Proxy SVAR #96

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gabrielkonecny wants to merge 28 commits into nk027:master from gabrielkonecny:master

.gitignore

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+Diff line change
@@ Expand Up / @@ -316,3 +316,5 @@ TSWLatexianTemp* @@
     *.glstex
     # End of https://www.gitignore.io/api/latex
+    tests/test_file_iv.R
+    tests/iv_testfile.R

DESCRIPTION

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+Diff line change
@@ Expand Up / @@ -6,12 +6,15 @@ Date: 2024-02-13 @@
     Authors@R: c(person("Nikolas", "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at", comment = c(ORCID = "0000-0002-6642-2543")),
         person("Lukas", "Vashold", role = "aut", comment = c(ORCID = "0000-0002-3562-3414")),
         person("Nirai", "Tomass", role = "ctb"),
+        person("Gabriel", "Konecny", role = "ctb"),
         person("Michael", "McCracken", role = "dtc"),
         person("Serena", "Ng", role = "dtc"))
     Author: Nikolas Kuschnig [aut, cre] (<https://orcid.org/0000-0002-6642-2543>),
         Lukas Vashold [aut] (<https://orcid.org/0000-0002-3562-3414>),
         Nirai Tomass [ctb],
-        Michael McCracken [dtc], Serena Ng [dtc]
+        Gabriel Konecny [ctb],
+        Michael McCracken [dtc], Serena Ng [dtc],
+        Silvia Miranda-Agrippino [dtc], Giovanni Ricco [dtc], Riccardo Degasperi [dtc]
     Maintainer: Nikolas Kuschnig <nikolas.kuschnig@wu.ac.at>
     Description: Estimation of hierarchical Bayesian vector autoregressive models
         following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.
@@ Expand All / @@ -28,4 +31,4 @@ Suggests: coda, vars, tinytest @@
     License: GPL-3 | file LICENSE
     Encoding: UTF-8
     LazyData: true
-    RoxygenNote: 7.3.1
+    RoxygenNote: 7.3.2

NAMESPACE

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+Diff line change
@@ Expand Up / @@ -50,6 +50,7 @@ S3method(print,bvar_irf_summary) @@
     S3method(print,bvar_resid)
     S3method(print,bvar_summary)
     S3method(print,bvar_vcovs)
+    S3method(print,check_iv)
     S3method(residuals,bvar)
     S3method(rmse,bvar)
     S3method(rmse,default)
@@ Expand Down @@

R/10_bvar.R

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@@ Expand Up / @@ -187,6 +187,10 @@ bvar <- function( @@
       variables <- name_deps(variables = colnames(data), M = M)
       explanatories <- name_expl(variables = variables, M = M, lags = lags)
+      # if(!is.null(irf$instrument)){instrument <- irf$instrument
+      # check_iv_result <- check_iv(Y, instrument)
+      # print(check_iv_result)}
       # Priors -----
@@ Expand Down @@

