| Title: | Cointegrated ICU Forecasting |
| Description: | Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf. |
| Version: | 0.1.1 |
| Date: | 2022-03-01 |
| Maintainer: | Paolo Paruolo <Paolo.PARUOLO@ec.europa.eu> |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.6.0) |
| License: | GPL-3 |
| Imports: | stats, lubridate, graphics, grDevices |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-03-01 10:09:19 UTC; paoloberta |
| Author: | Paolo Berta [aut],
Paolo Paruolo 
[aut, cre],
Stefano Verzillo [ctb],
Pietro Giorgio Lovaglio [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2022-03-01 16:30:02 UTC |
Wald test for Multivariate Least-Squares regression
Description
Wald test for multivariate Least-Squares regression
Usage
Wald.mls(mlsresults)
Arguments
mlsresults |
output of mls, mlsresults<-mls(y, x, df_flag) |
Value
wald table of Wald tests on significance of single regressors and pvalues based on chi square distribution
Author(s)
P. Paruolo
References
Berta et al. 2020
computes at which observation a vector y crosses ref for the first time
Description
Computes at which observation vector yfor crosses yref for the first time if it is not crossed, then 0 is returned
Usage
crossing(yfor, ref = 0)
Arguments
yfor |
yfor is either a vector and a matrix |
ref |
ref is the refence value |
Value
whensign, a matrix with observation number at which there is crossing
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
appends NA at beginning of diff(y)
Description
appends NA at beginning of diff(y) and creates column names accordingly when y is either a vector and a matrix
Usage
diffe(y)
Arguments
y |
either a vector and a matrix |
Value
Dy contains the differences of y, with NA appended at the start
Author(s)
P. Paruolo
References
Berta et al. 2020
estimates the VECM with the 2-stage procedure of Engle & Granger
Description
Estimates the EC with EG. Cointegration rank fixed at 1
Usage
ec.EG1(det1, det2, ymat, npl, befpn, ndet, drop1 = NA, drop2 = NA)
Arguments
det1 |
deterministic matrix of constant(s) and trend(s) |
det2 |
deterministic matrix of seasonals and point dummies |
ymat |
matrix of lags |
npl |
n, p, nlag |
befpn |
begtrim, endtrim, nforecast, npred |
ndet |
order of the model d(i,j) |
drop1 |
selection of det1 regressors in first stage to drop |
drop2 |
selection of det1 regressors in second stage to drop |
Value
out a list with estimates
Author(s)
P. Berta, P. Paruolo, S. Verzillo, P.G. Lovaglio
References
Berta et al. 2020
computes companion matrix of the VAR
Description
builds the companion matrix of the VAR
Usage
ec.companion(est, p = 2, nlag = 4)
Arguments
est |
is the output of ec.EG1.R |
p |
(positive integer) is the dimension of the VAR |
nlag |
(positive integer) is the number of lags in the VAR |
Value
mA companion matrix
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
prepares deterministics D^(1)
Description
Prepares deterministic data
Usage
ec.datadet1(n, befpn, breaks)
Arguments
n |
is the number of obs in available data |
befpn |
is a vector with (begtrim,endtrim,nforecast,npred,nhstar) |
breaks |
is a vector of integers where the trend breaks should be |
Value
matdet1 a matrix with the following columns (1_vec, t_vec) and (n+npred) rows
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
prepares deterministics D^(2)
Description
Prepares deterministic dummies for de-meaned daily seasonal and difference point dummies
Usage
ec.datadet2(det1, booseas = NA, pntdates = NA)
Arguments
det1 |
is the det term with constant and trend created by ec.datadet1.R |
booseas |
is a boolean for daily seasonal dummies |
pntdates |
is a vector of integers where the point dummies should be |
Value
det2mat a matrix with the following columns (daily_seas, point_dummies) and n+npred rows
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
prepares Dy y_1 Dy_1 ... Dy_nlag-1 for estimation
Description
Prepares data for estimation
Usage
ec.datalag(y, nlag = 4)
Arguments
y |
is the data matrix of variables in the VAR |
nlag |
is the number of lags in the VAR (min = 2) |
Value
ymat contains the folloiwing columns Dy y_1 Dy_1 ... Dy_nlag-1
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
plots forecasts of difference with confidence bars
Description
plots forecasts of difference with confidence bars
Usage
ec.gfd(obj, whichseries = 1, nsigma = 3, xvec, yvec, cal, lar = 0.025, ...)
