Tests to check the model specifications
Description
Tests to check the model specifications
Usage
1  checkSpecif(obj1, obj2, level = 0.05)

Arguments
obj1 

obj2 
An object of class ' 
level 
The significance level. 
Details
This function is equipped with two types of specificationtests:
The first specificationtests is the Hausmantype test proposed by Bai (2009), which is computed if
obj1
as well asobj2
are specified by 'Eup
'objects. In this case the model given to the argumentsobj1
andobj2
are compared with each other. Note that this test assumes that the (unobserved) true factor dimension is lower or equal to 2; see Section 9 in Bai (2009) for more details.Given the assumption that there is only one unobserved common factor:
NullHypothesis: 'The unobserved common factor is a classical
individual
ortime
effect'.AlternativeHypothesis: 'The unobserved common factor is an arbitrary process'.
Given the assumption that there are two unobserved common factors:
NullHypothesis: 'The two unobserved common factors are classical
twoways
effects'.AlternativeHypothesis: 'The two unobserved common factors are arbitrary processes'.
The second specificationtest tests the existence of an additional factor structure beyond a classical additive effects model; as suggested in Kneip, Sickles, and Song (2012), which is applied if only
obj1
is specified andobj2
is left unspecified. This test can be used for 'Eup
'objects as well as for 'KSS
'objects.NullHypothesis: 'There are no unobserved common factors beyond the classical
individual
,time
, ortwoways
effects'.AlternativeHypothesis: 'There are additional unobserved common factors'.
Author(s)
Oualid Bada, Dominik Liebl
References
Bai, J., 2009 “Panel data models with interactive fixed effects”, Econometrica
Kneip, A., Sickles, R. C., Song, W., 2012 “A New Panel Data Treatment for Heterogeneity in Time Trends”, Econometric Theory
See Also
KSS, Eup, OptDim
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67  ## See the example in 'help(Cigar)' in order to take a look at the
## data set 'Cigar'
##########
## DATA ##
##########
data(Cigar)
## PanelDimensions:
N < 46
T < 30
## Dependent variable:
## CigaretteSales per Capita
l.Consumption < log(matrix(Cigar$sales, T,N))
d.l.Consumption < diff(l.Consumption)
## Independent variables:
## Consumer Price Index
cpi < matrix(Cigar$cpi, T,N)
## Real Price per Pack of Cigarettes
l.Price < log(matrix(Cigar$price, T,N)/cpi)
d.l.Price < diff(l.Price)
## Real Disposable Income per Capita
l.Income < log(matrix(Cigar$ndi, T,N)/cpi)
d.l.Income < diff(l.Income)
#####################################################################
## Testing the Sufficiency of a classical 'twoways' effects model: ##
## Hausmantype Test of Bai (2009) ##
#####################################################################
## Model under the null Hypothesis:
twoways.obj < Eup(d.l.Consumption ~ 1 + d.l.Price + d.l.Income,
factor.dim = 0, additive.effects = "twoways")
## Model under the alternative Hypothesis:
not.twoways.obj < Eup(d.l.Consumption ~ 1 + d.l.Price + d.l.Income,
factor.dim = 2, additive.effects = "none")
###########
## Test: ##
###########
## (This test returns an error message, since the (unobserved) true
## factor dimension is probably greater than 2.)
## Not run:
checkSpecif(obj1 = twoways.obj, obj2 = not.twoways.obj, level = 0.01)
## End(Not run)
#####################################################################
## Testing the Existence of additional (unobserved) common Factors ##
## Specification Test of Kneip, Sickles, and Song (2012) ##
#####################################################################
## For the model of Bai (2009):
Eup.obj < Eup(d.l.Consumption ~ 1 + d.l.Price + d.l.Income,
additive.effects = "twoways")
## Test:
checkSpecif(Eup.obj, level = 0.01)
## For the model of Kneip, Sickles, and Song (2012):
KSS.obj < KSS(l.Consumption ~ 1 + l.Price + l.Income,
additive.effects = "twoways")
## Test:
checkSpecif(KSS.obj, level = 0.01)
