Eup: Eup-Routine

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

View source: R/Eup.methods.R

Description

Estimation of Panel Data Models with Interactive Fixed Effects.

Usage

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Eup(formula,
    additive.effects = c("none", "individual", "time", "twoways"),
    dim.criterion    = c("PC1", "PC2", "PC3", "BIC3","IC1", "IC2" , "IC3",
                         "IPC1", "IPC2", "IPC3"),
    d.max            = NULL,
    sig2.hat         = NULL,
    factor.dim       = NULL,
    double.iteration = TRUE,
    start.beta       = NULL,
    max.iteration    = 500,
    convergence      = 1e-6,
    restrict.mode    = c("restrict.factors", "restrict.loadings"),
    ...)

Arguments

formula

An object of class 'formula' where the arguments are matrices. The number of rows has to be equal to the temporal dimension and the number of columns has to be equal to the number of individuals. The details of model specification are given under 'Details'.

additive.effects

Type of Data Transformations:

  • "none": for no transformation

  • "individual": for within transformation

  • "time": for between transformation

  • "twoways": for twoways transformation

dim.criterion

The dimensionality criterion to be used if factor.dim is left unspecified. The default criterion is "PC1"

.

d.max

Maximal dimension used in the dimensionality-criteria of Bai (2009). The default (d.max=NULL) yields to an internal selection of d.max.

sig2.hat

The squared standard deviation of the error-term required for the computation of some dimensionality criteria. The user can specify it in instead of d.max. The default (sig2.hat=NULL) yields to an internal estimation.

factor.dim

Dimension of Factor-Structure, pre-specified by the user. The default (factor.dim=NULL) yields to an internal estimation.

double.iteration

logical. If FALSE the update of the factor dimension d will be done simultaneously with remaining model parameters without alternating between inner and outer iteration. This may speed up computations, but the convergence is less stable than in the default setting.

start.beta

allows the user to give a vector of starting values for the slope parameters.

max.iteration

controls the maximum number of iterations. The default is '500'.

convergence

Convergence condition of the estimators. The default is '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

'Eup' is a function to estimate equidistant panel data models with unobserved multiple time varying individual effects. The considered model is given by Y_{it}= ∑_{j=1}^Pβ_{j}X_{itj}+v_{it}+ ε_{it}, i=1,...,n; t=1,...,T. Where the individual time trends, v_it, are assumed to come from a finite dimensional factor model: v_{it}=∑_{l=1}^dλ_{il}f_{lt}.

Inferences about the slope parameters can be obtained by using the method summary(). The type of correlation and heteroskedasticity in the idiosyncratic errors can be specified by choosing the corresponding number for the argument error.type = c(1, 2, 3, 4, 5, 6, 7, 8) in summary(), where

The default is 1. In presence of serial correlations (cases 5 and 8), the kernel weights required for estimating the long-run covariance can be externally specified by given a vector of weights in the argument kernel.weights. By default, the function uses internally the linearly decreasing weights of Newey and West (1987) and a truncation at the lower integer part of \min(√{n},√{T}). If case 7 or 8 are chosen, the method summary() calculates the realization of the bias corrected estimators and gives appropriate inference. The bias corrected coefficients can be called by using the method coef() to the object produced by summary().

Value

'Eup' returns an object of 'class' '"Eup"' containing the following components:

Author(s)

Oualid Bada

References

See Also

KSS, OptDim

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
  d.l.Consumption    <- diff(log(matrix(Cigar$sales, T,N)))
## Independent variables:
  ## Consumer Price Index
  cpi        <- matrix(Cigar$cpi, T,N)
  ## Real Price per Pack of Cigarettes 
  d.l.Price  <- diff(log(matrix(Cigar$price, T,N)/cpi))
  ## Real Disposable Income per Capita  
  d.l.Income    <- diff(log(matrix(Cigar$ndi,   T,N)/cpi))
  
## Estimation:
Eup.fit      <- Eup(d.l.Consumption~d.l.Price+d.l.Income)
(Eup.fit.sum <- summary(Eup.fit))

## Plot the components of the estimated individual effects

plot(Eup.fit.sum)

phtt documentation built on May 31, 2017, 4:17 a.m.

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