| Type: | Package |
| Title: | Time Series, Analysis and Application |
| Version: | 1.0.4 |
| Author: | Rainer Schlittgen |
| Maintainer: | Rainer Schlittgen <R.Schlittgen@t-online.de> |
| Description: | Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) https://www.degruyter.com/view/title/575978 "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.6.0), Matrix , vars, fftwtools, hdm |
| SystemRequirements: | under Linux, fftwtools needs libfftw3-dev |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-10-30 11:09:35 UTC; rainer |
| Repository: | CRAN |
| Date/Publication: | 2021-10-30 11:30:02 UTC |
Monthly numbers of road traffic accidents with personal injury in BRD
Description
Monthly numbers of road traffic accidents with personal injury in BRD
Usage
ACCIDENT
Format
ACCIDENT is a univariate time series of length 528, start January 1974, frequency = 12
- ACCIDENT
Monthly numbers of road traffic accidents with personal injury
Source
< https://www-genesis.destatis.de/genesis//online?operation=table&code=46241-0002&
levelindex=0&levelid=1583749114977>
Examples
data(ACCIDENT)
## maybe tsp(ACCIDENT) ; plot(ACCIDENT)
Alcohol Demand, UK, 1870-1938.
Description
Alcohol Demand, UK, 1870-1938.
Usage
ALCINCOME
Format
ALCINCOME is a threevariate time series of length 69 and 3 variables; start 1870, frequency = 1
- Y
log consumption per head
- Z
log real income per head
- X
log real price
Source
Durbin & Watson (1951) <https://doi.org/10.1093/biomet/38.1-2.159>
Examples
data(ALCINCOME)
## maybe tsp(ALCINCOME) ; plot(ALCINCOME)
Monthly beer production in Australia: megalitres. Includes ale and stout. Does not include beverages with alcohol percentage less than 1.15.
Description
Monthly beer production in Australia: megalitres. Includes ale and stout. Does not include beverages with alcohol percentage less than 1.15.
Usage
BEER
Format
BEER is a univariate time series of length 476, start January 1956, end Aug 1995, frequency = 12
- BEER
Monthly production of beer in Australia
Source
R package tsdl <https://github.com/FinYang/tsdl>
Examples
data(BEER)
## maybe tsp(BEER) ; plot(BEER)
Weekly number of births in New York
Description
Weekly number of births in New York
Usage
BLACKOUT
Format
BLACKOUT is a univariate time series of length 313, 1961 – 1966
- BLACKOUT
Weekly numbers of births in New York
Source
Izenman, A. J., and Zabell, S. L. (1981) <https://www.sciencedirect.com/science/article/abs/pii/ 0049089X81900181>
Examples
data(BLACKOUT)
## maybe tsp(BLACKOUT) ; plot(BLACKOUT)
BoxCox determines the power of a Box-Cox transformation to stabilize the variance of a time series
Description
BoxCox determines the power of a Box-Cox transformation to stabilize the variance of a time series
Usage
BoxCox(y, seg, Plot = FALSE)
Arguments
y |
the series, a vector or a time series |
seg |
scalar, number of segments |
Plot |
logical, should a plot be produced? |
Value
l scalar, the power of the Box-Cox transformation
Examples
data(INORDER)
lambda <-BoxCox(INORDER,6,Plot=FALSE)
U.S. annual coffee consumption
Description
U.S. annual coffee consumption
Usage
COFFEE
Format
COFFEE is a univariate time series of length 61; start 1910, frequency = 1
- COFFEE
annual coffee-consumption USA, logarithmic transformed
Source
R package tsdl <https://github.com/FinYang/tsdl>
Examples
data(COFFEE)
## maybe tsp(COFFEE) ; plot(COFFEE)
Market value of DAX
Description
Market value of DAX
Usage
DAX
Format
DAX is a multivariate time series of length 12180 and 4 variables
- DAY
Day of the week
- MONTH
Month
- Year
Year
- DAX30
Market value
Examples
data(DAX)
## maybe tsp(DAX) ; plot(DAX)
Incidences of insulin-dependent diabetes mellitus
Description
Incidences of insulin-dependent diabetes mellitus
Usage
DIABETES
Format
DIABETES is a univariate time series of length 72, start January 1979, frequency = 12
- DIABETES
Incidences of insulin-dependent diabetes mellitus
Source
Waldhoer, T., Schober, E. and Tuomilehto, J. (1997) <https://www.sciencedirect.com/science/
article/abs/pii/S0895435696003344>
Examples
data(DIABETES)
## maybe tsp(DIABETES) ; plot(DIABETES)
Running yield of public bonds in Austria and Germany
Description
Running yield of public bonds in Austria and Germany
Usage
DOMINANCE
Format
DOMINANCE is a bivariate time series of length 167:
- X
Interest rate Germany
- Y
Interest rate Austria
Source
Jaenicke, J. and Neck, R. (1996) <https://doi.org/10.17713/ajs.v25i2.555>
Examples
data(DOMINANCE)
## maybe tsp(DOMINANCE) ; plot(DOMINANCE)
ENGINES is an alias for MACHINES
Description
ENGINES is an alias for MACHINES
Usage
ENGINES
Format
ENGINES is a univariate time series of length 188, start January 1972 frequency = 12
- ENGINES
Incoming orders for engines
Examples
data(ENGINES)
## maybe tsp(ENGINES) ; plot(ENGINES)
Portfolio-Insurance-Strategies
Description
Portfolio-Insurance-Strategies
Usage
FINANCE
Format
FINANCE is a multivariate time series of length 7529:
- CPPI
first Portfolio-Insurance-Strategy
- TIPP
second Portfolio-Insurance-Strategy
- StopLoss
third Portfolio-Insurance-Strategy
- SyntheticPut
fourth Portfolio-Insurance-Strategy
- CASH
money market investment
Source
Dichtl, H. and Drobetz, W. (2011) <doi:10.1016/j.jbankfin.2010.11.012>
Examples
data(FINANCE)
## maybe tsp(FINANCE) ; plot(FINANCE)
Germany's gross domestic product adjusted for price changes
Description
Germany's gross domestic product adjusted for price changes
Usage
GDP
Format
GDP is a univariate time series of length 159, start January 1970, frequency = 4
- GDP
Gross domestic product adjusted for price changes
Source
<https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex
=0&levelid=1583750132341>
Examples
data(GDP)
## maybe tsp(GDP) ; plot(GDP)
Germany's gross domestic product, values of Laspeyres index to base 2000
Description
Germany's gross domestic product, values of Laspeyres index to base 2000
Usage
GDPORIG
Format
GDPORIG is a univariate time series of length 159, start January 1970, frequency = 4
- GDPORIG
gross domestic product, values of Laspeyres index to the base 2000
Source
<https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex
=0&levelid=1583750132341>
Examples
data(GDPORIG)
## maybe tsp(GDPORIG) ; plot(GDPORIG)
Grangercaus determines three values of BIC from a twodimensional VAR process
Description
Grangercaus determines three values of BIC from a twodimensional VAR process
Usage
Grangercaus(x, y, p)
Arguments
x |
first time series |
y |
second time series |
p |
maximal order of VAR process |
Value
out list with components
BIC |
vector of length 3: |
| BIC1 | minimum aic value for all possible lag structures |
| BIC2 | minimum aic value when Y is not included as regressor in the equation for X |
| BIC3 | minimum aic value when X is not included as regressor in the equation for Y |
out1 |
output of function lm for regression equation for x-series |
out2 |
output of function lm for regression equation for y-series |
Examples
data(ICECREAM)
out <- Grangercaus(ICECREAM[,1],ICECREAM[,2],3)
HAC Covariance Matrix Estimation
HAC computes the central quantity (the meat) in the HAC covariance matrix estimator, also called
sandwich estimator. HAC is the abbreviation for "heteroskedasticity and autocorrelation consistent".