R/12_aux.R

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+Diff line change
@@ Expand Up / @@ -267,3 +267,124 @@ quantile_check <- function(conf_bands) { @@
       return(quants)
     }
+    #' Set dates
+    #'
+    #' Changes rownames to a character string containing dates. Common format of
+    #' dates or other index is needed if the length of the \emph{instrument} and
+    #' reduced form residuals differ. In such case, the identification is based on
+    #' a common subset of residuals and the instrument.
+    #'
+    #' @param data Numeric vector, matrix or a data.frame. Note that observations
+    #' are expected to be ordered from earliest to latest, and variables in the
+    #' columns.
+    #' @param start Starting date of the data in form "YYYY-MM-DD".
+    #' @param frequency Frequency of the data: "year", "quarter", "month", "week" or "day".
+    #'
+    #' @return Returns data with rownames containing sequence of dates as character.
+    set_dates <- function(data, start, frequency) {
+      # Check if the input is a data frame, matrix, or numeric vector
+      if (!is.data.frame(data) && !is.matrix(data) && !(is.vector(data) && is.numeric(data))) {
+        stop("Error: data must be either a data frame, a matrix, or a numeric vector.")
+      }
+      # Determine the number of elements to set row names for
+      num_elements <- if (is.data.frame(data)) {
+        nrow(data)
+      } else if (is.matrix(data)) {
+        nrow(data)
+      } else {
+        length(data)
+      }
+      # Set the row names or names based on the sequence of dates
+      rownames(data) <- seq(as.Date(start), by = frequency, length.out = num_elements)
+      return(data)
+    }
+    #' Check IV
+    #'
+    #' Intersects the \emph{residuals} and the \emph{instrument} based on an index
+    #' (e.g. dates) provided in rownames and names, respectively. Returns an error
+    #'  if the intersection is empty.
+    #'
+    #' @param residuals Data frame or numeric matrix. During estimation, draw specific residuals
+    #' are used and rownames are inherited from data.
+    #' @param instrument Numeric vector.
+    #' @param proxyvar character string. Variable for which instrument is provided -
+    #' Needs to correspond to one of the column names of the data used in
+    #' \emph{bvar}.
+    #' @param manual_matching If set to TRUE, user is not expected to specify common
+    #' index for the data and instrument. Instead the length of instrument needs to
+    #' match the length of residuals (= length(data inputted in bvar) - lags).
+    #' @return Returns a list of class \code{check_iv} with information on
+    #' values and indices corresponding to the intersection. Additionally the list
+    #' contains inputs provided to the function.
+    #' @noRd
+    check_iv <- function(residuals, instrument, manual_matching = FALSE,
+                         proxyvar) {
+    residuals <- data.frame(residuals)
+    if(manual_matching == TRUE){
+      dates_residuals <- NULL
+      dates_instrument <- NULL
+      common_dates <- NULL
+      if(!nrow(residuals) == length(instrument)){
+      stop("manual_matching is TRUE but the length of residuals does not match the length of instrument!
+      Note that residuals are shorter than the data inputted to bvar, since first l observations of data are not part of residuals,
+      where l corresponds to number of lags used.")
+      }
+      residuals_shortened <- residuals
+      instrument_shortened <- instrument
+    } else{
+      if (!proxyvar %in% colnames(residuals)) {
+        stop(paste("The proxied variable", proxyvar, "was not found in the column names of residuals."))
+      }
+      # Get the dates or other indices
+      dates_residuals <- rownames(residuals)
+      dates_instrument <- names(instrument)
+      # Find the intersection of dates (common dates)
+      common_dates <- intersect(dates_residuals, dates_instrument)
+      # If there are no matches and throw an error
+      if (length(common_dates) == 0) {
+        stop("Could not match instrument with the data; please provide common indices for both. See `bv_irf` for details.")
+        }
+      # Shorten both objects to the common dates
+      residuals_shortened <- residuals[common_dates,]
+      instrument_shortened <- instrument[common_dates]
+    }
+      out <- structure(mget(c("residuals", "instrument",
+                              "dates_residuals", "dates_instrument",
+                              "residuals_shortened", "instrument_shortened",
+                              "common_dates", "manual_matching")),
+                           class = "check_iv")
+      return(out)
+    }
+    get_swapped_index <- function(residuals, proxyvar) {
+      # Find the column index of the proxy variable
+      j <- which(colnames(residuals) == proxyvar)
+      # Create a sequence of column indices
+      index <- seq_len(ncol(residuals))
+      # Swap the first column index with the proxyvar column index
+      index[1] <- index[j]
+      index[j] <- 1
+      return(index)
+    }

R/60_irf_setup.R

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -8,15 +8,25 @@
  
    #' as well as if and what kind of identification should be used. See the Details

    #' section for further information on identification. Identification can be

    #' achieved via Cholesky decomposition, sign restrictions (Rubio-Ramirez,

    #' Waggoner and Zha, 2010), and zero and sign restrictions (Arias,

    #' Rubio-Ramirez and Waggoner, 2018).

