega.R | 22 ++++++++++++++++++++-- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/R/fnets.R b/R/fnets.R index 0fdd953..ff4d239 100644 --- a/R/fnets.R +++ b/R/fnets.R @@ -451,8 +451,13 @@ plot.fnets <- function(x, type = c('granger', 'pc', 'lrpc'), display = c('networ } else if(display == "heatmap"){ heat.cols <- rev(RColorBrewer::brewer.pal(11, 'RdBu')) if(type == 'granger') mv <- max(1e-3, abs(A)) - if(type %in% c('pc', 'lrpc')) mv <- 1.01 + if(type %in% c('pc', 'lrpc')){ + A[abs(A) > 1] <- sign(A[abs(A) > 1]) + diag(A) <- 0 + mv <- 1.01 + } breaks <- seq(-mv, mv, length.out = 12) + fields::imagePlot(A, axes = FALSE, col = heat.cols, breaks = breaks, main = nm, ...) if(!is.na(names[1]) || !is.na(groups[1])){ diff --git a/R/omega.R b/R/omega.R index 4275202..f0786f9 100644 --- a/R/omega.R +++ b/R/omega.R @@ -122,7 +122,7 @@ par.lrpc <- function(object, x, eta = NULL, Delta <- direct.inv.est(GG, eta = eta, symmetric = 'min', do.correct = do.correct, n.cores = n.cores)$DD Omega <- 2 * pi * t(A1) %*% Delta %*% A1 - if(do.correct & sum(diag(Omega) <= 0) > 0) Omega <- correct.diag(Re(object$spec$Sigma_i[,, 1]), Omega) + if(do.correct) Omega <- correct.diag(Re(object$spec$Sigma_i[,, 1]), Omega) pc <- - t(t(Delta)/sqrt(diag(Delta)))/sqrt(diag(Delta)) lrpc <- - t(t(Omega)/sqrt(diag(Omega)))/sqrt(diag(Omega)) @@ -229,7 +229,7 @@ direct.inv.est <- function(GG, eta = NULL, symmetric = c('min', 'max', 'avg', ' parallel::stopCluster(cl) DD <- make.symmetric(DD, symmetric) - if(do.correct & sum(diag(DD) <= 0) > 0) DD <- correct.diag(GG, DD) + if(do.correct) DD <- correct.diag(GG, DD) out <- list(DD = DD, eta = eta, symmetric = symmetric) return(out) @@ -250,9 +250,27 @@ gen.inverse <- function(GG){ #' @keywords internal correct.diag <- function(GG, DD){ + p <- dim(GG)[1] tmp <- gen.inverse(GG) ind <- which(diag(DD) <= 0) diag(DD)[ind] <- tmp[ind] + + # ind0 <- setdiff(1:p, ind) + # mat <- t(t(DD[ind0, ind0])/sqrt(diag(DD)[ind0]))/sqrt(diag(DD)[ind0]) + # ind <- c(ind, ind0[apply(abs(mat), 1, max) > 1]) + # ind0 <- setdiff(1:p, ind) + # if(length(ind) > 0){ + # for(ii in ind) DD[ii, ii] <- max(tmp[ii], (DD[ii, ind0]/sqrt(diag(DD)[ind0]))^2) + # mat <- t(t(DD[ind, ind])/sqrt(diag(DD)[ind]))/sqrt(diag(DD)[ind]) + # while(max(abs(mat)) - 1 > 1e-10){ + # for(ii in ind[which(apply(abs(mat), 1, max) > 1)]){ + # ind1 <- setdiff(ind, ii) + # DD[ii, ii] <- max(tmp[ii], (DD[ii, ind1]/sqrt(diag(DD)[ind1]))^2) + # } + # mat <- t(t(DD[ind, ind])/sqrt(diag(DD)[ind]))/sqrt(diag(DD)[ind]) + # } + # } + return(DD) } From db4394bd2a38fc7823ee1aa3f4fac2f7875a09b4 Mon Sep 17 00:00:00 2001 From: Haeran Cho Date: Mon, 17 Jan 2022 13:12:41 +0000 Subject: [PATCH 4/8] Update fnets_0.1.0.pdf --- fnets_0.1.0.pdf | Bin 116053 -> 117318 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/fnets_0.1.0.pdf b/fnets_0.1.0.pdf index 71a3ae80e18fa779efa76313505407b98fa2a49e..0832156892c6c205fab8dc931674b7c9988e3a23 100644 GIT binary patch delta 70459 zcmZs?Q*hu-)a@PH&cx=#HYT=h+r~e(ZQHgp;RF*+Y}?lNyyrW2=dJ3CzUu0#UA4OR 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zJsZcc#&c)T8|@faJf9^!