Arguments
obj |
output of ec.main |
whichseries |
series number |
nsigma |
how many standard deviations in confidence bars |
xvec |
vector of dates to place on x axis |
yvec |
vector of exp(y) values to display on y axis |
cal |
calendar vector |
lar |
length of arrows in error bars |
... |
other plot parameters |
Value
does not return output, just creates a graph
Author(s)
P. Paruolo,
plots level forecasts with confidence bars
Description
plots level forecasts with confidence bars
Usage
ec.gfl(obj, whichseries = 1, nsigma = 3, xvec, yvec, cal, lar = 0.025, ...)
Arguments
obj |
output of ec.main |
whichseries |
series number |
nsigma |
how many standard deviations in confidence bars |
xvec |
vector of dates to place on x axis |
yvec |
vector of exp(y) values to display on y axis |
cal |
calendar vector |
lar |
length of arrows in error bars |
... |
other plot parameters |
Value
does not return output, just creates a graph
Author(s)
P. Paruolo
ec.gfld plots forecasts of levels and difference with confidence bars
Description
plots forecasts of levels and difference with confidence bars
Usage
ec.gfld(
obj,
whichseries = 1,
nsigma = 3,
jointboo = TRUE,
epsboo = TRUE,
filename = "whatever",
xvec,
yvec,
cal,
lar = 0.025,
...
)
Arguments
obj |
output of ec.main |
whichseries |
series number |
nsigma |
how many standard deviations in confidence bars |
jointboo |
boolean: TRUE if 1x2 graph, FALSE otherwise |
epsboo |
boolean: TRUE eps graph, FALSE pdf graph |
filename |
string, name of the file (no extension) |
xvec |
vector of dates to place on x axis |
yvec |
vector of exp(y) values to display on y axis |
cal |
calendar vector |
lar |
length of arrows in error bars |
... |
other plot parameters |
Value
does not return output, just creates a double graph
Author(s)
P. Paruolo
Computes Indices of Forecast Performance
Description
indices of forecast performance
Usage
ec.ifp(afdlin, rwsigma, rwabsmean, kval = 1.959964)
Arguments
afdlin |
actual + forecast values + fcse |
rwsigma |
standard deviation of Random Walk in sample |
rwabsmean |
mean absolute deviation of Random Walk in sample |
kval |
how many se to use, default kval = 1.959964 |
Value
list of indices of forecast performance 1: index for model forecast 0: index for Random Walk forecast
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
Forecast with Vector Error Correction Model
Description
This function estimate VECM model. Selects begtrim and entrim period, define lag and run.
Usage
ec.main(
y,
ndet = c(2, 1),
nlag,
befpn,
breaks = NA,
booseas = NA,
pntdates = NA,
drop1 = NA,
drop2 = NA,
cal,
kval = 1.959964
)
Arguments
y |
matrix with time across rows and variables in columns |
ndet |
vector of lenght 3, (i,j,q): i for EG1-st stage, j for EG-2nd stage, q number of breaks i,j=0 no deterministics i,j=1 constant i,j=2 constant and trend |
nlag |
number of lags in the VAR |
befpn |
begtrim, endtrim, nforecast, npred |
breaks |
vector with observation numbers for T1,T2,... |
booseas |
boolean =T if seasonal dummies, =F otherwise |
pntdates |
vector with observation numbers for point dummies |
drop1 |
selection of det1 regressors in first stage to drop |
drop2 |
selection of det1 regressors in second stage to drop |
cal |
calendar for the y matrix |
kval |
how many se to use, default kval=1.959964 |
Value
results Output contains the a set of estimates and forecasting results.
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
plots forecasts
Description
plot actual and forecast intervals
Usage
ec.plotfor(
y,
x = NA,
lcolact = NA,
lcolfor = NA,
ltypefor = NA,
polycol = NA,
myylim = NA,
...