Description
HAC Covariance Matrix Estimation
HAC computes the central quantity (the meat) in the HAC covariance matrix estimator, also called
sandwich estimator. HAC is the abbreviation for "heteroskedasticity and autocorrelation consistent".
Usage
HAC(mcond, method = "Bartlett", bw)
Arguments
mcond |
a q-dimensional multivariate time series. In the case of OLS regression with q regressors mcond contains the series of the form regressor*residual (see example below). |
method |
kernel function, choose between "Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral". |
bw |
bandwidth parameter, controls the number of lags considered in the estimation. |
Value
mat a (q,q)-matrix
Source
Heberle, J. and Sattarhoff, C. (2017) <doi:10.3390/econometrics5010009> "A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators"
Examples
data(MUSKRAT)
y <- ts(log10(MUSKRAT))
n <- length(y)
t <- c(1:n)
t2 <- t^2
out2 <- lm(y ~ t +t2)
mat_xu <- matrix(c(out2$residuals,t*out2$residuals, t2*out2$residuals),nrow=62,ncol=3)
hac <- HAC(mat_xu, method="Bartlett", 4)
mat_regr<- matrix(c(rep(1,62),t,t2),nrow=62,ncol=3)
mat_q <- t(mat_regr)%*%mat_regr/62
vcov_HAC <- solve(mat_q)%*%hac%*%solve(mat_q)/62
# vcov_HAC is the HAC covariance matrix estimation for the OLS coefficients.
Cardiac frequency of a patient
Description
Cardiac frequency of a patient
Usage
HEARTBEAT
Format
HEARTBEAT is a univariate time series of length 30:
- HEARTBEAT
cardiac frequency of a patient
Examples
data(HEARTBEAT)
## maybe tsp(HEARTBEAT) ; plot(HEARTBEAT)
HSV's position in the first German soccer league
Description
HSV's position in the first German soccer league
Usage
HSV
Format
HSV is a univariate time series of length 47:
- HSV
HSV's position in the first German soccer league
Source
<https://www.transfermarkt.de/hamburger-sv/platzierungen/verein/41>
Examples
data(HSV)
## maybe tsp(HSV) ; plot(HSV)
IBM's stock price
Description
IBM's stock price
Usage
IBM
Format
IBM is a univariate time series of length 369, start 17 May 1961
- IBM
IBM's daily stock price
Source
Box, G. E. P. and Jenkins, G. M. (1970, ISBN: 978-0816210947) "Time series analysis: forecasting and control"
Examples
data(IBM)
## maybe tsp(IBM) ; plot(IBM)
Temperature and consumption of ice cream
Description
Temperature and consumption of ice cream
Usage
ICECREAM
Format
ICECREAM is a bivariate time series of length 160:
- ICE
consumption of ice cream
- TEMP
Temperature in Fahrenheit degrees
Source
Hand, D. J., et al. (1994, ISBN: 9780412399206) "A Handbook of Small Data Sets"
Examples
data(ICECREAM)
## maybe tsp(ICECREAM) ; plot(ICECREAM)
Income orders of a company
Description
Income orders of a company
Usage
INORDER
Format
INORDER is a univariate time series of length 237, start January 1968, frequency =12
- INORDER
Income orders of a company
Examples
data(INORDER)
## maybe tsp(INORDER) ; plot(INORDER)
Subsoil water level and precipitation at pilot well L921
Description
Subsoil water level and precipitation at pilot well L921
Usage
L921
Format
L921 is a trivariate time series of length 335:
- T
Day
- Y
Water level
- Z
Supplemented water level
Examples
data(L921)
## maybe tsp(L921) ; plot(L921)
Daily subsoil water level and precipitation at pilot well Lith
Description
Daily subsoil water level and precipitation at pilot well Lith
Usage
LITH
Format
LITH is a bivariate time series of length 1347:
- N
precipitation amount
- G
water level
Examples
data(LITH)
## maybe tsp(LITH) ; plot(LITH)
Level of Luteinzing hormone of a cow
Description
Level of Luteinzing hormone of a cow
Usage
LUHORMONE
Format
LUHORMONE is a bivariate time series of length 29:
- T
Time in minutes
- X
Level of the Luteinzing-hormone
Annual lynx trappings in a region of North-West Canada. Taken from Andrews and Herzberg (1985).
Description
Annual lynx trappings in a region of North-West Canada. Taken from Andrews and Herzberg (1985).
