1 / 19

Empirical Methods for Microeconomic Applications

Empirical Methods for Microeconomic Applications. William Greene Department of Economics Stern School of Business. Upload Your Project File. Commands for Random Parameters. Random Parameter Specifications.

Download Presentation

Empirical Methods for Microeconomic Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Empirical Methods for Microeconomic Applications William Greene Department of Economics Stern School of Business

  2. Upload Your Project File

  3. Commands for Random Parameters

  4. Random Parameter Specifications All models in LIMDEP/NLOGIT may be fit with random parameters, with panel or cross sections. NLOGIT has more options (not shown here) than the more general cases. Options for specifications ; FCN = name ( type ), name ( type ), … Type is N = normal, U = uniform, L = lognormal (positive), T = tent shaped distributions. C = nonrandom (variance = 0 – only in NLOGIT) Name is the name of a variable or parameter in the model orA_choice for ASCs (up to 8 characters). In the CLOGIT model, they are A_AIR A_TRAIN A_BUS. ; Correlated parameters (otherwise, independent)

  5. Replicability Consecutive runs of the identical model give different results. Why? Different random draws. Achieve replicability (1) Use ;HALTON (2) Set random number generator before each run with the same value. CALC ; Ran( large odd number) $ (Setting the seed is not needed for ;Halton)

  6. Random Parameters Models SETPANEL ; Group = id ; Pds = ti $ PROBIT ; Lhs = doctor ; Rhs = One,age,educ,income,female ; RPM ; Pts = 25 ; Halton ; Panel ; Fcn = one(N),educ(N) ; Correlated $ POISSON ; Lhs = Doctor ; Rhs = One,Educ,Age,Income,Hhkids ; Fcn = educ(N) ; Panel ; Pts=100 ; Halton ; Maxit = 25 $ And so on…

  7. Saving Individual Expected Values SETPANEL ; Group = id ; Pds = ti $ PROBIT ; Lhs = doctor ; Rhs = One,age,educ,income,female ; RPM ; Pts = 25 ; Halton ; Panel ; Fcn = one(N),educ(N) ; Correlated ; Parameters $

  8. Commands for Latent Class Models

More Related