Package 'tsapp' reference manual

Title:	Time Series, Analysis and Application
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.
Authors:	Rainer Schlittgen
Maintainer:	Rainer Schlittgen <[email protected]>
License:	GPL
Version:	1.0.4
Built:	2025-01-29 03:07:35 UTC
Source:	https://github.com/cran/tsapp

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
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) 
data(ACCIDENT)
## maybe  tsp(ACCIDENT) ; plot(ACCIDENT)

`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)
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) 
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")
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") 
data(LYNX)
  acfpacf(log(LYNX),15,HV="H")

Alcohol Demand, UK, 1870-1938.

Description

Alcohol Demand, UK, 1870-1938.

Usage

ALCINCOME
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) 
data(ALCINCOME)
## maybe  tsp(ALCINCOME) ; plot(ALCINCOME)

`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)
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) 

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)
aspectratio(y)

Arguments

`y`	time series

Value

a scalar, the aspect ratio

Examples

data(GDP)
a <- aspectratio(GDP) 
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)
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")   
data(GDP)
yf <- bandfilt(GDP,5,2,6)
plot(GDP); lines(yf+mean(GDP),col="red")

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
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) 
data(BEER)
## maybe  tsp(BEER) ; plot(BEER)

`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")
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") 
data(ICECREAM)
y <- ICECREAM
out <- bispeces(y[,1],y[,2],8,win="bartlett")

Weekly number of births in New York

Description

Weekly number of births in New York

Usage

BLACKOUT
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) 
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)
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)

data(INORDER)
lambda <-BoxCox(INORDER,6,Plot=FALSE)

U.S. annual coffee consumption

Description

U.S. annual coffee consumption

Usage

COFFEE
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) 
data(COFFEE)
## maybe  tsp(COFFEE) ; plot(COFFEE)

Market value of DAX

Description

Market value of DAX

Usage

DAX
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) 
data(DAX)
## maybe  tsp(DAX) ; plot(DAX)

Incidences of insulin-dependent diabetes mellitus

Description

Incidences of insulin-dependent diabetes mellitus

Usage

DIABETES
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) 
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
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) 
data(DOMINANCE)
## maybe  tsp(DOMINANCE) ; plot(DOMINANCE)

`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)
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)
data(IBM) 
y <- diff(log(IBM))
out <- dynspecest(y,60,50,0.2,theta=0,phi=15,d=1,Plot=FALSE)

ENGINES is an alias for MACHINES

Description

ENGINES is an alias for MACHINES

Usage

ENGINES
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)  
data(ENGINES)
## maybe  tsp(ENGINES) ; plot(ENGINES)

Portfolio-Insurance-Strategies

Description

Portfolio-Insurance-Strategies

Usage

FINANCE
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) 
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
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) 
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
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) 
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)
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) 

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)
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. 
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
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) 
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
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) 
data(HSV)
## maybe  tsp(HSV) ; plot(HSV)

IBM's stock price

Description

IBM's stock price

Usage

IBM
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) 
data(IBM)
## maybe  tsp(IBM) ; plot(IBM)

Temperature and consumption of ice cream

Description

Temperature and consumption of ice cream

Usage

ICECREAM
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) 
data(ICECREAM)
## maybe  tsp(ICECREAM) ; plot(ICECREAM)

`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)
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.

Income orders of a company

Description

Income orders of a company

Usage

INORDER
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) 
data(INORDER)
## maybe  tsp(INORDER) ; plot(INORDER)

`interpol` help function for missls

Description

interpol help function for missls

Usage

interpol(rho, xcent)
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
)
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

Subsoil water level and precipitation at pilot well L921

Description

Subsoil water level and precipitation at pilot well L921

Usage

L921
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) 
data(L921)
## maybe  tsp(L921) ; plot(L921)

`lagwinba` Bartlett's Lag-window for indirect spectrum estimation

Description

lagwinba Bartlett's Lag-window for indirect spectrum estimation

Usage

lagwinba(NL)
lagwinba(NL)

Arguments

`NL`	number of lags used for estimation

Value

win vector, one-sided weights

Examples

win <-lagwinba(5) 
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)
lagwinpa(NL)

Arguments

`NL`	number of lags used for estimation

Value

win vector, one-sided weights

Examples

win <- lagwinpa(5)  
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)
lagwintu(NL)

Arguments

`NL`	number of lags used for estimation

Value

win vector, one-sided weights

Examples

win <- lagwintu(5)   
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
)
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
)
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)
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$acf)
data(HEARTBEAT)
a <- acf(HEARTBEAT,5,plot=FALSE)
mat <- ldrec(a$acf)

Daily subsoil water level and precipitation at pilot well Lith

Description

Daily subsoil water level and precipitation at pilot well Lith

Usage

LITH
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) 
data(LITH)
## maybe  tsp(LITH) ; plot(LITH)