#9>xZ{RHt1|DAUV>Rh5VYu=d;gdB7ClfX3-G|o54D7#Y< zhVp@IuW2V9B36T|9N6s2?ee|m)($2Zxp2R-jXv+2E6i(ztu|1N$wuo~3 z0WA(NHxOgjKRqTx^O;dy1B_Bct7N;fUT}*MH&F|p56U7?t&`yPo4;yxY?D zT%la2yM;EiCTMvER!H@7Fu#;K% z<#R0~Wlh~JX}{#;2gxn+=gHKSId}y3P&BdEx-JpH!wADPsbkKALY#kKWJl)sO)=cn|dK-{f38DZ}M5 ztQ5;#^Hn_YEA=aNjB-B2=P0dK9q?+QxLa`k;;nN>L2%tG9d{;8v~33ycI|l~Ef@TL zU-(r-g}bkqiLma69~E$p8%gF$<$B01avK1+*SXh~NKJ}S{_o$X9fG Date: Wed, 19 Jan 2022 08:54:27 +0000 Subject: [PATCH 5/8] arxiv update --- R/common.R | 4 ++-- R/fnets.R | 6 +++--- R/idio.R | 4 ++-- R/omega.R | 2 +- R/sim.R | 2 +- README.md | 2 +- fnets_0.1.0.pdf | Bin 117318 -> 117741 bytes man/common.irf.estimation.Rd | 2 +- man/common.predict.Rd | 2 +- man/fnets.Rd | 2 +- man/fnets.var.Rd | 2 +- man/idio.predict.Rd | 2 +- man/par.lrpc.Rd | 2 +- man/plot.fnets.Rd | 2 +- man/predict.fnets.Rd | 2 +- man/sim.common1.Rd | 2 +- 16 files changed, 19 insertions(+), 19 deletions(-) diff --git a/R/common.R b/R/common.R index 09c58be..95876eb 100644 --- a/R/common.R +++ b/R/common.R @@ -17,7 +17,7 @@ #' \item{fc}{ forecasts of the common component for a given forecasting horizon \code{h}} #' \item{r}{ static factor number} #' \item{h}{ forecast horizon} -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @references Ahn, S. C. & Horenstein, A. R. (2013) Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203--1227. #' @references Forni, M., Hallin, M., Lippi, M. & Reichlin, L. (2005). The generalized dynamic factor model: one-sided estimation and forecasting. Journal of the American Statistical Association, 100(471), 830--840. #' @references Forni, M., Hallin, M., Lippi, M. & Zaffaroni, P. (2017). Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis. Journal of Econometrics, 199(1), 74--92. @@ -51,7 +51,7 @@ common.predict <- function(object, x, h = 1, common.method = c('restricted', 'un #' @title Blockwise VAR estimation under GDFM #' @references Forni, M., Hallin, M., Lippi, M. & Zaffaroni, P. (2017). Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis. Journal of Econometrics, 199(1), 74--92. -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @keywords internal common.irf.estimation <- function(xx, Gamma_c, q, var.order = NULL, max.var.order = NULL, trunc.lags, n.perm){ diff --git a/R/fnets.R b/R/fnets.R index ff4d239..8a819a3 100644 --- a/R/fnets.R +++ b/R/fnets.R @@ -64,7 +64,7 @@ #' \item{lrpc.method}{ input parameter} #' \item{kern.const}{ input parameter} #' -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @references Hallin, M. & Liška, R. (2007) Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603--617. #' @examples #' \dontrun{ @@ -338,7 +338,7 @@ hl.factor.number <- function(x, q.max = NULL, mm, w = NULL, do.plot = FALSE, cen #' \item{common.pred}{ a list containing forecasting results for the common component} #' \item{idio.pred}{ a list containing forecasting results for the idiosyncratic component} #' \item{mean.x}{ \code{mean.x} argument