Usage
LYNX
Format
LYNX is a univariate time series of length 114; start 1821 frequency = 1
- LYNX
annual lynx trappings in a region of North-west Canada
Source
Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
Examples
data(LYNX)
## maybe tsp(LYNX) ; plot(LYNX)
Size of populations of lynxes and snow hares
Description
Size of populations of lynxes and snow hares
Usage
LYNXHARE
Format
LYNXHARE is a simulated bivariate time series from a VAR[1]-model of length 100:
- X
Number of lynxes
- Y
Number of snow hares
Examples
data(LYNXHARE)
LjungBoxPierceTest determines the test statistic and p values for several lags for a residual series
Description
LjungBoxPierceTest determines the test statistic and p values for several lags for a residual series
Usage
LjungBoxPierceTest(y, n.par = 0, maxlag = 48)
Arguments
y |
the series of residuals, a vector or a time series |
n.par |
number of parameters which had been estimated |
maxlag |
maximal lag up to which the test statistic is computed, default is maxlag = 48 |
Value
BT matrix with columns: lags, degrees of freedom, test statistic, p-value
Examples
data(COFFEE)
out <- arima(COFFEE,order=c(1,0,0))
BT <- LjungBoxPierceTest(out$residuals,1,20)
Number of incoming orders for machines
Description
Number of incoming orders for machines
Usage
MACHINES
Format
MACHINES is a univariate time series of length 188, start January 1972 frequency = 12
- MACHINES
Incoming orders for machines
Examples
data(MACHINES)
## maybe tsp(MACHINES) ; plot(MACHINES)
Atmospheric CO2 concentrations (ppmv) derived from in situ air samples collected at Mauna Loa Observatory, Hawaii
Description
Atmospheric CO2 concentrations (ppmv) derived from in situ air samples collected at Mauna Loa Observatory, Hawaii
Usage
MAUNALOA
Format
MAUNALOA is a univariate time series of length 735; start March 1958, frequency = 12
- MAUNALOA
CO2-concentration at Mauna Loa
Source
Keeling, C. D. , Piper, S. C., Bacastow, R. B., Wahlen, M. , Whorf, T. P., Heimann, M., and Meijer, H. A. (2001) <https://library.ucsd.edu/dc/object/bb3859642r>
Examples
data(MAUNALOA)
## maybe tsp(MAUNALOA) ; plot(MAUNALOA)
Stock market price of MDAX
Description
Stock market price of MDAX
Usage
MDAX
Format
MDAX is a multivariate time series of length 6181 and 4 variables
- DAY
Day of the week
- MONTH
Month
- YEAR
Year
- MDAX
Opening stock market price
Source
<https://www.onvista.de/index/MDAX-Index-323547>
Examples
data(MDAX)
## maybe tsp(MDAX) ; plot(MDAX[,3])
Melanoma incidence in Connecticut
Description
Melanoma incidence in Connecticut
Usage
MELANOM
Format
MELANOM is a multivariate time series of length 45 and 3 variables
- POP
Population
- RATE
Incidence
- SUN
Sunspots
Source
Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
Examples
data(MELANOM)
## maybe tsp(MELANOM) ; plot(MELANOM[,-1])
Annual trade of muskrat pelts
Description
Annual trade of muskrat pelts
Usage
MUSKRAT
Format
MUSKRAT is a univariate time series of length 62; start 1848, frequency = 1
- MUSKRAT
annual trade of muskrat pelts
Source
<https://archive.uea.ac.uk/~gj/book/data/mink.dat>
Examples
data(MUSKRAT)
## maybe tsp(MUSKRAT) ; plot(MUSKRAT)
Daily values of the Japanese stock market index Nikkei 225 between 02.02.2000 and 20.10.2020
Description
Daily values of the Japanese stock market index Nikkei 225 between 02.02.2000 and 20.10.2020
Usage
NIKKEI
Format
NIKKEI is a univariate time series of length 5057
- NIKKEI
Daily values of Nikkei
Source
Heber, G., Lunde, A., Shephard, N. and Sheppard, K. (2009) "Oxford-Man Institute's realized library, version 0.3", Oxford-Man Institute, University of Oxford, Oxford <https://realized.oxford-man.ox.ac.uk/data>
Examples
data(NIKKEI)
## maybe plot(NIKKEI)
Amount of an Oxygen isotope
Description
Amount of an Oxygen isotope
Usage
OXYGEN
Format
OXYGEN is a matrix with 164 rows and 2 columns
- T
Time
- D
DELTA18O
Source
Belecher, J., Hampton, J. S., and Tunnicliffe Wilson, T. (1994, ISSN: 1369-7412) "Parameterization of Continuous Time Autoregressive Models for Irregularly Sampled Time Series Data"
Examples
data(OXYGEN)
## maybe plot(OXYGEN[,1],OXYGEN[,2],type="l"); rug(OXYGEN[,1])
Two measurements at a paper machine
Description
Two measurements at a paper machine
Usage
PAPER
Format
PAPER is a bivariate time series of length 160
- H
High
- W
Weight
Source
Janacek, G. J. & Swift, L. (1993, ISBN: 978-0139184598) "Time Series: Forecasting, Simulation, Applications"
Examples
data(PAPER)
## maybe tsp(PAPER) ; plot(PAPER)
Monthly prices for pigs
Description
Monthly prices for pigs
Usage
PIGPRICE
Format
PIGPRICE is a univariate time series of length 240; start January 1894, frequency =12
- PIGPRICE
Monthly prices for pigs
Source
Hanau, A. (1928) "Die Prognose der Schweinepreise"
Examples
data(PIGPRICE)
## maybe tsp(PIGPRICE) ; plot(PIGPRICE)
Peak power demand in Berlin
Description
Peak power demand in Berlin
Usage
PPDEMAND
Format
PPDEMAND is a univariate time series of length 37; start 1955, frequency = 1
- PPDEMAND
annual peak power demand in Berlin, Megawatt
Source
Fiedler, H. (1979) "Verschiedene Verfahren zur Prognose des des Stromspitzenbedarfs in Berlin (West)"
Examples
data(PPDEMAND)
## maybe tsp(PPDEMAND) ; plot(PPDEMAND)
Production index of manufacturing industries
Description
Production index of manufacturing industries
Usage
PRODINDEX
Format
PRODINDEX is a univariate time series of length 119:
- PRODINDEX
Production index of manufacturing industries
Source
Statistisches Bundesamt (2009) <https://www-genesis.destatis.de/genesis/online>
Examples
data(PRODINDEX)
## maybe tsp(PRODINDEX) ; plot(PRODINDEX)
Annual amount of rainfall in Los Angeles
Description
Annual amount of rainfall in Los Angeles
Usage
RAINFALL
Format
RAINFALL is a univariate time series of length 119; start 1878, frequency = 1
- RAINFALL
Amount of rainfall in Los Angeles
Source
LA Times (January 28. 1997)
Examples
data(RAINFALL)
## maybe tsp(RAINFALL) ; plot(RAINFALL)
Monthly sales of Australian red wine (1000 l)
Description
Monthly sales of Australian red wine (1000 l)
Usage
REDWINE
Format
REDWINE is a univariate time series of length 187; start January 1980, frequency =12
- REDWINE
Monthly sales of Australian red wine
Source
R package tsdl <https://github.com/FinYang/tsdl>
Examples
data(REDWINE)
## maybe tsp(REDWINE) ; plot(REDWINE)
RS rescaled adjusted range statistic
Description
RS rescaled adjusted range statistic
Usage
RS(x, k)
Arguments
x |
univariate time series |
k |
length of the segments for which the statistic is computed. Starting with t=1, the segments do not overlap. |
Value
(l,3)-matrix, 1. column: k, second column: starting time of segment, third column: value of RS statistic.
Examples
data(TREMOR)
R <- RS(TREMOR,10)
Monthly sales of a company
Description
Monthly sales of a company
Usage
SALES
Format
SALES is a univariate time series of length 77:
- y
monthly sales of a company
Source
Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
Examples
data(SALES)
## maybe tsp(SALES) ; plot(SALES)
CO2-Concentration obtained in Schauinsland, Germany
Description
CO2-Concentration obtained in Schauinsland, Germany
Usage
SCHAUINSLAND
Format
SCHAUINSLAND is a univariate time series of length 72:
- SCHAUINSLAND
CO2-Concentration obtained in Schauinsland
Source
<http://cdiac.ornl.gov/trends/co2/uba/uba-sc.html>
Examples
data(SCHAUINSLAND)
## maybe tsp(SCHAUINSLAND) ; plot(SCHAUINSLAND)
Annual logging of spruce wood.
Description
Annual logging of spruce wood.
