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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26172
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| Title: | Interpreting Estimated Parameters and Measuring Individual Heterogeneity
in Random Coefficient Models |
| Authors: | Greene, William |
| Keywords: | Panel data
random effects
random parameters
maximum simulated likelihood
posterior mean
posterior variance
marginal effects
confidence interval |
| Issue Date: | Sep-2003 |
| Series/Report no.: | EC-03-19 |
| Abstract: | Recent studies in econometrics and statistics include many applications
of random parameter models. There is some ambiguity in how estimation
results in these models are interpreted. The underlying structural
parameters are often not informative about the statistical relationship
of interest. As a result, standard significance tests of structural
parameters in random parameter models do not necessarily indicate the
presence or absence of a ‘significant’ relationship among
the model variables. This note offers some suggestions on how to
interpret and use the results of estimation of a general form of random
parameter model and how simulation based estimates of parameters in
conditional distributions can be used to examine the influence of model covariates. |
| URI: | http://hdl.handle.net/2451/26172 |
| Appears in Collections: | Economics Working Papers
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