from \code{object}} -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @references Ahn, S. C. & Horenstein, A. R. (2013) Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203--1227. #' @seealso \link[fnets]{fnets}, \link[fnets]{common.predict}, \link[fnets]{idio.predict} #' @export @@ -378,7 +378,7 @@ predict.fnets <- function(object, x, h = 1, common.method = c('restricted', 'unr #' @param groups an integer vector denoting any group structure of the vertices #' @param threshold if \code{threshold > 0}, hard thresholding is performed on the matrix giving rise to the network of interest #' @param ... additional arguments -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @seealso \link[fnets]{fnets} #' @import igraph #' @importFrom fields imagePlot diff --git a/R/idio.R b/R/idio.R index 1161a68..79df106 100644 --- a/R/idio.R +++ b/R/idio.R @@ -30,7 +30,7 @@ #' \item{mean.x}{ if \code{center = TRUE}, returns a vector containing row-wise sample means of \code{x}; if \code{center = FALSE}, returns a vector of zeros} #' @example R/examples/var_ex.R #' @importFrom parallel detectCores -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @export fnets.var <- function(x, center = TRUE, method = c('lasso', 'ds'), lambda = NULL, var.order = 1, @@ -223,7 +223,7 @@ yw.cv <- function(xx, method = c('lasso', 'ds'), #' \item{is}{ in-sample estimator of the idiosyncratic component} #' \item{fc}{ forecasts of the idiosyncratic component for a given forecasting horizon \code{h}} #' \item{h}{ forecast horizon} -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @examples #' set.seed(123) #' n <- 500 diff --git a/R/omega.R b/R/omega.R index f0786f9..f37ea87 100644 --- a/R/omega.R +++ b/R/omega.R @@ -79,7 +79,7 @@ npar.lrpc <- function(object, x, eta = NULL, #' \item{pc}{ estimated innovation partial correlation matrix} #' \item{lrpc}{ estimated long-run partial correlation matrix} #' \item{eta}{ regularisation parameter} -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110. #' @examples #' \dontrun{ #' set.seed(123) diff --git a/R/sim.R b/R/sim.R index 4e1b9c8..7d573df 100644 --- a/R/sim.R +++ b/R/sim.R @@ -8,7 +8,7 @@ #' @return a list containing #' \item{data}{ generated series} #' \item{q}{ number of factors} -#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. +#' @references Barigozzi, M., Cho, H. & Owens, D. (2021) FNETS: Factor-adjusted network analysis for high-dimensional time series. arXiv preprint arXiv:2201.06110 #' @examples #' common <- sim.common1(500, 50) #' @importFrom stats rnorm runif rt diff --git a/README.md b/README.md index ba0bee4..bb661f1 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Contains methods for network estimation and forecasting for high-dimensional tim > _FNETS_: Factor-adjusted network estimation and forecasting for high-dimensional time series -by Matteo Barigozzi, Haeran Cho and Dom Owens [arXiv:](arXiv link to follow) for full details. +by Matteo Barigozzi, Haeran Cho and Dom Owens [arXiv:2201.06110](https://arxiv.org/abs/2201.06110) for