KSS: KSS-Routine

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/KSS.methods.R

Description

Estimation of Panel Data Models with Heterogeneous Time Trends

Usage

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KSS(formula,
    additive.effects = c("none", "individual", "time", "twoways"),
    consult.dim.crit = FALSE,
    d.max            = NULL,
    sig2.hat         = NULL,
    factor.dim       = NULL,
    level            = 0.01,
    spar             = NULL,
    CV               = FALSE,
    convergence      = 1e-6,
    restrict.mode    = c("restrict.factors","restrict.loadings"), ...)

Arguments

formula

An object of class 'formula'.

additive.effects

Type of Data Transformations:

  • "none": for no transformation

  • "individual": for within transformation

  • "time": for between transformation

  • "twoways": for twoways transformation

consult.dim.crit

logical.

  • If consult.dim.crit is FALSE (default) and factor.dim is NULL: Only the dimensionality criterion of Kneip, Sickles & Song 2012 is used.

  • If consult.dim.crit is TRUE and factor.dim is NULL: All implemented dimensionality criteria as implemented in the function OptDim() are computed and the user has to select one proposed dimension via a GUI.

d.max

A maximal dimension needed for some dimensionality-criteria that are implemented in the function OptDim(). The default (d.max=NULL) yields to an internal selection of d.max.

sig2.hat

Standard deviation of the error-term. The default (sig2.hat=NULL) yields to an internal estimation of sig2.hat.

factor.dim

Dimension of Factor-Structure. The default (factor.dim=NULL) yields to an internal estimation of factor.dim.

level

Significance-level for Dimensionality-Criterion of Kneip, Sickles & Song 2012.

spar

Smoothing parameter for spline smoothing of the residuals. If (spar=NULL) (default) and CV=FALSE spar is determined via generalized cross validation (GCV).

CV

logical. Selects the procedure for the determination of the smoothing parameter spar.

  • If CV=FALSE (default) and spar=NULL: The smoothing parameter spar is determined by GCV.

  • If CV=TRUE and spar=NULL: The smoothing parameter spar is determined by Leave-one-out cross validation (CV).

convergence

Convergence criterion for the CV-optimization of the smoothing parameter spar. Default is convergence=1e-6.

restrict.mode

Type of Restriction on the Factor-Structure:

  • "restrict.factors": Factors are restricted to have an euclidean norm of 1.

  • "restrict.loadings": Factor-Loadings are restricted to have an euclidean norm of 1.

...

Additional arguments to be passed to the low level functions.

Details

'KSS' is a function to estimate panel data models with unobserved heterogeneous time trends v_i(t). The considered model in Kneip, Sickles & Song (2012) is given by Y_{it}=θ_{t}+∑_{j=1}^Pβ_{j} X_{itj}+v_i(t)+ε_{it}, i=1,...,n; t=1,...,T. Where the individual time trends, v_i(t), are assumed to come from a finite dimensional factor model v_i(t)=∑_{l=1}^dλ_{il}f_l(t), λ_{il}\in R, f_l\in L^2[0,T]. The unobserved functions v_i(t) can be interpreted as smooth functions of a continuous argument t, as well as stochastic processes for discrete argument t.

Value

'KSS' returns an object of 'class' '"KSS"'.

An object of class '"KSS"' is a list containing at least the following components:

Author(s)

Dominik Liebl

References

See Also

Eup

Examples

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## See the example in 'help(Cigar)' in order to take a look at the
## data set Cigar

##########
## DATA ##
##########

data(Cigar)
## Panel-Dimensions:
N <- 46
T <- 30
## Dependent variable:
  ## Cigarette-Sales per Capita
  l.Consumption    <- log(matrix(Cigar$sales, T,N))
## Independent variables:
  ## Consumer Price Index
  cpi        <- matrix(Cigar$cpi, T,N)
  ## Real Price per Pack of Cigarettes 
  l.Price  <- log(matrix(Cigar$price, T,N)/cpi)
  ## Real Disposable Income per Capita  
  l.Income    <- log(matrix(Cigar$ndi,   T,N)/cpi)

## Estimation:
KSS.fit      <- KSS(l.Consumption~l.Price+l.Income, CV=TRUE)
(KSS.fit.sum <- summary(KSS.fit))
plot(KSS.fit.sum)

phtt documentation built on May 30, 2017, 12:30 a.m.

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