    #' Waggoner and Zha, 2010), combination of zero and sign restrictions (Arias,

    #' Rubio-Ramirez and Waggoner, 2018) or using an external instrument

    #' (Stock and Watson 2012, Mertens and Ravn 2013)

    #'

    #' Identification can be performed via Cholesky decomposition, sign

    #' restrictions, or zero and sign restrictions. The algorithm

    #' for generating suitable sign restrictions follows Rubio-Ramirez, Waggoner

    #' and Zha (2010), while the one for zero and sign restrictions follows

    #' Arias, Rubio-Ramirez and Waggoner (2018).

    #' restrictions, zero and sign restrictions or using an external instrument.

    #' The algorithm for generating suitable sign restrictions follows

    #' Rubio-Ramirez, Waggoner and Zha (2010), while the one for zero and sign

    #' restrictions follows Arias, Rubio-Ramirez and Waggoner (2018).

    #' Note the possiblity of finding no suitable zero/sign restrictions.

    #' The proxy SVAR identification follows the implementation in

    #' Miranda-Agrippino and Ricco (2021) and examples make use of their instrument

    #' extended in Degasperi and Ricco (2021). The implemented proxy SVAR allows

    #' for identification using subsample of residuals. This can be useful if the

    #' instrument is not available for the full sample period. Therefore only

    #' a part of the residuals corresponding to the length of instrument is used.

    #' For correct selection of residuals user is expected to specify rownames(data)

    #' and names(instrument) in same format. The subset is then based on string

    #' matching.

    #'

    #' @param horizon Integer scalar. The horizon for which impulse responses

    #' (and FEVDs) should be computed. Note that the first period corresponds to

    @@ -26,7 +36,7 @@
  
    #' @param identification Logical scalar. Whether or not the shocks used for

    #' calculating impulses should be identified. Defaults to \code{TRUE}, i.e.

    #' identification via Cholesky decomposition of the VCOV-matrix unless

    #' \emph{sign_restr} is provided.

    #' \emph{sign_restr} or \emph{instrument} is provided.

    #' @param sign_restr Elements inform about expected impacts

    #' of certain shocks. Can be either \eqn{1}, \eqn{-1} or \eqn{0} depending

    #' on whether a positive, a negative or no contemporaneous effect of a

    @@ -37,20 +47,45 @@
  
    #' \eqn{M - j} zero restrictions can be imposed on the \eqn{j}'th column.

    #' @param sign_lim Integer scalar. Maximum number of tries to find suitable

    #' matrices to for fitting sign or zero and sign restrictions.

    #' @param instrument Numeric vector. If provided, the identification is performed

    #' using proxy SVAR. The implemented proxy SVAR allows for different length of

    #' instrument and residuals. In such case a common subset is used for the

    #' identification. To subset correct observations names(instrument) and

    #'  rownames(data) are used - they need to have same format. See details

    #' @param proxyvar character string. Variable for which instrument is provided.

    #' Needs to correspond to one of the column names of the data used in

    #' \emph{bvar}.

    #' @param manual_matching If set to TRUE, no indices for the data and instrument

    #' are needed. Instead, the length of the instrument needs to

    #' match the length of the residuals. This rules out the possibility of performing

    #' identification on a proper subset of residuals.

    #'

    #' @return Returns a named list of class \code{bv_irf} with options for

    #' \code{\link{bvar}}, \code{\link{irf.bvar}} or \code{\link{fevd.bvar}}.

    #'

    #' @references

    #'   Rubio-Ramirez, J. F. and Waggoner, D. F. and Zha, T. (2010) Structural

    #'   Vector Autoregressions: Theory of Identification and Algorithms for

    #'   Inference. \emph{The Review of Economic Studies}, \bold{77}, 665-696,

    #'   \doi{10.1111/j.1467-937X.2009.00578.x}.

    #'   Arias, J.E. and Rubio-Ramirez, J. F. and Waggoner, D. F. (2018)

    #'  Arias, J.E. and Rubio-Ramirez, J. F. and Waggoner, D. F. (2018)

    #'   Inference Based on Structural Vector Autoregressions Identifiied with

    #'   Sign and Zero Restrictions: Theory and Applications.

    #'   \emph{Econometrica}, \bold{86}, 2, 685-720,

    #'   \doi{10.3982/ECTA14468}.