Usage
SPRUCE
Format
SPRUCE is a univariate time series of length 42:
- SPRUCE
Annual logging of spruce wood
Examples
data(SPRUCE)
## maybe tsp(SPRUCE) ; plot(SPRUCE)
Monthly community taxes in Germany (billions EURO)
Description
Monthly community taxes in Germany (billions EURO)
Usage
TAXES
Format
TAXES is a univariate time series of length 246; start January 1999, frequency = 12
- TAXES
monthly community taxes in Germany
Source
<https://www-genesis.destatis.de/genesis/online?operation=previous&levelindex=1&step=1&titel=
Tabellenaufbau&levelid=1583748637039>
Examples
data(TAXES)
## maybe tsp(TAXES) ; plot(TAXES)
Mean thickness of annual tree rings
Description
Mean thickness of annual tree rings
Usage
TREERING
Format
TREERING is a multivariate time series of length 66 with 3 variables:
- THICK
mean thickness of annual tree rings
- TEMP
mean temperature of the year
- RAIN
amount of rain of the year
Source
<https://ltrr.arizona.edu/>
Examples
data(TREERING)
## maybe tsp(TREERING) ; plot(TREERING)
Measurements of physiological tremor
Description
Measurements of physiological tremor
Usage
TREMOR
Format
TREMOR is a univariate time series of length 400.
- TREMOR
Tremor
Examples
data(TREMOR)
## maybe tsp(TREMOR) ; plot(TREMOR)
Population of USA
Description
Population of USA
Usage
USAPOP
Format
USAPOP is a univariate time series of length 39; start 1630, frequency = 0.1
- USAPOP
Population of USA
Source
<https://www.worldometers.info/world-population/us-population/>
Examples
data(USAPOP)
## maybe tsp(USAPOP) ; plot(USAPOP)
Concentration of growth hormone of a bull
Description
Concentration of growth hormone of a bull
Usage
WHORMONE
Format
WHORMONE is a univariate time series of length 97:
- WHORMONE
Concentration of growth hormone of a bull
Source
Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
Examples
data(WHORMONE)
## maybe tsp(WHORMONE) ; plot(WHORMONE)
acfmat computes a sequence of autocorrelation matrices for a multivariate time series
Description
acfmat computes a sequence of autocorrelation matrices for a multivariate time series
Usage
acfmat(y, lag.max)
Arguments
y |
multivariate time series |
lag.max |
maximum number of lag |
Value
out list with components:
M |
array with autocovariance matrices |
M1 |
array with indicators if autocovariances are significantly greater (+), lower (-) than the critical value or insignificant (.) at 95 percent level |
Examples
data(ICECREAM)
out <- acfmat(ICECREAM,7)
acfpacf produces a plot of the acf and the pacf of a time series
Description
acfpacf produces a plot of the acf and the pacf of a time series
Usage
acfpacf(x, lag, HV = "H")
Arguments
x |
the series, a vector or a time series |
lag |
scalar, maximal lag to be plotted |
HV |
character, controls division of graphic window: "H" horizontal, "V" vertical, default is "H" |
Examples
data(LYNX)
acfpacf(log(LYNX),15,HV="H")
armathspec determines the theoretical spectrum of an arma process
Description
armathspec determines the theoretical spectrum of an arma process
Usage
armathspec(a, b, nf, s = 1, pl = FALSE)
Arguments
a |
ar-coefficients |
b |
ma-coefficients |
nf |
scalar, the number of equally spaced frequencies |
s |
variance of error process |
pl |
logical, if TRUE, the spectrum is plotted, FALSE for no plot |
Value
out (nf+1,2) matrix, the frequencies and the spectrum
Examples
out <-armathspec(c(0.3,-0.5),c(-0.8,0.7),50,s=1,pl=FALSE)
aspectratio determines the aspect ratio to plot a time series
Description
aspectratio determines the aspect ratio to plot a time series
Usage
aspectratio(y)
Arguments
y |
time series |
Value
a scalar, the aspect ratio
Examples
data(GDP)
a <- aspectratio(GDP)
bandfilt does a bandpass filtering of a time series
Description
bandfilt does a bandpass filtering of a time series
Usage
bandfilt(y, q, pl, pu)
Arguments
y |
the series, a vector or a time series |
q |
scalar, half of length of symmetric weights |
pl |
scalar, lower periodicity ( >= 2 ) |
pu |
scalar, upper periodicity ( > pl ) |
Value
yf (n,1) vector, the centered filtered time series with NA's at beginning and ending
Examples
data(GDP)
yf <- bandfilt(GDP,5,2,6)
plot(GDP); lines(yf+mean(GDP),col="red")
bispeces performs indirect bivariate spectral estimation of two series y1, y2 using lagwindows
Description
bispeces performs indirect bivariate spectral estimation of two series y1, y2 using lagwindows
Usage
bispeces(y1, y2, q, win = "bartlett")
Arguments
y1 |
vector, the first time series |
y2 |
vector, the second time series |
q |
number of covariances used for indirect spectral estimation |
win |
lagwindow (possible: "bartlett", "parzen", "tukey") |
Value
out data frame with columns:
f |
frequencies 0, 1/n, 2/n, ... (<= 1/2 ) |
coh |
estimated coherency at Fourier frequencies 0,1/n, ... |
ph |
estimated phase at Fourier frequencies 0,1/n, ... |
Examples
data(ICECREAM)
y <- ICECREAM
out <- bispeces(y[,1],y[,2],8,win="bartlett")
dynspecest performs a dynamic spectrum estimation
Description
dynspecest performs a dynamic spectrum estimation
Usage
dynspecest(y, nseg, nf, e, theta = 0, phi = 15, d, Plot = FALSE)
Arguments
y |
time series or vector |
nseg |
number of segments for which the spectrum is estimated |
nf |
number of equally spaced frequencies |
e |
equal bandwidth |
theta |
azimuthal viewing direction, see R function persp |
phi |
colatitude viewing direction, see R function persp |
d |
a value to vary the strength of the perspective transformation, see R function persp |
Plot |
logical, schould a plot be generated? |
Value
out list with components
f |
frequencies, vector of length nf |
t |
time, vector of length nseg |
spec |
the spectral estimates, (nf,nt)-matrix |
Examples
data(IBM)
y <- diff(log(IBM))
out <- dynspecest(y,60,50,0.2,theta=0,phi=15,d=1,Plot=FALSE)
init_values is an auxiliary function for rlassoHAC, for fitting linear models with
the method of least squares where only the variables in X with highest correlations
are considered; taken from package hdm.
Description
init_values is an auxiliary function for rlassoHAC, for fitting linear models with
the method of least squares where only the variables in X with highest correlations
are considered; taken from package hdm.
Usage
init_values(X, y, number = 5, intercept = TRUE)
Arguments
X |
Regressors (matrix or object can be coerced to matrix). |
y |
Dependent variable(s). |
number |
How many regressors in X should be considered. |
intercept |
Logical. If TRUE, intercept is included which is not penalized. |
Value
init_values returns a list containing the following components:
residuals |
Residuals. |
coefficients |
Estimated coefficients. |
Source
Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.
interpol help function for missls
Description
interpol help function for missls
Usage
interpol(rho, xcent)
Arguments
rho |
autocorrelation function |
xcent |
centered time series |
Value
z new version of xcent
kweightsHAC help function for HAC
Description
kweightsHAC help function for HAC
Usage
kweightsHAC(
kernel = c("Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral"),
dimN,
bw
)
Arguments
kernel |
kernel function, choose between "Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral". |
dimN |
number of observations |
bw |
bandwidth parameter |
Value
ww weights
lagwinba Bartlett's Lag-window for indirect spectrum estimation
Description
lagwinba Bartlett's Lag-window for indirect spectrum estimation
Usage
lagwinba(NL)
Arguments
NL |
number of lags used for estimation |
Value
win vector, one-sided weights
Examples
win <-lagwinba(5)
lagwinpa Parzen's Lag-window for indirect spectrum estimation
Description
lagwinpa Parzen's Lag-window for indirect spectrum estimation
Usage
lagwinpa(NL)
Arguments
NL |
number of lags used for estimation |
Value
win vector, one-sided weights
Examples
win <- lagwinpa(5)
lagwintu Tukey's Lag-window for indirect spectrum estimation
Description
lagwintu Tukey's Lag-window for indirect spectrum estimation
Usage
lagwintu(NL)
Arguments
NL |
number of lags used for estimation |
Value
win vector, one-sided weights
Examples
win <- lagwintu(5)
lambdaCalculationHAC is an auxiliary function for rlassoHAC; it calculates the penalty parameters.
Description
lambdaCalculationHAC is an auxiliary function for rlassoHAC; it calculates the penalty parameters.
Usage
lambdaCalculationHAC(
X.dependent.lambda = FALSE,
c = 2,
gamma = 0.1,
kernel,
bands,
bns,
lns,
nboot,
y = NULL,
x = NULL
)
Arguments
X.dependent.lambda |
Logical, TRUE, if the penalization parameter depends on the design of the matrix x. FALSE, if independent of the design matrix (default). |
c |
Constant for the penalty with default c = 2 . |
gamma |
Constant for the penalty with default gamma=0.1. |
kernel |
String kernel function, choose between "Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral". |
bands |
Constant bandwidth parameter. |
bns |
Block length. |
lns |
Number of blocks. |
nboot |
Number of bootstrap iterations. |
y |
Residual which is used for calculation of the variance or the data-dependent loadings. |
x |
Regressors (vector, matrix or object can be coerced to matrix). |
Value
lambda0 |
Penalty term |
Ups0 |
Penalty loadings, vector of length p (no. of regressors) |
lambda |
This is lambda0 * Ups0 |
penalty |
Summary of the used penalty function. |
Source
Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.
lambdaCalculationLoad is an auxiliary function for rlassoLoad; it calculates the penalty parameters
with predefined loadings.
Description
lambdaCalculationLoad is an auxiliary function for rlassoLoad; it calculates the penalty parameters
with predefined loadings.
Usage
lambdaCalculationLoad(
X.dependent.lambda = FALSE,
c = 2,
gamma = 0.1,
load,
bns,
lns,
nboot,
y = NULL,
x = NULL
)
Arguments
X.dependent.lambda |
Logical, TRUE, if the penalization parameter depends on the design of the matrix x. FALSE, if independent of the design matrix (default). |
c |
Constant for the penalty with default c = 2 . |
gamma |
Constant for the penalty with default gamma=0.1. |
load |
Penalty loadings, vector of length p (no. of regressors). |
bns |
Block length. |
lns |
Number of blocks. |
nboot |
Number of bootstrap iterations. |
y |
Residual which is used for calculation of the variance or the data-dependent penalty. |
x |
Regressors (vector, matrix or object can be coerced to matrix). |
Value
lambda0 |
Penalty term |
Ups0 |
Penalty loadings, vector of length p (no. of regressors) |
lambda |
This is lambda0 * Ups0 |
penalty |
Summary of the used penalty function |
Source
Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.
ldrec does Levinson-Durbin recursion for determing all coefficients a(i,j)
Description
ldrec does Levinson-Durbin recursion for determing all coefficients a(i,j)
Usage
ldrec(a)
Arguments
a |
(p+1,1)-vector of acf of a time series: acov(0),...,acov(p) or 1,acor(1),..,acor(p) |
Value
mat (p,p+2)-matrix, coefficients in lower triangular, pacf in colum p+2 and Q(p) in colum p+1
Examples
data(HEARTBEAT)
a <- acf(HEARTBEAT,5,plot=FALSE)
mat <- ldrec(a$acf)
multifractal check
mfraccheck computes the absolute empirical moments of the differenced series for various lags
and moment orders. E.g. for lag = 3 and moment order = 1 the average absolute value of
the differences with lag 3 will be computed. By default, the maximum lag is determined
so that the differenced series contains at lest 50 observations.
Description
multifractal check
mfraccheck computes the absolute empirical moments of the differenced series for various lags
and moment orders. E.g. for lag = 3 and moment order = 1 the average absolute value of
the differences with lag 3 will be computed. By default, the maximum lag is determined
so that the differenced series contains at lest 50 observations.
Usage
mfraccheck(p, q_max)
Arguments
p |
the series |
q_max |
maximum moment order |
Value
out list with components:
moments |
matrix with lagmax raws and q_max columns containing the values of the absolute empirical moments |
lagmax |
the maximum lag for differencing |
Examples
data(NIKKEI)
p <- NIKKEI
out <- mfraccheck(log(p),5)
mom <- ts(out$moments,start=1)
ts.plot(mom, log ="xy",xlab="lag",ylab="abs. empirical moments", lty=c(1:5))
missar Substitution of missing values in a time series by
conditional exspectations of AR(p) models
Description
missar Substitution of missing values in a time series by
conditional exspectations of AR(p) models
Usage
missar(x, p, iterout = 0)
Arguments
x |
vector, the time series |
p |
integer, the maximal order of ar polynom 0 < p < 18, |
iterout |
if = 1, iteration history is printed |
Value
out list with elements
a |
(p,p)-matrix, estimated ar coefficients for ar-models |
y |
(n,1)-vector, completed time series |
iterhist |
matrix, NULL or the iteration history |
Source
Miller R.B., Ferreiro O. (1984) <doi.org/10.1007/978-1-4684-9403-7_12> "A Strategy to Complete a Time Series with Missing Observations"
Examples
data(HEARTBEAT)
x <- HEARTBEAT
x[c(20,21)] <- NA
out <- missar(x,2)
missls substitutes missing values in a time series using the LS approach with ARMA models
Description
missls substitutes missing values in a time series using the LS approach with ARMA models
Usage
missls(x, p = 0, tol = 0.001, theo = 0)
Arguments
x |
vector, the time series |
p |
integer, the order of polynom alpha(B)/beta(B) |
tol |
tolerance that can be set; it enters via tol*sd(x,na.rm=TRUE) |
theo |
(k,1)-vector, prespecified Inverse ACF, IACF (starting at lag 1) |
Value
y completed time series
Source
S. R. Brubacher and G. Tunnicliffe Wilson (1976) <https://www.jstor.org/stable/2346678> "Interpolating Time Series with Application to the Estimation of Holiday Effects on Electricity Demand Journal of the Royal Statistical Society"
Examples
data(HEARTBEAT)
x <- HEARTBEAT
x[c(20,21)] <- NA
out <- missls(x,p=2,tol=0.001,theo=0)
moveav smoothes a time series by moving averages
Description
moveav smoothes a time series by moving averages
Usage
moveav(y, q)
Arguments
y |
the series, a vector or a time series |
q |
scalar, span of moving average |
Value
g vector, smooth component
Examples
data(GDP)
g <- moveav(GDP,12)
plot(GDP) ; lines(g,col="red")
movemed smoothes a time series by moving medians
Description
movemed smoothes a time series by moving medians
Usage
movemed(y, q)
Arguments
y |
the series, a vector or a time series |
q |
scalar, span of moving median |
Value
g vector, smooth component
Examples
data(BIP)
g <- movemed(GDP,12)
plot(GDP) ; t <- seq(from = 1970, to = 2009.5,by=0.25) ; lines(t,g,col="red")
outidentify performs one iteration of Wei's iterative procedure to identify impact, locations and type
of outliers in arma processes
Description
outidentify performs one iteration of Wei's iterative procedure to identify impact, locations and type
of outliers in arma processes
Usage
outidentify(x, object, alpha = 0.05, robust = FALSE)
Arguments
x |
vector, the time series |
object |
output of a model fit with the function arima (from stats) |
alpha |
the level of the tests for deciding which value is to be considered an outlier |
robust |
logical, should the standard error be computed robustly? |
Value
out list with elements
outlier |
matrix with time index (ind), type of outlier (1 = AO, 2 = IO) and value of test statistic (lambda) |
arima.out |
output of final arima model where the outliers are incorporated as fixed regressors |
Examples
data(SPRUCE)
out <- arima(SPRUCE,order=c(2,0,0))
out2 <- outidentify(SPRUCE,out,alpha=0.05, robust = FALSE)
pacfmat sequence of partial autocorrelation matrices and related statistics for a multivariate time series
Description
pacfmat sequence of partial autocorrelation matrices and related statistics for a multivariate time series
Usage
pacfmat(y, lag.max)
Arguments
y |
multivariate time series |
lag.max |
maximum number of lag |
Value
out list with components:
M |
array with matrices of partial autocovariances divided by their standard error |
M1 |
array with indicators if partial autocovariances are significantly greater (+), lower (-) than the critical value or insignificant (.) |
R |
array with matrices of partial autocovariances |
S |
matrix of diagonals of residual covariances (row-wise) |
Test |
test statistic |
pval |
p value of test |
Examples
data(ICECREAM)
out <- pacfmat(ICECREAM,7)
periodogram determines the periodogram of a time series
Description
periodogram determines the periodogram of a time series
Usage
periodogram(y, nf, ACF = FALSE, type = "cov")
Arguments
y |
(n,1) vector, the time series or an acf at lags 0,1,...,n-1 |
nf |
scalar, the number of equally spaced frequencies; not necessay an integer |
ACF |
logical, FALSE, if y is ts, TRUE, if y is acf |
type |
c("cov","cor"), area under spectrum, can be variance or normed to 1. |
Value
out (floor(nf/2)+1,2) matrix, the frequencies and the periodogram
Examples
data(WHORMONE)
## periodogram at Fourier frequencies and frequencies 0 and 0.5
out <-periodogram(WHORMONE,length(WHORMONE)/2,ACF=FALSE,type="cov")
periodotest computes the p-value of the test for a hidden periodicity
Description
periodotest computes the p-value of the test for a hidden periodicity
Usage
periodotest(y)
Arguments
y |
vector, the time series |
Value
pval the p-value of the test
Examples
data(PIGPRICE)
y <- PIGPRICE
out <- stl(y,s.window=6)
e <- out$time.series[,3]
out <- periodotest(e)
perwinba Bartlett-Priestley window for direct spectral estimation
Description
perwinba Bartlett-Priestley window for direct spectral estimation
Usage
perwinba(e, n)
Arguments
e |
equal bandwidth (at most n frequencies are used for averaging) |
n |
length of time series |
Value
w weights (symmetric)
Examples
data(WHORMONE)
w <- perwinba(0.1,length(WHORMONE))
perwinda Daniell window for direct spectral estimation
Description
perwinda Daniell window for direct spectral estimation
Usage
perwinda(e, n)
Arguments
e |
equal bandwidth (at most n frequencies are used for averaging) |
n |
length of time series |
Value
w weights (symmetric)
Examples
data(WHORMONE)
w <- perwinda(0.1,length(WHORMONE))
perwinpa Parzen's window for direct spectral estimation
Description
perwinpa Parzen's window for direct spectral estimation
Usage
perwinpa(e, n)
Arguments
e |
equal bandwidth (at most n frequencies are used for averaging) |
n |
length of time series |
Value
w weights (symmetric)
Examples
data(WHORMONE)
w <- perwinpa(0.1,length(WHORMONE))
pestep help function for missar
Description
pestep help function for missar
Usage
pestep(f, xt)
Arguments
f |
IACF, inverse ACF |
xt |
segment of the time series |
Value
xt new version of xt
polymake generates the coefficients of an AR process given the zeros of the
characteristic polynomial. The norm of the roots must be greater than one for stationary processes.
Description
polymake generates the coefficients of an AR process given the zeros of the
characteristic polynomial. The norm of the roots must be greater than one for stationary processes.
Usage
polymake(r)
Arguments
r |
vector, the zeros of the characteristic polynomial |
Value
C coefficients (a[1],a[2],...,a[p]) of the polynomial 1 - a[1]z -a[2]z^2 -...- a[p]z^p
Examples
C <- polymake(c(2,-1.5,3))
psifair is a psi-function for robust estimation
Description
psifair is a psi-function for robust estimation
Usage
psifair(u)
Arguments
u |
vector |
Value
out transformed vector
Examples
out <- psifair(c(3.3,-0.7,2.1,1.8))
psihuber is a psi-function for robust estimation
Description
psihuber is a psi-function for robust estimation
Usage
psihuber(u)
Arguments
u |
vector |
Value
out transformed vector
Examples
out <- psihuber(c(3.3,-0.7,2.1,1.8))
rlassoHAC performs Lasso estimation under heteroscedastic and autocorrelated non-Gaussian disturbances.
Description
rlassoHAC performs Lasso estimation under heteroscedastic and autocorrelated non-Gaussian disturbances.
Usage
rlassoHAC(
x,
y,
kernel = "Bartlett",
bands = 10,
bns = 10,
lns = NULL,
nboot = 5000,
post = TRUE,
intercept = TRUE,
model = TRUE,
X.dependent.lambda = FALSE,
c = 2,
gamma = NULL,
numIter = 15,
tol = 10^-5,
threshold = NULL,
...