`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)
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)

data(COFFEE)
out <- arima(COFFEE,order=c(1,0,0))
BT <- LjungBoxPierceTest(out$residuals,1,20)

Level of Luteinzing hormone of a cow

Description

Level of Luteinzing hormone of a cow

Usage

LUHORMONE
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
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) 
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
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)
data(LYNXHARE)

Number of incoming orders for machines

Description

Number of incoming orders for machines

Usage

MACHINES
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)  
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
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) 
data(MAUNALOA)
## maybe  tsp(MAUNALOA) ; plot(MAUNALOA)

Stock market price of MDAX

Description

Stock market price of MDAX

Usage

MDAX
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]) 
data(MDAX)
## maybe  tsp(MDAX) ; plot(MDAX[,3])

Melanoma incidence in Connecticut

Description

Melanoma incidence in Connecticut

Usage

MELANOM
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]) 
data(MELANOM)
## maybe  tsp(MELANOM) ; plot(MELANOM[,-1])

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)
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$moments,start=1)
ts.plot(mom, log ="xy",xlab="lag",ylab="abs. empirical moments", lty=c(1:5)) 
data(NIKKEI)
p <- NIKKEI
out <- mfraccheck(log(p),5)
mom <- ts(out$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)
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)
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)
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)
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)
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")  
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)
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")  
data(BIP)
g <- movemed(GDP,12) 
 plot(GDP) ; t <- seq(from = 1970, to = 2009.5,by=0.25) ; lines(t,g,col="red")

Annual trade of muskrat pelts

Description

Annual trade of muskrat pelts

Usage

MUSKRAT
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) 
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
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) 
data(NIKKEI)
## maybe plot(NIKKEI)

`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)
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)
data(SPRUCE)
out <- arima(SPRUCE,order=c(2,0,0))
out2 <- outidentify(SPRUCE,out,alpha=0.05, robust = FALSE)

Amount of an Oxygen isotope

Description

Amount of an Oxygen isotope

Usage

OXYGEN
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])
data(OXYGEN)
## maybe   plot(OXYGEN[,1],OXYGEN[,2],type="l"); rug(OXYGEN[,1])

`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)
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) 
data(ICECREAM)
out <- pacfmat(ICECREAM,7)

Two measurements at a paper machine

Description

Two measurements at a paper machine

Usage

PAPER
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) 
data(PAPER)
## maybe  tsp(PAPER) ; plot(PAPER)

`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")
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") 
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)
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$time.series[,3]
out <- periodotest(e)
data(PIGPRICE)
y <- PIGPRICE
out <- stl(y,s.window=6)  
e <- out$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)
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)) 

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)
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)) 

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)
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)) 

data(WHORMONE)
w <- perwinpa(0.1,length(WHORMONE))

`pestep` help function for missar

Description

pestep help function for missar

Usage

pestep(f, xt)
pestep(f, xt)

Arguments

`f`	IACF, inverse ACF
`xt`	segment of the time series

Value

xt new version of xt

Monthly prices for pigs

Description

Monthly prices for pigs

Usage

PIGPRICE
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) 
data(PIGPRICE)
## maybe  tsp(PIGPRICE) ; plot(PIGPRICE)

`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)
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))

C <- polymake(c(2,-1.5,3))

Peak power demand in Berlin

Description

Peak power demand in Berlin

Usage

PPDEMAND
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) 
data(PPDEMAND)
## maybe  tsp(PPDEMAND) ; plot(PPDEMAND)

Production index of manufacturing industries

Description

Production index of manufacturing industries

Usage

PRODINDEX
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) 
data(PRODINDEX)
## maybe  tsp(PRODINDEX) ; plot(PRODINDEX)

`psifair` is a psi-function for robust estimation

Description

psifair is a psi-function for robust estimation

Usage

psifair(u)
psifair(u)

Arguments

u

vector

Value

out transformed vector

Examples

 
out <- psifair(c(3.3,-0.7,2.1,1.8))

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)
psihuber(u)

Arguments

u

vector

Value

out transformed vector

Examples

out <- psihuber(c(3.3,-0.7,2.1,1.8)) 

out <- psihuber(c(3.3,-0.7,2.1,1.8))

Annual amount of rainfall in Los Angeles

Description

Annual amount of rainfall in Los Angeles

Usage

RAINFALL
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) 
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
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) 
data(REDWINE)
## maybe  tsp(REDWINE) ; plot(REDWINE)

`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,
  ...
)
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) 

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,
  ...
)
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)
 
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)
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) 
data(GDP) 
out  <- robsplinedecomp(GDP,4,2,10,Plot=FALSE)

`RS` rescaled adjusted range statistic

Description

RS rescaled adjusted range statistic

Usage

RS(x, k)
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)  
data(TREMOR)
 R <- RS(TREMOR,10)

Monthly sales of a company

Description

Monthly sales of a company

Usage

SALES
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) 
data(SALES)
## maybe  tsp(SALES) ; plot(SALES)

CO2-Concentration obtained in Schauinsland, Germany

Description

CO2-Concentration obtained in Schauinsland, Germany

Usage

SCHAUINSLAND
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) 
data(SCHAUINSLAND)
## maybe  tsp(SCHAUINSLAND) ; plot(SCHAUINSLAND)

`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)
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) 
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)
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")  
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)
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")  
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"
)
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") 

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)
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) 
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)
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) 
data(GDP)
out  <- splinedecomp(GDP,4,2,4,Plot=FALSE)

Annual logging of spruce wood.