full details. ## Installation diff --git a/fnets_0.1.0.pdf b/fnets_0.1.0.pdf index 0832156892c6c205fab8dc931674b7c9988e3a23..f611a7b188e0d61dca049e866be336e2c98720dc 100644 GIT binary patch delta 36356 zcmZs=Q*svI+C@p@--VLf($FS8ABlj1osAYzm4>)@+z;6{z&s5N1y~q-Cd-_YlAiGPbbd_ zMz5U^6E&m$x~)zI`fI|9fN?xrN{0& 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zJzy{B2Kzt{*bfeXgWwS0JE|k#M3mCuGd&9Uq3RgFb`eYfKBhX(XdFyY_Jc9t6Mx9Z z_%wJm@oPE(CINp3p$Dw>Z;XJxDD|JM;u7TmBOb0!0?xEL4Tiul@LzeWYBWmAJHPW8 zmR(_V7Muqc0B>4(XTSY4n26H3;AQ>uwoCJ_N~v4wb%5{&|4Cd6wv z%F-(@L%e@*F0Q>0FT~3cMR$_FB(}n(a4F;o$*P56rO{iF1Ca<32C-H%07sgQvNq-C z&@FU}M2HR|{E8bOtjg_%#-saDo`3Q(?92B9tHP?VDqD5*d=0F&F5=6IOVnmVl$R|o zv6@&-tR_|ytBKX*<|QuEjSQiQ&_rk=G!dHaFGPPJ-BnPVT-YSG>4t~eL~WurQJdIL zdPyQVx!sA{B=YH=h~#wNsDiN!P1Qf3+Zl)ibO@CB2kg3$eN0n zDG^iO#`#Y(6QBZhpdK`UMz9Vvfo9MGT0t9V2kSvc#Mk$=O}}@Se+!rTMhazaWOH Date: Wed, 19 Jan 2022 08:57:00 +0000 Subject: [PATCH 6/8] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index bb661f1..72ee2b4 100644 --- a/README.md +++ b/README.md @@ -44,6 +44,11 @@ out$lrpc.method <- 'par' plot(out, type = "lrpc", display = "heatmap") ``` +Of course, we can estimate the (long-run) partial correlation-based networks directly using `fnets`: +``` +out <- fnets(x, q = 2, idio.var.order = 1, idio.method = "lasso", lrpc.method = "par") +``` + Perform h-step ahead forecasting ``` pr <- predict(out, x, h = 1, common.method = "restricted") From 12be79dc8f2b3bb7a725aaf306951e7952d3c9a5 Mon Sep 17 00:00:00 2001 From: Haeran Cho Date: Tue, 1 Feb 2022 19:04:22 +0000 Subject: [PATCH 7/8] update --- fnets_0.1.0.tar.gz | Bin 0 -> 21029 bytes fnets_0.1.0.tgz | Bin 0 -> 136687 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 fnets_0.1.0.tar.gz create mode 100644 fnets_0.1.0.tgz diff --git a/fnets_0.1.0.tar.gz b/fnets_0.1.0.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..340d806edec54874e82f6f53e446bd847f6d67c0 GIT binary patch literal 21029 zcmV(*K;FL}iwFP!000001MPi#f7?cq;QY-`f$=yiz@aFTdS&D&zm=`n$*q&|*_qwD z)hG!BLL_1kpa4)0>-b^r!`%0}dh`U-?0iX6ydoHyEsN z;QSaC7ispv`8F@2v0p@K;`qtHd6i~i;6qt5bWYQ(<%oaZMZ?khAR32B4psfw`3ZhI z@53w#^VWZc8J6xj-A=F5Z5>RDQJUrd{%+3+T!Jux3Tfg#`N1!WFn!W?p1kn0XqbNd 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If \code{var.order = NULL}, it is selected blockwise by Schwarz criterion} #' \item{\code{max.var.order}}{ maximum blockwise VAR order for the Schwarz criterion} #' \item{\code{trunc.lags}}{ truncation lag for impulse response function estimation} -#' \item{\code{n.perm}}{ number of cross-sectional permutations involved in impluse response function estimation} +#' \item{\code{n.perm}}{ number of cross-sectional permutations involved in impulse response function estimation} #' } #' @param idio.var.order order of the idiosyncratic VAR process; if a vector of integers is supplied, the order is chosen via cross validation #' @param idio.method a string specifying the method to be adopted for idiosyncratic VAR process estimation; possible values are: