Classical Assumption Test Data Analyze Method
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time by including dummy variables. In general, the approach Fixed Effect Least Square Dummy Variable can be written as follows :
In the equation, there is the addition of as many variables N-1 and T-1 as a dummy variable in the model as well as eliminating the two other variables to avoid
perfect collinearity among explanatory variables. This led to a degree of freedom of NT - 2 - N-1 - T-1 or a NT - N - T which affect the efficiency of the parameters to
be estimated.
3. Random Effect Model REM Random Effect models assume that the sample is randomly in each period, so
it is assumed u
i
and v
t
in case consideration of heterogeneity intertemporal follow a normal distribution. This model provides benefits in terms of savings compared to the
number of variables Fix Effect, so as to improve the efficiency of the model. In Random Effect models, parameters that vary from time put into the component error
error component model. The form of a random effects model with two independent variables is
Y
it
= β
1
+ β
2
X
2it
+ w
it
w
it
= u
i
+ v
t
+ ɛ
it
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Where, u
i
is the error component of the cross-section, v
t
is the error component time series, and εit is the error component of the combination. By using
the model of random effect, the use of degrees of freedom can be saved and not reduce the amount as in the fix effect models. The result parameter estimation result
will be more efficient.
1 Selection Test Model In Panel Data
In choosing a panel data model that will be used, first Chow test to determine whether the use of panel data processing Pooled Least Square method or Fixed
Effect. If significant then proceed with the Hausman test to choose between Fixed Effects and Random Effect. If significant Hausman test results it was concluded the
processing performed by the FEM method. Fix Effect
Random Effect
Pooled Least Square
Hausman Test
LM Test Chow Test
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1. Chow test Called the F test statistic for selecting the panel data model PLS or FEM. The
hypothesis is formed is: H
: PLS model H
1
: FE model Basic rejection of the null hypothesis is by using the F statistic.
Chow formulated: Chow =
–
where: RRSS: Restricted Residual Sum Square value Residual Sum Square with PLS
method USSR: Unrestricted Residual Sum Square Sum Square Residual value method FE
N: Number of cross-section data Q: The number of time series data
K: The number of explanatory variables This test follows the distribution of the F statistic is F N-1, NT-NK. If the
value of the F statistic Chow greater than F table, it is enough evidence to reject the null hypothesis and FE methods are used.
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2. Hausman test Hausman test is a statistical test that became the basis of the considerations in
choosing a model FE or RE. The test is performed with the following hypothesis: a. Ho: RE model
b. H1: FE model Basic rejection of the null hypothesis is by using chi-square statistical considerations.
Hausman test can be done in programming eviews as follows: if the result of the significant Hausman test Hausman probability α, the null hypothesis is rejected
and the FE method is used.