Description

Annual logging of spruce wood.

Usage

SPRUCE
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) 
data(SPRUCE)
## maybe  tsp(SPRUCE) ; plot(SPRUCE)

`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)
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)   
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)
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)

out <- subsets(4)

`symplot` produces a symmetry plot

Description

symplot produces a symmetry plot

Usage

symplot(y)
symplot(y)

Arguments

`y`	the series, a vector or a time series

Examples


data(LYNX)
symplot(LYNX)   
data(LYNX)
symplot(LYNX)

`taper` taper modification of a time series

Description

taper taper modification of a time series

Usage

taper(y, part)
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") 
data(WHORMONE)
out <-taper(WHORMONE,0.3)
 
plot(WHORMONE) 
lines(out,col="red")

Monthly community taxes in Germany (billions EURO)

Description

Monthly community taxes in Germany (billions EURO)

Usage

TAXES
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) 
data(TAXES)
## maybe  tsp(TAXES) ; plot(TAXES)

Mean thickness of annual tree rings

Description

Mean thickness of annual tree rings

Usage

TREERING
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) 
data(TREERING)
## maybe  tsp(TREERING) ; plot(TREERING)

Measurements of physiological tremor

Description

Measurements of physiological tremor

Usage

TREMOR
TREMOR

Format

TREMOR is a univariate time series of length 400.

TREMOR: Tremor

Examples

data(TREMOR)
## maybe  tsp(TREMOR) ; plot(TREMOR) 
data(TREMOR)
## maybe  tsp(TREMOR) ; plot(TREMOR)

`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)
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)

out <- tsmat(c(1:20),4)

Population of USA

Description

Population of USA

Usage

USAPOP
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) 
data(USAPOP)
## maybe  tsp(USAPOP) ; plot(USAPOP)

`vartable` determines table of variate differences

Description

vartable determines table of variate differences

Usage

vartable(y, season)
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) 

data(GDP)
out <- vartable(GDP,4)

Concentration of growth hormone of a bull

Description

Concentration of growth hormone of a bull

Usage

WHORMONE
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) 
data(WHORMONE)
## maybe  tsp(WHORMONE) ; plot(WHORMONE)

`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)
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) 
data(WHORMONE)
out <- wntest(WHORMONE,0.05,0)

Package 'tsapp'

Help Index

Monthly numbers of road traffic accidents with personal injury in BRD

Description

Usage

Format

Source

Examples

acfmat computes a sequence of autocorrelation matrices for a multivariate time series

Description

Usage

Arguments

Value

Examples

acfpacf produces a plot of the acf and the pacf of a time series

Description

Usage

Arguments

Examples

Alcohol Demand, UK, 1870-1938.

Description

Usage

Format

Source

Examples

armathspec determines the theoretical spectrum of an arma process

Description

Usage

Arguments

Value

Examples

aspectratio determines the aspect ratio to plot a time series

Description

Usage

Arguments

Value

Examples

bandfilt does a bandpass filtering of a time series

Description

Usage

Arguments

Value

Examples

Monthly beer production in Australia: megalitres. Includes ale and stout. Does not include beverages with alcohol percentage less than 1.15.

Description

Usage

Format

Source

Examples

bispeces performs indirect bivariate spectral estimation of two series y1, y2 using lagwindows

Description

Usage

Arguments

Value

Examples

Weekly number of births in New York

Description

Usage

Format

Source

Examples

BoxCox determines the power of a Box-Cox transformation to stabilize the variance of a time series

Description

Usage

Arguments

Value

Examples

U.S. annual coffee consumption

Description

Usage

Format

Source

Examples

Market value of DAX

Description

Usage

Format

Examples

Incidences of insulin-dependent diabetes mellitus

Description

`acfmat` computes a sequence of autocorrelation matrices for a multivariate time series

`acfpacf` produces a plot of the acf and the pacf of a time series

`armathspec` determines the theoretical spectrum of an arma process

`aspectratio` determines the aspect ratio to plot a time series

`bandfilt` does a bandpass filtering of a time series

`bispeces` performs indirect bivariate spectral estimation of two series y1, y2 using lagwindows

`BoxCox` determines the power of a Box-Cox transformation to stabilize the variance of a time series

`dynspecest` performs a dynamic spectrum estimation

`Grangercaus` determines three values of BIC from a twodimensional VAR process

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".