)
Arguments
x |
Regressors (vector, matrix or object can be coerced to matrix). |
y |
Dependent variable (vector, matrix or object can be coerced to matrix). |
kernel |
Kernel function, choose between "Truncated", "Bartlett" (by default), "Parzen", "Tukey-Hanning", "Quadratic Spectral". |
bands |
Bandwidth parameter with default bands=10. |
bns |
Block length with default bns=10. |
lns |
Number of blocks with default lns = floor(T/bns). |
nboot |
Number of bootstrap iterations with default nboot=5000. |
post |
Logical. If TRUE (default), post-Lasso estimation is conducted, i.e. a refit of the model with the selected variables. |
intercept |
Logical. If TRUE, intercept is included which is not penalized. |
model |
Logical. If TRUE (default), model matrix is returned. |
X.dependent.lambda |
Logical, TRUE, if the penalization parameter depends on the design of the matrix x. FALSE (default), if independent of the design matrix. |
c |
Constant for the penalty, default value is 2. |
gamma |
Constant for the penalty, default gamma=0.1/log(T) with T=data length. |
numIter |
Number of iterations for the algorithm for the estimation of the variance and data-driven penalty, ie. loadings. |
tol |
Constant tolerance for improvement of the estimated variances. |
threshold |
Constant applied to the final estimated lasso coefficients. Absolute values below the threshold are set to zero. |
... |
further parameters |
Value
rlassoHAC returns an object of class "rlasso". An object of class "rlasso" is a list containing at least the following components:
coefficients |
Parameter estimates. |
beta |
Parameter estimates (named vector of coefficients without intercept). |
intercept |
Value of the intercept. |
index |
Index of selected variables (logical vector). |
lambda |
Data-driven penalty term for each variable, product of lambda0 (the penalization parameter) and the loadings. |
lambda0 |
Penalty term. |
loadings |
Penalty loadings, vector of lenght p (no. of regressors). |
residuals |
Residuals, response minus fitted values. |
sigma |
Root of the variance of the residuals. |
iter |
Number of iterations. |
call |
Function call. |
options |
Options. |
model |
Model matrix (if model = TRUE in function call). |
Source
Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.
Examples
set.seed(1)
T = 100 #sample size
p = 20 # number of variables
b = 5 # number of variables with non-zero coefficients
beta0 = c(rep(10,b), rep(0,p-b))
rho = 0.1 #AR parameter
Cov = matrix(0,p,p)
for(i in 1:p){
for(j in 1:p){
Cov[i,j] = 0.5^(abs(i-j))
}
}
C <- chol(Cov)
X <- matrix(rnorm(T*p),T,p)%*%C
eps <- arima.sim(list(ar=rho), n = T+100)
eps <- eps[101:(T+100)]
Y = X%*%beta0 + eps
reg.lasso.hac1 <- rlassoHAC(X, Y,"Bartlett") #lambda is chosen independent of regressor
#matrix X by default.
bn = 10 # block length
bwNeweyWest = 0.75*(T^(1/3))
reg.lasso.hac2 <- rlassoHAC(X, Y,"Bartlett", bands=bwNeweyWest, bns=bn, nboot=5000,
X.dependent.lambda = TRUE, c=2.7)
rlassoLoad performs Lasso estimation under heteroscedastic and autocorrelated non-Gaussian disturbances
with predefined penalty loadings.
Description
rlassoLoad performs Lasso estimation under heteroscedastic and autocorrelated non-Gaussian disturbances
with predefined penalty loadings.
Usage
rlassoLoad(
x,
y,
load,
bns = 10,
lns = NULL,
nboot = 5000,
post = TRUE,
intercept = TRUE,
model = TRUE,
X.dependent.lambda = FALSE,
c = 2,
gamma = NULL,
numIter = 15,
tol = 10^-5,
threshold = NULL,
...
)
Arguments
x |
Regressors (vector, matrix or object can be coerced to matrix). |
y |
Dependent variable (vector, matrix or object can be coerced to matrix). |
load |
Penalty loadings, vector of length p (no. of regressors). |
bns |
Block length with default bns=10. |
lns |
Number of blocks with default lns = floor(T/bns). |
nboot |
Number of bootstrap iterations with default nboot=5000. |
post |
Logical. If TRUE (default), post-Lasso estimation is conducted, i.e. a refit of the model with the selected variables. |
intercept |
Logical. If TRUE, intercept is included which is not penalized. |
model |
Logical. If TRUE (default), model matrix is returned. |
X.dependent.lambda |
Logical, TRUE, if the penalization parameter depends on the design of the matrix x. FALSE (default), if independent of the design matrix. |
c |
Constant for the penalty default is 2. |
gamma |
Constant for the penalty default gamma=0.1/log(T) with T=data length. |
numIter |
Number of iterations for the algorithm for the estimation of the variance and data-driven penalty. |
tol |
Constant tolerance for improvement of the estimated variances. |
threshold |
Constant applied to the final estimated lasso coefficients. Absolute values below the threshold are set to zero. |
... |
further parameters |
Value
rlassoLoad returns an object of class "rlasso". An object of class "rlasso" is a list containing at least the following components:
coefficients |
Parameter estimates. |
beta |
Parameter estimates (named vector of coefficients without intercept). |
intercept |
Value of the intercept. |
index |
Index of selected variables (logical vector). |
lambda |
Data-driven penalty term for each variable, product of lambda0 (the penalization parameter) and the loadings. |
lambda0 |
Penalty term. |
loadings |
Penalty loadings, vector of lenght p (no. of regressors). |
residuals |
Residuals, response minus fitted values. |
sigma |
Root of the variance of the residuals. |
iter |
Number of iterations. |
call |
Function call. |
options |
Options. |
model |
Model matrix (if model = TRUE in function call). |
Source
Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.
Examples
set.seed(1)
T = 100 #sample size
p = 20 # number of variables
b = 5 # number of variables with non-zero coefficients
beta0 = c(rep(10,b), rep(0,p-b))
rho = 0.1 #AR parameter
Cov = matrix(0,p,p)
for(i in 1:p){
for(j in 1:p){
Cov[i,j] = 0.5^(abs(i-j))
}
}
C <- chol(Cov)
X <- matrix(rnorm(T*p),T,p)%*%C
eps <- arima.sim(list(ar=rho), n = T+100)
eps <- eps[101:(T+100)]
Y = X%*%beta0 + eps
fit1 = rlasso(X, Y, penalty = list(homoscedastic = "none",
lambda.start = 2*0.5*sqrt(T)*qnorm(1-0.1/(2*p))), post=FALSE)
beta = fit1$beta
intercept = fit1$intercept
res = Y - X %*% beta - intercept * rep(1, length(Y))
load = rep(0,p)
for(i in 1:p){
load[i] = sqrt(lrvar(X[,i]*res)*T)
}
reg.lasso.load1 <- rlassoLoad(X,Y,load) #lambda is chosen independent of regressor
#matrix X by default.
bn = 10 # block length
reg.lasso.load2 <- rlassoLoad(X, Y,load, bns=bn, nboot=5000,
X.dependent.lambda = TRUE, c=2.7)
robsplinedecomp decomposes a vector into trend, season and irregular component
by robustified spline approach; a time series attribute is lost
Description
robsplinedecomp decomposes a vector into trend, season and irregular component
by robustified spline approach; a time series attribute is lost
Usage
robsplinedecomp(y, d, alpha, beta, Plot = FALSE)
Arguments
y |
the series, a vector or a time series |
d |
seasonal period |
alpha |
smoothing parameter for trend component (the larger alpha is, the smoother will the smooth component g be) |
beta |
smoothing parameter for seasonal component |
Plot |
logical, should a plot be produced? |
Value
out list with the elements trend, season, residual
Examples
data(GDP)
out <- robsplinedecomp(GDP,4,2,10,Plot=FALSE)
simpledecomp decomposes a vector into trend, season and irregular component
by linear regression approach
Description
simpledecomp decomposes a vector into trend, season and irregular component
by linear regression approach
Usage
simpledecomp(y, trend = 0, season = 0, Plot = FALSE)
Arguments
y |
the series, a vector or a time series |
trend |
order of trend polynomial |
season |
period of seasonal component |
Plot |
logical, should a plot be produced? |
Value
out: (n,3) matrix
1. column |
smooth component |
2. column |
seasonal component |
3. column |
irregular component |
Examples
data(GDP)
out <- simpledecomp(GDP,trend=3,season=4,Plot=FALSE)
smoothls smoothes a time series by Whittaker graduation.
