stable v0.1.0

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+^data-raw$
+^README\.Rmd$
+^cran-comments\.md$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

ARDL.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 4
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageCheckArgs: --as-cran
+PackageRoxygenize: rd,collate,namespace

DESCRIPTION

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+Package: ARDL
+Type: Package
+Title: Autoregressive Distributed Lag Models, Error Correction Models and
+    Bounds-Test for Cointegration
+Description: Creates complex Autoregressive Distributed Lag models providing
+    just the order and automatically constructs the underlying unrestricted and
+    restricted ECM. It also performs the bounds-test for cointegration and
+    provides the multipliers and the cointegrating equation.
+Version: 0.1.0
+Authors@R: c(
+  person("Kleanthis", "Natsiopoulos", 
+  email = "klnatsio@gmail.com", role = c("aut", "cre", "dis"), 
+  comment = c(ORCID = "0000-0003-1180-2984")), 
+  person("Nickolaos", "Tzeremes", 
+  email = "bus9nt@econ.uth.gr", role = "ths",
+  comment = c(ORCID = "0000-0002-6938-3404")), 
+  person("University of Thessaly, Department of Economics", role = "dgg"))
+License: GPL-3 | file LICENSE
+URL: https://github.com/Natsiopoulos
+Encoding: UTF-8
+LazyData: true
+Depends: 
+    R (>= 3.2.0)
+Suggests: 
+    qpcR,
+    xts
+Imports: 
+    aod,
+    dplyr,
+    dynlm,
+    lmtest,
+    msm,
+    stringr,
+    zoo
+RoxygenNote: 7.1.0

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+S3method(coint_eq,default)
+S3method(coint_eq,recm)
+S3method(multipliers,ardl)
+S3method(multipliers,uecm)
+S3method(uecm,ardl)
+S3method(uecm,default)
+export(ardl)
+export(auto_ardl)
+export(bounds_f_test)
+export(bounds_t_test)
+export(coint_eq)
+export(multipliers)
+export(recm)
+export(uecm)
+importFrom(dplyr,"%>%")
+importFrom(zoo,merge.zoo)

NEWS.md

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+# ARDL v0.1.0  (29 Mar 2020)
+
+## Released to CRAN

R/ardl-package.R

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+#' ARDL: ARDL, ECM and Bounds-Test for Cointegration
+"_PACKAGE"
+
+#'
+#' @keywords internal
+#' @docType package
+#' @name ARDL-package	
+#'
+#' @importFrom dplyr %>%
+#' @importFrom zoo merge.zoo
+#'
+# no visible binding for global variable '.' (solution line)
+globalVariables(c("."))
+
+.onAttach <- function(libname, pkgname) {
+	local_version = paste("R package version", utils::packageVersion('ARDL'))
+	year <- sub("-.*", "", utils::packageDate('ARDL'))
+	packageStartupMessage('To cite ARDL in publications use:
+  \nKleanthis Natsiopoulos and Nickolaos Tzeremes (', year, '). ARDL: ARDL, ECM and Bounds-Test for
+  Cointegration. ', local_version,'. University of Thessaly, Department of Economics. https://github.com/Natsiopoulos.')
+}