    #'   doi:10.3982/ECTA14468. \cr

    #'  Degasperi, R. and Ricco, G. (2021)

    #'   Information and policy shocks in monetary surprises. Working paper, University of Warwick. \cr

    #'  Mertens, K., and Ravn, M. O. (2013) The dynamic effects of personal and

    #'   corporate income tax changes in the United States. \emph{American

    #'   economic review}, \bold{103(4)}, 1212-1247, doi:10.1257/aer.103.4.1212. \cr

    #'  Miranda-Agrippino, S., & Ricco, G. (2021) The transmission of monetary

    #'   policy shocks. \emph{American Economic Journal: Macroeconomics},

    #'   \bold{13(3)}, 74-107, doi:10.1257/mac.20180124 \cr

    #'  Rubio-Ramirez, J. F. and Waggoner, D. F. and Zha, T. (2010) Structural

    #'   Vector Autoregressions: Theory of Identification and Algorithms for

    #'   Inference. \emph{The Review of Economic Studies}, \bold{77}, 665-696,

    #'   doi:10.1111/j.1467-937X.2009.00578.x. \cr

    #'  Stock, J. H., and Watson, M. W. (2012) Disentangling the Channels of the

    #'   2007-2009 Recession (No. w18094) \emph{National Bureau of Economic

    #'   Research}, doi:10.3386/w18094.

    #'

    #' @seealso \code{\link{irf.bvar}}; \code{\link{plot.bvar_irf}}

    #'

    @@ -71,14 +106,32 @@
  
    #' zero_signs <- matrix(c(1, 0, NA, -1, 1, 0, -1, 1, 1), nrow = 3)

    #' bv_irf(sign_restr = zero_signs)

    #'

    #' # Set up structural impulse responses using external instrument with length

    #' corresponding to reduced form residuals

    #' bv_irf(instrument = istrument)

    #'

    #' # Prepare to estimate unidentified impulse responses

    #' bv_irf(identification = FALSE)

    #'

    #' # Prepare to estimate SVAR-IV using an indexed instrument for monetary policy

    #' # shocks. names(instrument) need to be same format as rownames(data), since

    #' # subsetting is based on string matching.

    #' data("mpi")

    #' bv_irf(instrument = instrument, manual_matching = FALSE, proxyvar = "FEDFUNDS")

    #'

    bv_irf <- function(

      horizon = 12,

      fevd = FALSE,

      identification = TRUE,

      sign_restr = NULL,

      sign_lim = 1000) {

      sign_lim = 1000,

      instrument = NULL,

      proxyvar = NULL,

      manual_matching = FALSE

      )  {

      # Input checks

      horizon <- int_check(horizon, min = 1, max = 1e6,

    @@ -115,13 +168,43 @@ bv_irf <- function(
  
          }

        }

        # Cholesky

      }

      # IV

      if(!is.null(instrument)){

        if (is.numeric(instrument)) {

        } else {

          stop("Input must be a numeric vector. Multiple instruments are not

               supported.")

        }

      }

      if(!manual_matching && !is.null(instrument) &&

         is.null(names(instrument))){

        stop("No index detected for the object instrument. If manual_matching is

      FALSE, names(instrument) need to be specified. For identification to work

      correctly a common index for the data and the instrument must be specified.

      Alternatively, switch manual_matching to TRUE.")

      }

      if (!is.null(instrument) && is.null(proxyvar)) {

        stop("If 'instrument' is specified, 'proxyvar' must also be specified.")

      }

      if(!is.null(proxyvar) && !is.character(proxyvar)){

        stop("Proxyvar needs to be an object of type character.")

      }

      # Outputs

      out <- list("horizon" = horizon, "fevd" = fevd,

        "identification" = identification,

        "sign_restr" = sign_restr, "zero" = zero,

        "sign_lim" = sign_lim

        "sign_lim" = sign_lim,

        "instrument" = instrument,

        "manual_matching" = manual_matching,

        "proxyvar" = proxyvar

      )

      class(out) <- "bv_irf"

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