The function depends on the package Matrix.
Description
smoothls smoothes a time series by Whittaker graduation.
The function depends on the package Matrix.
Usage
smoothls(y, beta = 0)
Arguments
y |
the series, a vector or a time series |
beta |
smoothing parameter >=0 (the larger beta is, the smoother will g be) |
Value
g vector, smooth component
Examples
data(GDP)
g <- smoothls(GDP,12)
plot(GDP)
t <- seq(from = tsp(GDP)[1], to = tsp(GDP)[2],by=1/tsp(GDP)[3]) ; lines(t,g,col="red")
smoothrb smoothes a time series robustly by using Huber's psi-function.
The initialisation uses a moving median.
Description
smoothrb smoothes a time series robustly by using Huber's psi-function.
The initialisation uses a moving median.
Usage
smoothrb(y, beta = 0, q = NA)
Arguments
y |
the series, a vector or a time series |
beta |
smoothing parameter (The larger beta is, the smoother will the smooth component g be.) |
q |
length of running median which is used to get initial values |
Value
g vector, the smooth component
Examples
data(GDP)
g <- smoothrb(GDP,8,q=8)
plot(GDP) ; t <- seq(from = 1970, to = 2009.5,by=0.25) ; lines(t,g,col="red")
specest direct spectral estimation of series y
using periodogram window win
Description
specest direct spectral estimation of series y
using periodogram window win
Usage
specest(
y,
nf,
e,
win = c("perwinba", "perwinpa", "perwinda"),
conf = 0,
type = "cov"
)
Arguments
y |
(n,1) vector, the ts |
nf |
number of equally spaced frequencies |
e |
equal bandwidth, must be 0 <= e <0.5 |
win |
string, name of periodogram window (possible: "perwinba", "perwinpa", "perwinda") |
conf |
scalar, the level for confidence intervals |
type |
c("cov","cor"), area under spectrum is variance or is normed to 1. |
Value
est (nf+1,2)- or (nf+1,4)-matrix:
column 1: |
frequencies 0, 1/n, 2/n, ..., m/n |
column 2: |
the estimated spectrum |
column 3+4: |
the confidence bounds |
Examples
data(WHORMONE)
est <- specest(WHORMONE,50,0.05,win = c("perwinba","perwinpa","perwinda"),conf=0,type="cov")
specplot plot of spectral estimate
Description
specplot plot of spectral estimate
Usage
specplot(s, Log = FALSE)
Arguments
s |
(n,2) or (n,4) matrix, output of specest |
Log |
logical, if TRUE, the logs of the spectral estimates are shown |
Examples
data(WHORMONE)
est <- specest(WHORMONE,50,0.05,win = c("perwinba","perwinpa"),conf=0,type="cov")
specplot(est,Log=FALSE)
splinedecomp decomposes a time series into trend, season and irregular component
by spline approach.
Description
splinedecomp decomposes a time series into trend, season and irregular component
by spline approach.
Usage
splinedecomp(x, d, alpha, beta, Plot = FALSE)
Arguments
x |
the series, a vector or a time series |
d |
seasonal period |
alpha |
smoothing parameter for trend component (The larger alpha is, the smoother will the smooth component g be.) |
beta |
smoothing parameter for seasonal component |
Plot |
logical, should a plot be produced? |
Value
out (n,3) matrix:
1. column |
smooth component |
2. column |
seasonal component |
3. column |
irregular component |
Examples
data(GDP)
out <- splinedecomp(GDP,4,2,4,Plot=FALSE)
statcheck determines the means, standard deviations and acf's of segmets of a time series
and plots the acf's for the segments.
Description
statcheck determines the means, standard deviations and acf's of segmets of a time series
and plots the acf's for the segments.
Usage
statcheck(y, d)
Arguments
y |
the series, a vector or a time series |
d |
scalar, number of segments |
Value
out list with components:
ms |
matrix with means and standard deviations of the segments |
ac |
matrix with acf's, the first column: acf of the series, the others: acf's of the segments |
Examples
data(COFFEE)
out <- statcheck(COFFEE,4)
subsets determines all subsets of a set of n elements (labelled by 1,2,...,n ).
Description
subsets determines all subsets of a set of n elements (labelled by 1,2,...,n ).
Usage
subsets(n)
Arguments
n |
scalar, integer >= 1 |
Value
mat (2^n,n)-matrix, each row gives the membership indicators of the elements 1,2,...,n
Examples
out <- subsets(4)
symplot produces a symmetry plot
Description
symplot produces a symmetry plot
Usage
symplot(y)
Arguments
y |
the series, a vector or a time series |
Examples
data(LYNX)
symplot(LYNX)
taper taper modification of a time series
Description
taper taper modification of a time series
Usage
taper(y, part)
Arguments
y |
the time series |
part |
scalar, 0 <= part <= 0.5, part of modification (at each end of y) |
Value
tp tapered time series
Examples
data(WHORMONE)
out <-taper(WHORMONE,0.3)
plot(WHORMONE)
lines(out,col="red")
tsmat constructs a (n-p+1,p) matrix from a time series
where the first column is the shortened series y[p],...,y[n], the second is y[p-1],...,y[n-1], etc.
Description
tsmat constructs a (n-p+1,p) matrix from a time series
where the first column is the shortened series y[p],...,y[n], the second is y[p-1],...,y[n-1], etc.
Usage
tsmat(y, p)
Arguments
y |
the series, a vector or a time series of length n |
p |
desired number of columns |
Value
mat (n-p+1,p) matrix
Examples
out <- tsmat(c(1:20),4)
vartable determines table of variate differences
Description
vartable determines table of variate differences
Usage
vartable(y, season)
Arguments
y |
the series, a vector or a time series ( no NA's ) |
season |
scalar, period of seasonal component |
Value
d matrix with ratios of variances for differend numbers of simple and seasonal differencing
Examples
data(GDP)
out <- vartable(GDP,4)
wntest graphical test for white noise for a time series or a series of regression residuals
Description
wntest graphical test for white noise for a time series or a series of regression residuals
Usage
wntest(e, a, k = 0)
Arguments
e |
vector, the time series (k = 0) or residuals (k > 0) |
a |
scalar, level of significance |
k |
scalar >= 0, number of regressors used to compute e as residuals |
Value
tp vector, value of test statistic and p-value
Examples
data(WHORMONE)
out <- wntest(WHORMONE,0.05,0)