R/ardl.R

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+#' ARDL model regression
+#'
+#' A simple way to construct complex ARDL specifications prodiving just the
+#' model order additional to the model formula. It uses
+#' \code{\link[dynlm]{dynlm}} under the hood.
+#'
+#' The \code{formula} should contain only variables that exist in the data
+#' privided through \code{data} plus some additional functions supported by
+#' \code{\link[dynlm]{dynlm}} (i.e., \code{trend()}).
+#'
+#' You can also specify fixed variables that are not supposed to be lagged (e.g.
+#' dummies etc.) simply by placing them after \code{|}. For example, \code{y ~
+#' x1 + x2 | z1 + z2} where \code{z1} and \code{z2} are the fixed variables and
+#' should not be considered in \code{order}. Note that the \code{|} notion
+#' should not be confused with the same notion in \code{dynlm} where it
+#' introduces instrumental variables.
+#'
+#' @param formula A "formula" describing the linear model. Details for model
+#'   specification are given under 'Details'.
+#' @param data A time series object (e.g., "ts", "zoo" or "zooreg") or a data
+#'   frame containing the variables in the model. In the case of a data frame,
+#'   it is coerced into a \code{\link[stats]{ts}} object with \code{start = 1},
+#'   \code{end = nrow(data)} and \code{frequency = 1}. If not found in data, the
+#'   variables are NOT taken from any environment.
+#' @param order A specification of the order of the ARDL model. A numeric vector
+#'   of the same length as the total number of variables (excluding the fixed
+#'   ones, see 'Details'). It should only contain positive integers or 0. An
+#'   integer could be provided if all variables are of the same order.
+#' @param start Start of the time period which should be used for fitting the
+#'   model.
+#' @param end End of the time period which should be used for fitting the model.
+#' @param ... Additional arguments to be passed to the low level regression
+#'   fitting functions.
+#'
+#' @return \code{ardl} returns an object of \code{\link[base]{class}}
+#'   \code{c("dynlm", "lm", "ardl")}. In addition, attributes 'order', 'data',
+#'   'parsed_formula' and 'full_formula' are provided.
+#'
+#' @seealso \code{\link{uecm}}, \code{\link{recm}}
+#' @author Kleanthis Natsiopoulos, \email{klnatsio@@gmail.com}
+#' @keywords models ts
+#' @export
+#' @examples
+#' data(denmark)
+#' 
+#' ## Estimate an ARDL(3,1,3,2) model -------------------------------------
+#'
+#' ardl_3132 <- ardl(LRM ~ LRY + IBO + IDE, data = denmark, order = c(3,1,3,2))
+#' summary(ardl_3132)
+#'
+#' ## Add dummies or other variables that should stay fixed ---------------
+#'
+#' d_74Q1_75Q3 <- ifelse(time(denmark) >= 1974 & time(denmark) <= 1975.5, 1, 0)
+#'
+#' # the date can also be setted as below
+#' d_74Q1_75Q3_ <- ifelse(time(denmark) >= "1974 Q1" & time(denmark) <= "1975 Q3", 1, 0)
+#' identical(d_74Q1_75Q3, d_74Q1_75Q3_)
+#' den <- cbind(denmark, d_74Q1_75Q3)
+#' ardl_3132_d <- ardl(LRM ~ LRY + IBO + IDE | d_74Q1_75Q3,
+#'                     data = den, order = c(3,1,3,2))
+#' summary(ardl_3132_d)
+#' compare <- data.frame(AIC = c(AIC(ardl_3132), AIC(ardl_3132_d)),
+#'                       BIC = c(BIC(ardl_3132), BIC(ardl_3132_d)))
+#' rownames(compare) <- c("no ummy", "with dummy")
+#' compare
+#' 
+#' ## Estimate an ARDL(3,1,3,2) model with a linear trend -----------------
+#'
+#' ardl_3132_tr <- ardl(LRM ~ LRY + IBO + IDE + trend(LRM),
+#'                      data = denmark, order = c(3,1,3,2))
+#'
+#' # Alternative time trend specifications:
+#' # time(LRM)                 1974 + (0, 1, ..., 55)/4 time(data)
+#' # trend(LRM)                (1, 2, ..., 55)/4        (1:n)/freq
+#' # trend(LRM, scale = FALSE) (1, 2, ..., 55)          1:n
+#'
+#' ## Subsample ARDL regression (start after 1975 Q4) ---------------------
+#'
+#' ardl_3132_sub <- ardl(LRM ~ LRY + IBO + IDE, data = denmark,
+#'                       order = c(3,1,3,2), start = "1975 Q4")
+#'
+#' # the date can also be setted as below
+#' ardl_3132_sub2 <- ardl(LRM ~ LRY + IBO + IDE, data = denmark,
+#'                        order = c(3,1,3,2), start = c(1975,4))
+#' identical(ardl_3132_sub, ardl_3132_sub2)
+#' summary(ardl_3132_sub)
+#' 
+#' ## Ease of use ---------------------------------------------------------
+#'
+#' # The model specification of the ardl_3132 model can be created as easy as order=c(3,1,3,2)
+#' # or else, it could be done using the dynlm package as:
+#' library(dynlm)
+#' m <- dynlm(LRM ~ L(LRM, 1) + L(LRM, 2) + L(LRM, 3) + LRY + L(LRY, 1) + IBO + L(IBO, 1) +
+#'            L(IBO, 2) + L(IBO, 3) + IDE + L(IDE, 1) + L(IDE, 2), data = denmark)
+#' identical(m$coefficients, ardl_3132$coefficients)
+#'
+#' # The full formula can be extracted from the ARDL model, and this is equal to
+#' ardl_3132$full_formula
+#' m2 <- dynlm(ardl_3132$full_formula, data = ardl_3132$data)
+#' identical(m$coefficients, m2$coefficients)
+
+ardl <- function(formula, data, order, start = NULL, end = NULL, ...) {
+    if (!any(c("ts", "zoo", "zooreg") %in% class(data))) {
+        data <- stats::ts(data, start = 1, end = nrow(data), frequency = 1)
+    }
+
+    parsed_formula <- parse_formula(formula = formula, colnames_data = colnames(data))
+    order <- parse_order(orders = order, order_name = "order", kz = parsed_formula$kz)
+    ardl_formula <- build_ardl_formula(parsed_formula = parsed_formula, order = order)
+
+    full_formula <- formula(ardl_formula$full)
+    ardl_model <- dynlm::dynlm(full_formula, data = data, start = start, end = end, ...)
+    # for model compatibility in the global env
+    attr(ardl_model$terms, ".Environment") <- .GlobalEnv
+    attr(attr(ardl_model$model, "terms"), ".Environment") <- .GlobalEnv
+    attr(full_formula, ".Environment") <- .GlobalEnv
+
+    ardl_model$order <- order
+    ardl_model$data <- data
+    ardl_model$parsed_formula <- parsed_formula
+    ardl_model$full_formula <- full_formula
+
+    attr(ardl_model, "class") <- c(class(ardl_model), "ardl")
+
+    return(ardl_model)
+}