MATLAB: Panel Data Regression

multivariate regressionpanel regressionregression

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 (94×5) and 6 independent variables (94×5). How can I run an ols regression?

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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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MATLAB: Estimate confidence intervals after regress!

The ‘BINT’ matrix contains the parameter confidence intervals.

If you want both parameter and prediction confidence intervals, use fitlm:

x = (1:10)';                                        % Create Data

y = 1.5 + 2.3*x + 1.5*randn(size(x));               % Create Data
mdl = fitlm(x, y);                                  % Fit Data
B = mdl.Coefficients.Estimate;                      % Coefficients
CI = coefCI(mdl);                                   % Coefficient Confidence Intervals
[Ypred,YCI] = predict(mdl, x);                      % Fitted Regression Line & Confidence Intervals
figure(1)
plot(x, y, 'pg')
hold on
plot(x, Ypred,'-r',    x, YCI, '--r')
hold off
grid

MATLAB: 95% confidence intervalsof regression line!

I like regress, even with its limitations. However, it would probably be best for you to re-run your regression with the fitlm (link) function, then use the predict (link) function to calculate the confidence intervals. The ‘Xnew’ in the documentation are your existing independent variable values.

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Related Solutions

MATLAB: Estimate confidence intervals after regress!

MATLAB: 95% confidence intervalsof regression line!

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