Panel Data Regression

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Alessandra
Alessandra 2011-7-27
回答： Shashank Prasanna 2014-6-24
I have to run a regression with a panel data. I have a sample of 94 elements and a time horizon of 5 years,a dependent variable (94x5) and 6 independent variables (94x5). How can I run an ols regression?

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Shashank Prasanna
Shashank Prasanna 2014-6-24
http://www.mathworks.com/videos/multilevel-mixed-effects-modeling-using-matlab-91755.html
Various panel regression models are covered in the above webinar. While fixed effects can be estimated using ols (fitlm function) random effects can be estimated using mle using the fitlme function
http://www.mathworks.com/help/stats/fitlme.html

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Muhammad Anees
Muhammad Anees 2012-6-12
Hello: Late but a new member of Mathworks:
The following codes will work for you.
%%Classical estimation of the fixed effects panel data model
function[coeff,COVb]=panFE(Y,X,T)
% Y and X stacked by cross-section; T is the time dimension
% Estimator for panel data with fixed effects (balanced panel)
% coeff contains the estimator of the slope (slope) and the fixed effects (fe)
% COVb contains the estimated covariance matrix of the slope estimator
[NT,m] = size(Y);
[S,K]=size(X);
N=NT/T;
%within estimator
%build the matrix D
D=zeros(NT,N);
c=1;
for i=1:N,
D(c:T*i,i)=ones(T,1);
c=T*i+1;
end;
M=eye(NT)-D*inv(D'*D)*D';
b=inv(X'*M*X)*X'*M*Y;
a=inv(D'*D)*D'*(Y-X*b);
coeff.slope=b;
coeff.fe=a;
%compute the covariance matrix for the estimated coefficients
Xm=M*X;
Ym=M*Y;
res=Ym-Xm*b;
varres=(1/(NT-N-K))*res'*res;
COVb=varres*inv(X'*M*X);
  2 个评论
Greg Heath
Greg Heath 2012-6-12
1. What is the definition of "panel" data?
2. Why are you using INV instead of SLASH and BACKSLASH?
Hope this helps.
Greg
Tinashe Bvirindi
Tinashe Bvirindi 2014-5-23
编辑：Tinashe Bvirindi 2014-5-23
a panel is a collection of observations across entities and across time. it has both cross sectional and time series dimensions. the reason why the backslash operator is used is that it improves the efficiency of the code and reduces the degree of error where you require a repetitive estimation of the inverse... I hope this helps

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