)
Arguments
y |
actual values and forecasts (point forecast, lower bound, upper bound) |
x |
time calendar |
lcolact |
color actual (scalar) |
lcolfor |
color forecasts |
ltypefor |
type forecasts |
polycol |
color polygons if one wishes to have different lcolfor,ltypefor,polycol by week > make linecol, linetype, polycol vectors, indexed by week |
myylim |
vector with min and max for y axis |
... |
other plot parameters |
Value
does not return output, just creates a graph
Author(s)
P. Paruolo
References
Berta et al. 2020
Companion matrix of the VAR
Description
plots roots and the unit circle
Usage
ec.plotroots(roots)
Arguments
roots |
are the roots of the companion matrix, see ec.companion.R |
Value
does not return output, just creates a graph
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
produces predictions for the VECM via its VAR companion form
Description
Predicts both in-sample (1 step ahead) and out-of-sample (1 step ahead and dynamic forecasts)
Usage
ec.predict(est, det1, det2, ymat, npl, befpn, ndet, cal, kval = 1.959964)
Arguments
est |
output from estimation by ec.EG1.R |
det1 |
deterministic matrix of constant(s) and trend(s) |
det2 |
deterministic matrix of seasonals and point dummies |
ymat |
matrix of lags |
npl |
n, p, nlag |
befpn |
begtrim, endtrim, nforecast, npred, nhstar |
ndet |
order of the model d(i,j) |
cal |
calendar, should match the number of rows in ymat |
kval |
how many se to use, default kval= 1.959964 |
Value
list with contains: afl (actual and 1 step ahead fitted levels) afd (actual and 1 step ahead fitted differences) fit (1 step ahead fit) dynpred (dynamic predictions) mAt mB (companion matrix and selection of it) Sigmah (Sigmah for dyn forecasts) forstartdate (starting date for dyn forecast) outcal (dates for the prediction) h1star (h1star) cspred (table with change in sign of pred for Dx_1) indexfa (indices of forecast accuracy)
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
search for breaks dates for given q (=1,2,3,4)
Description
Search for location of break points in 1st-stage of Engle-Granger
Usage
ec.searchbreaks(qse, ymat, npl, befpn, ndet, gfillmin = 10, fixed = NA)
Arguments
qse |
q: number of (additional) breaks, s: start date for search, e: end date for search |
ymat |
matrix of lags |
npl |
n, p, nlag |
befpn |
begtrim, endtrim, nforecast, npred |
ndet |
order of the model d(i,j) |
gfillmin |
gfill value |
fixed |
vector of breaks to be taken as fixed (not between s=start and e=end) |
Value
out list with break dates and values of regression average sum of squares
Author(s)
P. Paruolo
References
Berta et al. 2020
Data from Italian Civil Protection
Description
Data from Italian Civil Protection
Usage
data(iculomb)
Format
A dataset including 324 obs and 24 columns.
Source
https://github.com/pcm-dpc/COVID-19/
References
Italian Civil Protection
Examples
data(iculomb)
lag j of matrix or vector y
Description
lagn(y,j,fill=NA) produces lag j of matrix or vector y, with fill in missing j cells REM: alternative to "lead-lag" dplyr which applies to vector y
Usage
lagn(y, j, fill = NA)
Arguments
y |
column vector or matrix |
j |
number of lags |
fill |
value to be used to fill the missing values at the beginning, default = NA |
Value
y lagged j cells, with fill in the missing j positions
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
listsize number of terms in the search for 1,2,3,4 number of breaks
Description
computes length-4 vector with number of terms in the search for 1,2,3,4 number of breaks
Usage
listsize(myT, gfill, start)
Arguments
myT |
sample size |
gfill |
number of gap periods |
start |
beginning |
Value
a vector of 4 elements, with the number of candidate models for 1,2,3,4 breaks
Author(s)
P. Paruolo
Multivariate Least-Squares regression
Description
Multivariate Least-Squares regression y = x beta + u
Usage
mls(y, x, df_flag = FALSE)
Arguments
y |
left hand side data matrix (one or more columns) |
x |
right hand side data matrix (one or more columns) |
df_flag |
flag = TRUE for degrees of freedom correction for the variance |
Value
out regression coefficients and related statistics
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020
summary function for cif
Description
Summary function for presize
Usage
## S3 method for class 'cif'
summary(object, ..., digits = 4)
Arguments
object |
is the name of the cif object created by cif |
... |
other parameters |
digits |
integer indicating the number of decimal places (round) or significant digits (signif) to be used. |
Value
returns summary output from model estimation and forecasting
Author(s)
P. Berta, P. Paruolo, S. Verzillo, PG. Lovaglio
References
Berta et al. 2020